A Optimum Review for Plant Leaf Disease Classification using Machine Learning

Accurately identifying leaf diseases is important for keeping crops healthy and ensuring farming efficiency. Major decreases in agricultural output and possible risks to food security can arise from diseases. For the preservation of agricultural systems and the mitigation of financial losses, timely identification and control are critical components. Evaluation of leaf diseases is critical for ensuring both food security and sustainable agriculture. The aim of this study is to examine the present and potential future effectiveness of integrating advanced deep learning (DL) models, namely InceptionResNetV2, Xception, and EfficientNetV2-L, with traditional machine learning algorithms, including SVM, KNN, and Random Forest, with the purpose of attaining accurate disease classification. Further research may focus on hybrid approaches that combine the advantages of both methodologies to develop scalable, understandable, and effective systems. This type of research may also highlight the trade-offs between traditional machine learning (ML) and deep learning, particularly in terms of accuracy, computational capacity, and interpret ability. Through the combined application of these methodologies, agricultural practitioners are granted advantageous interventions that safeguard the vitality of their produce and make a positive contribution to the worldwide food supply.