# A Comprehensive Literature Review on Sweet Orange Leaf Diseases

arXiv: https://arxiv.org/abs/2312.01756 · HTML: https://mlsanity.com/paper/2312.01756/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [A comprehensive review on Plant Leaf Disease detection using Deep learning](https://mlsanity.com/paper/2308.14087/) (2023)
- [Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review](https://mlsanity.com/paper/2311.03240/) (2023)
- [Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review](https://mlsanity.com/paper/2311.15741/) (2023)

## Built on by (most cited first, 1 of 1)

- [R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration](https://mlsanity.com/paper/2509.16251/) (2025)

## Abstract (arXiv)

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