Rice Plant Disease Detection Using Machine Learning

Computer Science And Engineering

Plant diseases have a very serious affect on the farming industry. As a result of this, there is a bad impact on the productivity of the crops. This leads to huge losses to farmers. To ensure better quality, quantity and productivity of the yield, it is very crucial for identifying the diseases at early stage for reducing the use of pesticides to reduce damage of the crops and environment. In this research our aim was to detect and classify the rice plant leaf diseases, divided into four categories of classes as healthy, hispa, brown spot and leaf blast. We have used convolutionl neural network for the feature extraction from the rice images. Along with i, some machine learning classifiers such as K-Nearest Neighbors and Random Forest were used for the classification of the diseases based on the categories. The CNN Model performed well for the feature extraction with the accuracy of 80 percent. Along with this second model was classification of diseases using some machine learning classifiers such as Random Forest and K- Nearest Neighbors, accomplished the accuracy of 96% and 72% respectively.

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