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