plants_disease_Detection_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>plant disease detector</center><h1>\n",
    "<img src=\"https://images.unsplash.com/photo-1564543236611-216a24136f51?ixlib=rb-1.2.1&ixid=eyJhcHBfaWQiOjEyMDd9&auto=format&fit=crop&w=750&q=80\" style=\"width : 750px ; height : 410px \" >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "colab_type": "code",
    "id": "nW-WJzky9v22",
    "outputId": "21aeb9ed-429b-42d2-c258-2cfa5cf7eabd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-10-09 10:59:19--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
      "Resolving bin.equinox.io (bin.equinox.io)... 52.200.233.201, 52.207.111.186, 3.213.5.196, ...\n",
      "Connecting to bin.equinox.io (bin.equinox.io)|52.200.233.201|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 13773305 (13M) [application/octet-stream]\n",
      "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
      "\n",
      "ngrok-stable-linux- 100%[===================>]  13.13M  37.6MB/s    in 0.3s    \n",
      "\n",
      "2019-10-09 10:59:20 (37.6 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [13773305/13773305]\n",
      "\n",
      "Archive:  ngrok-stable-linux-amd64.zip\n",
      "  inflating: ngrok                   \n"
     ]
    }
   ],
   "source": [
    "!pip install -U -q PyDrive\n",
    "!wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
    "!unzip ngrok-stable-linux-amd64.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m9C_Pg5Xuhlr"
   },
   "outputs": [],
   "source": [
    "\n",
    "#from pydrive.auth import GoogleAuth\n",
    "#from pydrive.drive import GoogleDrive\n",
    "#from google.colab import auth\n",
    "#from oauth2client.client import GoogleCredentials\n",
    "#import zipfile\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import matplotlib.pyplot  as plt  \n",
    "from tensorflow.keras.layers import Conv2D,Dense,Flatten,Input\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras import metrics\n",
    "\n",
    "from PIL import Image\n",
    "#import os\n",
    "import numpy as np\n",
    "import json\n",
    "import sys\n",
    "import getopt\n",
    "from time import time\n",
    "\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "#from google.colab import drive\n",
    "#from google.colab import files\n",
    "from PIL import Image\n",
    "import utils\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "PATH='/content/gdrive'\n",
    "LOG_DIR = 'log/'\n",
    "with open('idx_to_class.json', 'r') as fp:\n",
    "    data = json.load(fp)\n",
    "    idx_to_class= {int(k):class_name  for k,class_name in data.items() }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "wxqU-vgFvpGN",
    "outputId": "55588acc-be27-49d3-9444-c452d5a56bd8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"<string>\", line 1, in <module>\n",
      "  File \"/usr/lib/python3.6/json/__init__.py\", line 299, in load\n",
      "    parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n",
      "  File \"/usr/lib/python3.6/json/__init__.py\", line 354, in loads\n",
      "    return _default_decoder.decode(s)\n",
      "  File \"/usr/lib/python3.6/json/decoder.py\", line 339, in decode\n",
      "    obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n",
      "  File \"/usr/lib/python3.6/json/decoder.py\", line 357, in raw_decode\n",
      "    raise JSONDecodeError(\"Expecting value\", s, err.value) from None\n",
      "json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)\n"
     ]
    }
   ],
   "source": [
    "get_ipython().system_raw(\n",
    "    'tensorboard --logdir={} --host 0.0.0.0 --port 6006 &'\n",
    "    .format(LOG_DIR)\n",
    ")\n",
    "\n",
    "get_ipython().system_raw('./ngrok http 6006 &')\n",
    "! curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
    "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AZGc5RzSwW9T"
   },
   "outputs": [],
   "source": [
    "def build_model(num_classes,input_shape=(224,224,3)):\n",
    "  \"\"\"\n",
    "  Args:\n",
    "            num_classes (int): number of classes\n",
    "            intut_shape (tuple): shape of image as an input , default value if not specified : (244,224,3)\n",
    "  \"\"\"\n",
    "  \n",
    "  input_tensor = Input(shape=input_shape)\n",
    "  base_model = VGG16(include_top=False,weights=\"imagenet\",input_tensor=input_tensor)\n",
    "  x = base_model.output\n",
    "  x = Flatten()(x)\n",
    "  # let's add 2 fully-connected layers with 1024 units\n",
    "  x = Dense(1024, activation='relu')(x)\n",
    "  x = Dense(1024, activation='relu')(x)\n",
    "  # and a logistic layer -- let's say we have 200 classes\n",
    "  predictions = Dense(num_classes, activation='softmax')(x)\n",
    "  # this is the model we will train\n",
    "  model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "  # i.e. freeze all convolutional InceptionV3 layers\n",
    "  for layer in base_model.layers:\n",
    "      layer.trainable = False\n",
    "  return model    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(best_checkpoint_path=\"/content/gdrive/My Drive/checkpoints/epochs:009-val_acc:0.953.hdf5\"):    \n",
    "    model = build_model(len(idx_to_class)) \n",
    "    model.compile(optimizer='Adam',loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    model.load_weights(best_checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "lAqTNOD8KJoh",
    "outputId": "121a9fc6-cdc5-4c16-9aa0-65974eb547ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/gdrive\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pydrive.drive.GoogleDrive at 0x7f987c0b6080>"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "utils.my_auth()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# create dataset generators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "bm2lvqAY430u",
    "outputId": "ef7a9344-8c99-4cb6-fbbe-4510ef795f80"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/gdrive\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-34b44876-0ee9-4b9c-a901-8e6606274eea\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-34b44876-0ee9-4b9c-a901-8e6606274eea\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving kaggle.json to kaggle.json\n",
      "kaggle.json\n",
      "Downloading plant-diseases.zip to /content\n",
      "100% 3.20G/3.20G [00:52<00:00, 84.6MB/s]\n",
      "100% 3.20G/3.20G [00:52<00:00, 65.0MB/s]\n",
      "finished !\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:336: UserWarning: This ImageDataGenerator specifies `zca_whitening`, which overrides setting of `featurewise_center`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ugly files deleted from test\n",
      "Found 130356 images belonging to 38 classes.\n",
      "Found 43452 images belonging to 38 classes.\n",
      "Found 41614 images belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "#upload the dataset \n",
    "utils.upload_data()\n",
    "\n",
    "init_setting= dict(rescale=1./255,\n",
    "             #  preprocessing_function=preprocess_input,\n",
    "               zca_whitening=True,\n",
    "               horizontal_flip=True,\n",
    "               vertical_flip=True,\n",
    "               validation_split=0.25)\n",
    "setting=dict(target_size=(224,224),\n",
    "             color_mode='rgb',\n",
    "             batch_size=32,\n",
    "             class_mode='categorical',\n",
    "             shuffle=True)\n",
    "\n",
    "\n",
    "train_datagen=ImageDataGenerator(**init_setting)\n",
    "test_datagen=ImageDataGenerator(rescale=1./255)\n",
    "delete_ugly()\n",
    "train_generator=train_datagen.flow_from_directory('./dataset_itr2/train', \n",
    "                                                   subset='training',**setting)\n",
    "valid_generator=train_datagen.flow_from_directory('./dataset_itr2/train', \n",
    "                                                  subset='validation',**setting)\n",
    "\n",
    "#since it's a test set , we need to preserve the images order and omit shuffeling \n",
    "setting[\"shuffle\"]=False\n",
    "\n",
    "test_generator=test_datagen.flow_from_directory(directory='./dataset_itr2/test',\n",
    "                                                  **setting)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 369
    },
    "colab_type": "code",
    "id": "XOZC36FO49p8",
    "outputId": "c9d03dc0-5461-47c9-e062-71e43d326b16"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "data": {
      "image/png": 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YzJPNpcgXMhRKZYxEkp7r0e3auF4AQkURGmYyhW4mUHUDVdcJUOjaDtV6k2bX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oFAQP7cpaUlOa6IjqHRaJBIJOjp6ZFFSkjBBagXDAal+YjFYsHjMUhb2WxWGpDYXUZ4TK1W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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(images,labels)=train_generator.next()\n",
    "plt.imshow(images[0,:])\n",
    "plt.title(idx_to_class[np.argmax(labels[0,:]) ])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ugHKVr2J6kZL"
   },
   "source": [
    "# train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "OUc1GG7HKJ7q",
    "outputId": "58f291c4-85b4-4077-b47a-875d54ab9397"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 2s 0us/step\n",
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              25691136  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 38)                38950     \n",
      "=================================================================\n",
      "Total params: 41,494,374\n",
      "Trainable params: 26,779,686\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model=build_model(len(idx_to_class))  \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "vehxBiCd7ZFd",
    "outputId": "521aa675-8ffb-4db5-8d0e-0b7386bd1cc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 1s 0us/step\n",
      "http://613869b9.ngrok.io\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n",
      "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
      "  warnings.warn('This ImageDataGenerator specifies '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.4601 - acc: 0.8548Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:29 - loss: 0.3300 - acc: 0.8925\n",
      "Epoch 00001: val_loss improved from inf to 0.32997, saving model to /content/gdrive/My Drive/checkpoints/epochs:001-val_acc:0.893.hdf5\n",
      "4073/4073 [==============================] - 2041s 501ms/step - loss: 0.4600 - acc: 0.8548 - val_loss: 0.3300 - val_acc: 0.8925\n",
      "Epoch 2/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.2396 - acc: 0.9206Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:27 - loss: 0.2919 - acc: 0.9082\n",
      "Epoch 00002: val_loss improved from 0.32997 to 0.29193, saving model to /content/gdrive/My Drive/checkpoints/epochs:002-val_acc:0.908.hdf5\n",
      "4073/4073 [==============================] - 2030s 498ms/step - loss: 0.2396 - acc: 0.9206 - val_loss: 0.2919 - val_acc: 0.9082\n",
      "Epoch 3/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1873 - acc: 0.9376Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:29 - loss: 0.2424 - acc: 0.9216\n",
      "Epoch 00003: val_loss improved from 0.29193 to 0.24243, saving model to /content/gdrive/My Drive/checkpoints/epochs:003-val_acc:0.922.hdf5\n",
      "4073/4073 [==============================] - 2028s 498ms/step - loss: 0.1873 - acc: 0.9376 - val_loss: 0.2424 - val_acc: 0.9216\n",
      "Epoch 4/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1659 - acc: 0.9459Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:27 - loss: 0.2896 - acc: 0.9162\n",
      "Epoch 00004: val_loss did not improve from 0.24243\n",
      "4073/4073 [==============================] - 2024s 497ms/step - loss: 0.1660 - acc: 0.9458 - val_loss: 0.2896 - val_acc: 0.9162\n",
      "Epoch 5/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1482 - acc: 0.9523Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:26 - loss: 0.2180 - acc: 0.9343\n",
      "Epoch 00005: val_loss improved from 0.24243 to 0.21798, saving model to /content/gdrive/My Drive/checkpoints/epochs:005-val_acc:0.934.hdf5\n",
      "4073/4073 [==============================] - 2025s 497ms/step - loss: 0.1482 - acc: 0.9523 - val_loss: 0.2180 - val_acc: 0.9343\n",
      "Epoch 6/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1348 - acc: 0.9574Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:24 - loss: 0.1941 - acc: 0.9456\n",
      "Epoch 00006: val_loss improved from 0.21798 to 0.19415, saving model to /content/gdrive/My Drive/checkpoints/epochs:006-val_acc:0.946.hdf5\n",
      "4073/4073 [==============================] - 2023s 497ms/step - loss: 0.1348 - acc: 0.9574 - val_loss: 0.1941 - val_acc: 0.9456\n",
      "Epoch 7/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1264 - acc: 0.9600Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:31 - loss: 0.2454 - acc: 0.9346\n",
      "Epoch 00007: val_loss did not improve from 0.19415\n",
      "4073/4073 [==============================] - 2030s 498ms/step - loss: 0.1264 - acc: 0.9600 - val_loss: 0.2454 - val_acc: 0.9346\n",
      "Epoch 8/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1169 - acc: 0.9635Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:29 - loss: 0.2755 - acc: 0.9349\n",
      "Epoch 00008: val_loss did not improve from 0.19415\n",
      "4073/4073 [==============================] - 2027s 498ms/step - loss: 0.1170 - acc: 0.9635 - val_loss: 0.2755 - val_acc: 0.9349\n",
      "Epoch 9/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1106 - acc: 0.9660Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:30 - loss: 0.1827 - acc: 0.9525\n",
      "Epoch 00009: val_loss improved from 0.19415 to 0.18272, saving model to /content/gdrive/My Drive/checkpoints/epochs:009-val_acc:0.953.hdf5\n",
      "4073/4073 [==============================] - 2031s 499ms/step - loss: 0.1106 - acc: 0.9660 - val_loss: 0.1827 - val_acc: 0.9525\n",
      "Epoch 10/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1046 - acc: 0.9685Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:28 - loss: 0.2490 - acc: 0.9397\n",
      "Epoch 00010: val_loss did not improve from 0.18272\n",
      "4073/4073 [==============================] - 2027s 498ms/step - loss: 0.1046 - acc: 0.9685 - val_loss: 0.2490 - val_acc: 0.9397\n",
      "Epoch 11/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.1016 - acc: 0.9700Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:25 - loss: 0.2223 - acc: 0.9464\n",
      "Epoch 00011: val_loss did not improve from 0.18272\n",
      "4073/4073 [==============================] - 2021s 496ms/step - loss: 0.1016 - acc: 0.9700 - val_loss: 0.2223 - val_acc: 0.9464\n",
      "Epoch 12/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.0963 - acc: 0.9712Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:27 - loss: 0.2032 - acc: 0.9523\n",
      "Epoch 00012: val_loss did not improve from 0.18272\n",
      "4073/4073 [==============================] - 2022s 496ms/step - loss: 0.0963 - acc: 0.9712 - val_loss: 0.2032 - val_acc: 0.9523\n",
      "Epoch 13/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.0923 - acc: 0.9731Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:27 - loss: 0.2762 - acc: 0.9375\n",
      "Epoch 00013: val_loss did not improve from 0.18272\n",
      "4073/4073 [==============================] - 2021s 496ms/step - loss: 0.0923 - acc: 0.9731 - val_loss: 0.2762 - val_acc: 0.9375\n",
      "Epoch 14/20\n",
      "4072/4073 [============================>.] - ETA: 0s - loss: 0.0877 - acc: 0.9749Epoch 1/20\n",
      "1357/4073 [========>.....................] - ETA: 16:26 - loss: 0.2771 - acc: 0.9418\n",
      "Epoch 00014: val_loss did not improve from 0.18272\n",
      "4073/4073 [==============================] - 2022s 496ms/step - loss: 0.0877 - acc: 0.9749 - val_loss: 0.2771 - val_acc: 0.9418\n",
      "Epoch 00014: early stopping\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#---------------------compile model---------------------------------------------------\n",
    "model.compile(optimizer=\"Adam\",loss='categorical_crossentropy', metrics=[\"accuracy\"])\n",
    "steps_per_epochs=train_generator.n//train_generator.batch_size\n",
    "valid_steps=valid_generator.n//valid_generator.batch_size\n",
    "\n",
    "\n",
    "#---------------------prepare callbacks----------------------------------------\n",
    "\n",
    "filepath=\"/content/gdrive/My Drive/checkpoints/epochs:{epoch:03d}-val_acc:{val_acc:.3f}.hdf5\"    \n",
    "\n",
    "tbCallBack = TensorBoard(log_dir='log/'.format(time()), histogram_freq=0,\n",
    "                         write_graph=True,\n",
    "                         write_grads=True,\n",
    "                         batch_size=train_generator.batch_size,\n",
    "                         write_images=True)\n",
    "                        \n",
    "es_callback=EarlyStopping(patience=5,verbose=1,min_delta=1e-3)\n",
    "rop_callback=ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=2, min_lr=0.01,verbose=1)\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto')\n",
    "\n",
    "#---------------------train model -----------------------------------------------\n",
    "history=model.fit_generator(generator=train_generator,\n",
    "                    steps_per_epoch=steps_per_epochs,\n",
    "                    validation_data=valid_generator,\n",
    "                    validation_steps=valid_steps,\n",
    "                    epochs=20,\n",
    "                    verbose=1,\n",
    "                    callbacks=[tbCallBack,checkpoint,es_callback,rop_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.model accuracy & loss plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "colab_type": "code",
    "id": "drMsrC4D9N00",
    "outputId": "4bd03de2-b04a-4cb4-c3f2-7c644c337221"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2OoJh6UmcMSLdNSH1ds1IKQldghu46TQ6T7IIAe+++y7vvvsuY8aMAaCiooL169czYcIE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\naIeUEd6CjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tyL3W9WHNvBaeGwcFX/kdWeiouou2uGQY/Su/owm5UCaLTUBgve1u3rZDiMho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\nJUDevsstFGTNT6YBLFm0Ysd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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.classification report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "wDGfFcOH_B4f",
    "outputId": "dfb51484-e3e1-4ced-c52f-e12f23abde61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "58892288/58889256 [==============================] - 2s 0us/step\n"
     ]
    }
   ],
   "source": [
    "#let's first load the best checkpoint\n",
    "load_model(\"/content/gdrive/My Drive/checkpoints/epochs:009-val_acc:0.953.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "hYOJnUlCQlxW",
    "outputId": "ea35382a-4afd-464e-9964-5befb0848ce0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test accuracy : 95.12 %\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apple___Apple_scab</th>\n",
       "      <th>Apple___Black_rot</th>\n",
       "      <th>Apple___Cedar_apple_rust</th>\n",
       "      <th>Apple___healthy</th>\n",
       "      <th>Blueberry___healthy</th>\n",
       "      <th>Cherry_(including_sour)___Powdery_mildew</th>\n",
       "      <th>Cherry_(including_sour)___healthy</th>\n",
       "      <th>Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot</th>\n",
       "      <th>Corn_(maize)___Common_rust_</th>\n",
       "      <th>Corn_(maize)___Northern_Leaf_Blight</th>\n",
       "      <th>Corn_(maize)___healthy</th>\n",
       "      <th>Grape___Black_rot</th>\n",
       "      <th>Grape___Esca_(Black_Measles)</th>\n",
       "      <th>Grape___Leaf_blight_(Isariopsis_Leaf_Spot)</th>\n",
       "      <th>Grape___healthy</th>\n",
       "      <th>Orange___Haunglongbing_(Citrus_greening)</th>\n",
       "      <th>Peach___Bacterial_spot</th>\n",
       "      <th>Peach___healthy</th>\n",
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       "      <th>Pepper,_bell___healthy</th>\n",
       "      <th>Potato___Early_blight</th>\n",
       "      <th>Potato___Late_blight</th>\n",
       "      <th>Potato___healthy</th>\n",
       "      <th>Raspberry___healthy</th>\n",
       "      <th>Soybean___healthy</th>\n",
       "      <th>Squash___Powdery_mildew</th>\n",
       "      <th>Strawberry___Leaf_scorch</th>\n",
       "      <th>Strawberry___healthy</th>\n",
       "      <th>Tomato___Bacterial_spot</th>\n",
       "      <th>Tomato___Early_blight</th>\n",
       "      <th>Tomato___Late_blight</th>\n",
       "      <th>Tomato___Leaf_Mold</th>\n",
       "      <th>Tomato___Septoria_leaf_spot</th>\n",
       "      <th>Tomato___Spider_mites Two-spotted_spider_mite</th>\n",
       "      <th>Tomato___Target_Spot</th>\n",
       "      <th>Tomato___Tomato_Yellow_Leaf_Curl_Virus</th>\n",
       "      <th>Tomato___Tomato_mosaic_virus</th>\n",
       "      <th>Tomato___healthy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apple___Apple_scab</th>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple___Black_rot</th>\n",
       "      <td>10</td>\n",
       "      <td>476</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple___Cedar_apple_rust</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple___healthy</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1244</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Blueberry___healthy</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1196</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherry_(including_sour)___Powdery_mildew</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>833</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cherry_(including_sour)___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>658</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>324</td>\n",
       "      <td>1</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corn_(maize)___Common_rust_</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>950</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corn_(maize)___Northern_Leaf_Blight</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corn_(maize)___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>918</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grape___Black_rot</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>845</td>\n",
       "      <td>86</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grape___Esca_(Black_Measles)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1070</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grape___Leaf_blight_(Isariopsis_Leaf_Spot)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grape___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>329</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orange___Haunglongbing_(Citrus_greening)</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2613</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peach___Bacterial_spot</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1787</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Peach___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>268</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pepper,_bell___Bacterial_spot</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>714</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pepper,_bell___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1162</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato___Early_blight</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>762</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato___Late_blight</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>721</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Potato___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Raspberry___healthy</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>291</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soybean___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4056</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squash___Powdery_mildew</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1466</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strawberry___Leaf_scorch</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>877</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strawberry___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Bacterial_spot</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1540</td>\n",
       "      <td>19</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Early_blight</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>626</td>\n",
       "      <td>53</td>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>17</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Late_blight</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "      <td>1362</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Leaf_Mold</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>659</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Septoria_leaf_spot</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>43</td>\n",
       "      <td>27</td>\n",
       "      <td>1281</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Spider_mites Two-spotted_spider_mite</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>1118</td>\n",
       "      <td>154</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Target_Spot</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>37</td>\n",
       "      <td>27</td>\n",
       "      <td>1006</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Tomato_Yellow_Leaf_Curl_Virus</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>4195</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___Tomato_mosaic_virus</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tomato___healthy</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Apple___Apple_scab  ...  Tomato___healthy\n",
       "Apple___Apple_scab                                                 456  ...                 0\n",
       "Apple___Black_rot                                                   10  ...                 0\n",
       "Apple___Cedar_apple_rust                                             4  ...                 0\n",
       "Apple___healthy                                                     18  ...                 0\n",
       "Blueberry___healthy                                                  1  ...                 0\n",
       "Cherry_(including_sour)___Powdery_mildew                             1  ...                 0\n",
       "Cherry_(including_sour)___healthy                                    0  ...                 0\n",
       "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot                   0  ...                 0\n",
       "Corn_(maize)___Common_rust_                                          0  ...                 0\n",
       "Corn_(maize)___Northern_Leaf_Blight                                  0  ...                 0\n",
       "Corn_(maize)___healthy                                               0  ...                 0\n",
       "Grape___Black_rot                                                    0  ...                 0\n",
       "Grape___Esca_(Black_Measles)                                         0  ...                 0\n",
       "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)                           0  ...                 0\n",
       "Grape___healthy                                                      0  ...                 0\n",
       "Orange___Haunglongbing_(Citrus_greening)                             0  ...                 0\n",
       "Peach___Bacterial_spot                                               3  ...                 0\n",
       "Peach___healthy                                                      0  ...                 0\n",
       "Pepper,_bell___Bacterial_spot                                        2  ...                 0\n",
       "Pepper,_bell___healthy                                               0  ...                 0\n",
       "Potato___Early_blight                                                1  ...                 0\n",
       "Potato___Late_blight                                                 1  ...                 0\n",
       "Potato___healthy                                                     0  ...                 0\n",
       "Raspberry___healthy                                                  2  ...                 0\n",
       "Soybean___healthy                                                    0  ...                 2\n",
       "Squash___Powdery_mildew                                              0  ...                 0\n",
       "Strawberry___Leaf_scorch                                             0  ...                 0\n",
       "Strawberry___healthy                                                 0  ...                 0\n",
       "Tomato___Bacterial_spot                                              1  ...                 1\n",
       "Tomato___Early_blight                                                0  ...                 0\n",
       "Tomato___Late_blight                                                 0  ...                 4\n",
       "Tomato___Leaf_Mold                                                   0  ...                 1\n",
       "Tomato___Septoria_leaf_spot                                          0  ...                 1\n",
       "Tomato___Spider_mites Two-spotted_spider_mite                        0  ...                 8\n",
       "Tomato___Target_Spot                                                 0  ...                18\n",
       "Tomato___Tomato_Yellow_Leaf_Curl_Virus                               0  ...                 0\n",
       "Tomato___Tomato_mosaic_virus                                         0  ...                 0\n",
       "Tomato___healthy                                                     0  ...              1242\n",
       "\n",
       "[38 rows x 38 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "steps=test_generator.n//test_generator.batch_size+1\n",
    "y_test=test_generator.classes\n",
    "y_pred=model.predict_generator(test_generator,steps=steps,verbose=1)\n",
    "y_pred=np.argmax(y_pred,axis=1)\n",
    "target_names=list(idx_to_class.values())\n",
    "\n",
    "print(\"test accuracy : {} %\".format(round(accuracy_score(y_test,y_pred),4 )*100) )\n",
    "\n",
    "confusion_mat = pd.DataFrame(confusion_matrix(y_test, y_pred),columns = target_names , index=target_names) \n",
    "confusion_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "76W3XI9HE5U-"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "colab_type": "code",
    "id": "cg_v8lgwVjwm",
    "outputId": "731600db-4dc7-418f-8956-338f05da6e9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    precision    recall  f1-score   support\n",
      "\n",
      "                                Apple___Apple_scab       0.91      0.90      0.91       504\n",
      "                                 Apple___Black_rot       0.99      0.96      0.97       496\n",
      "                          Apple___Cedar_apple_rust       1.00      0.91      0.95       220\n",
      "                                   Apple___healthy       0.97      0.95      0.96      1316\n",
      "                               Blueberry___healthy       0.98      1.00      0.99      1200\n",
      "          Cherry_(including_sour)___Powdery_mildew       0.97      0.99      0.98       840\n",
      "                 Cherry_(including_sour)___healthy       1.00      0.97      0.98       680\n",
      "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot       0.91      0.79      0.85       408\n",
      "                       Corn_(maize)___Common_rust_       1.00      1.00      1.00       952\n",
      "               Corn_(maize)___Northern_Leaf_Blight       0.88      0.96      0.92       788\n",
      "                            Corn_(maize)___healthy       1.00      0.99      0.99       928\n",
      "                                 Grape___Black_rot       0.97      0.90      0.93       944\n",
      "                      Grape___Esca_(Black_Measles)       0.93      0.97      0.95      1104\n",
      "        Grape___Leaf_blight_(Isariopsis_Leaf_Spot)       0.99      0.99      0.99       860\n",
      "                                   Grape___healthy       0.98      0.98      0.98       336\n",
      "          Orange___Haunglongbing_(Citrus_greening)       0.99      1.00      0.99      2622\n",
      "                            Peach___Bacterial_spot       0.98      0.97      0.98      1836\n",
      "                                   Peach___healthy       0.95      0.93      0.94       288\n",
      "                     Pepper,_bell___Bacterial_spot       0.96      0.90      0.93       796\n",
      "                            Pepper,_bell___healthy       0.93      0.98      0.96      1180\n",
      "                             Potato___Early_blight       0.97      0.95      0.96       800\n",
      "                              Potato___Late_blight       0.95      0.90      0.92       800\n",
      "                                  Potato___healthy       0.90      0.73      0.81       120\n",
      "                               Raspberry___healthy       0.99      0.98      0.99       296\n",
      "                                 Soybean___healthy       0.98      1.00      0.99      4072\n",
      "                           Squash___Powdery_mildew       0.99      1.00      0.99      1468\n",
      "                          Strawberry___Leaf_scorch       0.98      0.99      0.99       884\n",
      "                              Strawberry___healthy       1.00      0.99      1.00       364\n",
      "                           Tomato___Bacterial_spot       0.97      0.91      0.94      1700\n",
      "                             Tomato___Early_blight       0.77      0.78      0.78       800\n",
      "                              Tomato___Late_blight       0.84      0.89      0.86      1524\n",
      "                                Tomato___Leaf_Mold       0.90      0.87      0.88       760\n",
      "                       Tomato___Septoria_leaf_spot       0.87      0.90      0.89      1416\n",
      "     Tomato___Spider_mites Two-spotted_spider_mite       0.94      0.83      0.88      1340\n",
      "                              Tomato___Target_Spot       0.79      0.90      0.84      1120\n",
      "            Tomato___Tomato_Yellow_Leaf_Curl_Virus       0.98      0.98      0.98      4284\n",
      "                      Tomato___Tomato_mosaic_virus       0.90      0.95      0.93       296\n",
      "                                  Tomato___healthy       0.97      0.98      0.97      1272\n",
      "\n",
      "                                          accuracy                           0.95     41614\n",
      "                                         macro avg       0.95      0.94      0.94     41614\n",
      "                                      weighted avg       0.95      0.95      0.95     41614\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.plot predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GKKzM6AnAc_U"
   },
   "outputs": [],
   "source": [
    "def prediction(model,img_path,topk):\n",
    "  img = image.load_img(img_path, target_size=(224, 224))\n",
    "  input_image = image.img_to_array(img)\n",
    "  input_image = np.expand_dims(input_image, axis=0)\n",
    "  input_image/=255\n",
    "  preds = model.predict(input_image)\n",
    " \n",
    "  idx=preds.argsort().squeeze(0).tolist()[-topk:][::-1]\n",
    "  top_pred=preds.squeeze(0)[idx]\n",
    "  classes=[idx_to_class[x] for x in idx]\n",
    "  plot_prob(input_image,top_pred,classes,topk)\n",
    "  print('Predicted: {} \\nclass {} '.format(top_pred,classes))\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kgow5bX3n-pf"
   },
   "outputs": [],
   "source": [
    "def plot_prob(img,ps,classes,topk):\n",
    "  fig,(ax1,ax2)=plt.subplots(figsize=(11,20),ncols=2)\n",
    "  ax1.axis('off')\n",
    "  img=(img*255).astype(np.uint8)\n",
    "  ax1.imshow(img.squeeze())\n",
    "  ax1.set_title('leaf')\n",
    "  ax2.barh(np.arange(topk),ps,align='center',alpha=.5)\n",
    "  ax2.set(yticks=range(topk),yticklabels=classes )\n",
    "  ax2.set_aspect(0.1)\n",
    "  ax2.set_xlim(0, 1.1)\n",
    "  plt.tight_layout()\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "RS0xfsltp8U8",
    "outputId": "5f1918d6-9682-4d7c-fd15-24bd368031b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: [9.9935287e-01 6.2550453e-04 1.5370711e-05 6.2295358e-06 2.0956929e-08] \n",
      "class ['Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', 'Corn_(maize)___healthy', 'Corn_(maize)___Common_rust_', 'Strawberry___Leaf_scorch'] \n"
     ]
    },
    {
     "data": {
      "image/png": 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xZLHWYu3Mr959i3WO02nh5fUZL56SC8tpoZTM09MHSmksy8LxeMR5Q2tCboXDMuOddiBq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\nrkTBmoIxha7v6IfJx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Oz6nqv6J4TAwXLJbjsF1dVzrFHNCqIl0mw+x3tNoT48vMF6GCgk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8nqs3Kn/812B0oi+tfPQb2Kr1D33svy32+Cngl32U3zkFbqpqFJEvAj7xqR8Qkd+MUUj++/ve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mVFHIzj1EW0bRM+x2hbkRgDj+cLcRhx4qit0ppsmxaRrnFw181F7Jka14tk032gX8PYOf5q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\nzBR60+7D4/0DL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      "text/plain": [
       "<Figure size 792x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "prediction(model,'./test4.png',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fen7b9KXmIO2"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EVN-K5lwVnPc"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XWoZB54WY5aS"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "plants_disease_Detection_.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "title_cell": "Table of Contents",
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   "toc_position": {},
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  "varInspector": {
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   "kernels_config": {
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     "delete_cmd_postfix": "",
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   "types_to_exclude": [
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