A Comprehensive Literature Review on Sweet Orange Leaf Diseases

TL;DR

This paper reviews machine learning techniques for detecting sweet orange leaf diseases using image classification, comparing various models' performance to enhance early diagnosis and management.

Contribution

It systematically compares different machine learning models applied to leaf disease detection, highlighting their benefits and limitations in citrus agriculture.

Findings

Vision Transformer (ViT) and CNN-based models show high accuracy.

Hybrid models like CNN-SVM improve detection performance.

Model performance varies across datasets and techniques.

Abstract

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