ACCURATE AND TIMELY PREDICTION OF RICE CROP DISEASE BY MEANS OF MACHINE LEARNING ALGORITHMS

Abstract

In Worldwide, India stood second in agriculture production. Agriculture plays a key role in Indian economy. These days, the main objective of agriculture not only lies in enhancing the cultivation but also to satisfy the end users with high quality goods. Rice pests and diseases are stipulating more importance day by day with the global changes. It leads concern on both global food safeties along with security of a major food crop worldwide. The small changes in availability may have heavy impact on prices as the rice global market is very low. Technical innovations need to be explored to meet the quality standards and ever growing rice demands. To achieve this, digital agricultural domain is focusing on enabling distinct applications by combining emerging technologies with the traditional techniques. The Proposed system mainly focuses on predicting the diseases in rice crop for pesticide management by utilizing machine learning algorithms such as J48, Naïve-Bayes and SVM. This study also presents the comparative results of these algorithms for disease prediction in terms of classification, prediction accuracy, time complexity and space complexity.