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A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy. [ABSTRACT FROM AUTHOR]

The changes in weather have beneficial and harmful effects on crop yields. There will be a loss of yield because of the diseases in crops. With the growing population, the fundamental want of food is growing. That is why agriculture gains a prominent position all around the world. It eventually ends up by a massive defeat for the farmers and the financial boom of India. The article's primary goalis to bring together farmers and cutting-edge technologies to minimise 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. Tomato is a mere consumable vegetable in India. 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%. [ABSTRACT FROM AUTHOR]