Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review

TL;DR

This paper reviews machine learning techniques for plant leaf disease detection, focusing on models applicable to jamun leaves, highlighting current advancements, limitations, and future research directions.

Contribution

It provides a comprehensive assessment of various ML models and vision transformers for plant disease detection, specifically targeting jamun leaf disease diagnosis.

Findings

Vision Transformer models show promise for leaf disease classification.

Deep learning models like DenseNet and ResNet perform effectively on datasets.

The review identifies gaps and future opportunities in automated jamun leaf disease detection.

Abstract

Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.