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Tomato Leaf Disease Detection using Convolution Neural Network
Tomato grains are an essential significant, abundant product in the Indian market with high commercial value. Diseases are detrimental to the plant's health, affecting its growth. It is crucial to monitor the condition of the crop for a sustainable farming system. There are many types of tomato diseases that affect the leaves of the crop at an alarming rate. This paper slightly modifies the evolutionary neural network model called lnceptionV3 to detect and classify disease on tomato leaves. The main goal of the proposed work is to find solutions to the problem of tomato leaf disease detection using simple methods while using minimal computing resources to achieve results comparable to the latest technology. Neural network models employ automated feature extraction to classify the input image into the corresponding disease class. This proposed system has achieved an average accuracy of 94-95%, which indicates the feasibility of the neural network approach even in adverse conditions.
leaf disease detection, neural network, convolution, inceptionV3
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