Classification of plant disease using SVM and deep learning

Abstract Plants assume an indispensable role in the endurance of all living beings. False analysis of plant disease causes unreasonable utilization of pesticides which in turns influences the nature of harvest. This research paper focuses on soy bean disease classification using SVM and Deep learning algorithms. The plant attributes of about 36 are collected for 683 instances, and SVM classifier is applied to classify 19 classes of diseases. Deep learning custom net multilayer perceptron is then applied to classify the soybean data set.643 instances are correctly classified and 40 are incorrectly classified and the classification accuracy is 94.1435%. The classification accuracy of Deep learning classifier is obtained to be 88.7262%. The attributes of the layers of the architecture need to be optimized to increase the classification accuracy.

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