Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help reduce the adverse effects of diseases and overcome the shortcomings of continuous human monitoring. To identify the recent advancements in the development of plant disease

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... on and classification system based on Machine Learning (ML) and Deep Learning (DL) models [5]. An organized way of analysis of various plant disease classification models has been shown in well-formed tables. In this paper, we have conducted a systematic literature study on the applications of the state-of-the-art ML and DL algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Naïve Bayes (NB), other few popular ML algorithms and AlexNet, GoogLeNet, VGGNet, and other few popular DL algorithms respectively for plant disease categorization. Each stated algorithm is characterized through the corresponding processing methods such as image segmentation, and feature extraction, along with the standardized experimental-setup metrics such as total number of training/testing datasets employed, number of diseases under consideration, type of classifier utilized, and the percentage of classification accuracy.