2023/12/04 by Yousuf Rayhan Emon, Emon, Yousuf Rayhan, Md. Golam Rabbani +5 · 1 citation
Agricultural and Biological Sciences · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Leaf Properties and Growth Measurement #Smart Agriculture and AI
paper · pdf · doi:10.48550/arxiv.2312.01756
openalex publication_date 2023/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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