Enhanced learning

Automatic recognition of tomato leaf disease using rapid enhanced learning with image processing

The main objective of the article is to bring together farmers and advanced technologies to minimize leaf diseases of plants. To apply the idea, ‘Tomato’ is selected in which leaf diseases are expected and identified by artificial intelligence algorithms, CNN (Convolution Neural Network) with computer technological know-how. In this investigation, seven types of tomato leaf disorders were detected, including healthy elegance. Farmers are able to check for symptoms with picture shapes of tomato leaves with those expecting disease.

Its comparison of various classifications and filters / methods with different techniques, such as K-Means classifier, SVM (Support Vector), RBF (Radial Basis Function) Kernel, Optimized MLP (Multilayer perceptron), NN classifier, BPNN (back-propagation neural network) and CNN Classifier. The classification precision of the existing method after experience is RBF – 89%, k-means – 85.3%, SVM – 88.8%, optimized MLP – 91.4%, NN – 97, BPNN – 85.5% , CNN – 94.4%. The proposed architecture can achieve the desired accuracy of 99.4%.

Read the full research at www.researchgate.net.

Vadivel, Thanjai & Suguna, R .. (2021). Automatic recognition of tomato leaf disease using enhanced rapid learning with image processing. Acta Agriculturae Scandinavica, Section B – Soil and Plant Sciences. 1-13. 10.1080 / 09064710.2021.1976266.


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