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.

Revitalizing the Potential of Minor Millets: Agrarian Constraints, Possible Solutions and Future Roadmap

In contrary to the recent revival of millets like sorghum & pearl millet globally, one segment of millets is still neglected mostly in spite of their massive nutritional benefits and complementing effect on ecology; these are referred as minor millets collectively. The focal causes of its under recognition include lack of awareness among the consumers about its vast potential utilities and multifaceted constraints acting together to curb down its production pattern. Lack of effective research and development in the area of crop improvement via breeding and biotechnological interventions is holding back its upswing. There is very little comprehensive documentation available on these dynamics of minor millets. This paper studies those critical limiting factors while also prescribing the way out to ensure nutritional security and environmental sustainability.