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

The article’s primary goal is to bring together farmers and cutting-edge technologies to minimize diseases in plant leaves. To enforce the idea, ‘Tomato’ is selected in which leaf sicknesses are expected and identified by the Artificial Intelligence algorithms, CNN (Convolution Neural Network) with pc technological know-how. In this investigation, seven types of tomato leaf disorders were sensed, including one wholesome elegance. The farmers are able to check the symptoms with the shapes of images of the tomato leaves with those expecting diseases.

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

Read the complete research at www.researchgate.net.

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


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