Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques

Rice is one of the most important staple food crops in the world. Most Asian countries are dependent on rice and huge quantities of rice are grown every year. However, there are many categories of diseases (e.g., blast) which affect rice production and can ultimately lead to huge financial loss to rice growers. Yield loss due to rice blast disease about 10 to 30 percent annually and under favourable condition, this disease can destroy the rice plant within 15 to 20 days and cause yield loss up to 100%.Therefore to ensure better quality, quantity and better productivity early disease detection should be done so that the right amount of pesticides can be as administered at right time to curb the infection. Nowadays Machine Learning has been integrated into the agriculture sector. The aim of this review paper is to identify which Machine Learning algorithms work best in rice blast disease detection. The algorithms reviewed here include Naive Bayes, LSTM RNN, Random Forest Classifiers, Support Vector Machines, K Means, Decision Tree and Convolutional Neural Networks. This review paper also covers the future scope of improvement of some Machine Learning algorithms like Naive Bayes and Recurrent Neural Networks.

Impact of Pesticides on Beneficial Insects in Various Agroecosystem: A Review

Beneficial insects play a vital role in natural pest control and pollination in agricultural crops. The use of synthetic pesticides in agricultural areas is harmful to both natural enemies and pollinators. Pesticides impair the survival of a variety of life cycle stages, limit reproductive capability, alter host fitness for parasitising or predation, reduce parasitoids' emergence from sprayed host eggs, and cause direct death. When natural enemies are decreased, pest population dynamics, such as resurgence and secondary pest eruption, may suffer even more devastating repercussions. Pollinator decline decreases agricultural yield. This study intends to investigate the side effects of synthetic and botanical pesticides on beneficial insects in order to provide a foundation for future research into the detrimental effects of synthetic and botanical pesticides on these insects. This information will aid in optimising pesticide use in integrated pest management programmes by implementing more sustainable and environment friendly methods such as the use of correct doses and selective insecticides in agricultural areas.