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Sweet orange leaf diseases are significant to agricultural productivity. Leafdiseases impact fruit quality in the citrus industry. The apparition of machinelearning makes the development of disease finder. Early detection and diagnosisare necessary for leaf management. Sweet orange leaf disease-predictingautomated systems have already been developed using different image-processingtechniques. This comprehensive literature review is systematically based onleaf disease and machine learning methodologies applied to the detection ofdamaged leaves via image classification. The benefits and limitations ofdifferent machine learning models, including Vision Transformer (ViT), NeuralNetwork (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP,EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machinelearning models tested on various datasets and detected the disease. Thiscomprehensive review study related to leaf disease compares the performance ofthe models; those models' accuracy, precision, recall, etc., were used in thesubsisting studies

A Comprehensive Literature Review on Sweet Orange Leaf Diseases