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

Bidisha Chakraborty

Government College of Engineering and Leather Technology, Kolkata, West Bengal, India.

Santanu Banerjee *

Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India.

Sanjoy Samanta

Department of Entomology, Uttar Banga Krishi Vishwavidyalaya, Coochbehar, 736165, West Bengal, India.

Udit Debangshi

Department of Agronomy, Institute of Agriculture, Visva-Bharati, Sriniketan, West Bengal, India.

Sandhya V. Yadav

Fergusson College (Autonomous), Pune (MH), 411005, India.

Pravin B. Khaire

Department of Plant Pathology & Microbiology, Mahatma Phule Krishi Vidyapeeth, Rahuri (MH), 413722, India.

Vaibhav B. Shelar

Biological Nitrogen Fixation Lab, College of Agriculture, Pune (MH), 411005, India.

Govardhan D. Bansode

Fergusson College (Autonomous), Pune (MH), 411005, India.

Kiran B. Landage

Biological Nitrogen Fixation Lab, College of Agriculture, Pune (MH), 411005, India.

*Author to whom correspondence should be addressed.


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.

Keywords: Rice, Magnaporthe grisea, detection, machine learning, algorisms

How to Cite

Chakraborty , B., Banerjee , S., Samanta , S., Debangshi , U., Yadav , S. V., Khaire , P. B., Shelar , V. B., Bansode , G. D., & Landage , K. B. (2023). Detection of Rice Blast Disease (Magnaporthe grisea) Using Different Machine Learning Techniques. International Journal of Environment and Climate Change, 13(8), 2256–2264.


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