Published in: Journal of Computer Science and Engineering in Innovations and Research (ISSN No: 3049-1762 online)
Publication Date: July 15, 2024
Rice blast disease is a significant threat to rice production, causing substantial yield losses and financial burdens for rice growers. Early detection of the disease is crucial to mitigate its impact and ensure better quality and productivity. The integration of Machine Learning (ML) techniques in the agricultural sector has shown promise in disease detection. This review paper aims to identify the most effective ML algorithms for rice blast disease detection. Several algorithms are reviewed, including Naive Bayes, LSTM RNN, Random Forest Classifiers, Support Vector Machines, K Means, Decision Tree, and Convolutional Neural Networks. The paper also discusses the potential future improvements for ML algorithms, specifically Naive Bayes and Recurrent Neural Networks. By evaluating the performance and capabilities of these algorithms, this review provides insights into the optimal ML techniques for early detection of rice blast disease and highlights areas for further advancement in the field.
Rice blast disease, Machine Learning (ML) algorithms, Disease detection, Early detection, Agricultural sector, Yield losses