Blast disease is caused by the Pyricularia oryzae fungus and has been recorded in more than 80 rice-producing countries around the world, and the disease is progressively more complex, causing many difficulties for farmers. From previous studies on rice blast disease, six important factors including rice variety,seed density, temperature, humidity , leaf color (protein) and lesion status have been found to have significant influence on the pathogenesis of diseases . Today, with the rapid development of internet networks, mobile devices, etc., most of the farmers own mobile phones. In this study, the content-based of recommender method is used to build the mobile application “BLASTREC” that supports farmers in blast prevention . The software BLASTREC functions Window operating system based on two Naive Bayes and Decision Tree classification algorithms. Experimental results show that the accuracy of two algorithms is more than 90%. The experiment data on blast in Trung An area, Thot Not district, Can Tho city combines with agricultural expert’s opinion to provide farmers with appropriate treatment .
This research has been upgraded from the post-article : https://sj.ctu.edu.vn/ql/docgia/tacgia-18299/baibao-50447/doi-ctu.jsi.2017.022.html , instead of using the traditional Entropy Function ( Shannon Entropy ) for each class, the recommendation : alpha Entropy ( Renyi Entropy ) , beta Entropy ( Daróczy Entropy ) performed from this article http://www.cit.ctu.edu.vn/~dtnghi/rech/dir_0/Nghi_Khang.pdf .