Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive
  Review
Auvick Chandra Bhowmik Dr. Md. Taimur Ahad Yousuf Rayhan Emon
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.