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.

翻译：贾姆果树叶病害对农业生产率构成严重威胁，严重影响贾姆果产业的产量和品质。机器学习的出现为有效应对这些病害开辟了新途径。早期检测与诊断对于成功的作物管理至关重要。尽管尚未开发出专门针对贾姆果树叶病害检测的自动化系统，但已有多种基于图像处理技术的自动化系统应用于类似病害检测。本文对通过图像分类诊断植物叶部病害的机器学习方法进行了全面评述，这些方法可适用于贾姆果树叶病害检测。文章细致评估了多种视觉Transformer模型的优势与局限，包括迁移学习与视觉Transformer模型（TLMViT）、SLViT、SE-ViT、IterationViT、Tiny-LeViT、IEM-ViT、GreenViT和PMViT。此外，还评述了稠密卷积网络（DenseNet）、残差神经网络（ResNet）-50V2、EfficientNet、集成模型、卷积神经网络（CNN）以及局部可逆Transformer等模型。这些机器学习模型已在多种数据集上进行评估，展示了其现实适用性。本评述不仅揭示了该领域当前的研究进展，还为基于机器学习的贾姆果树叶病害检测与分类的未来研究方向提供了宝贵见解。