Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching implications for yield and quality in the tea industry. The rise of machine learning has enabled the development of innovative approaches to combat these diseases. Early detection and diagnosis are crucial for effective crop management. For predicting tea leaf disease, several automated systems have already been developed using different image processing techniques. This paper delivers a systematic review of the literature on machine learning methodologies applied to diagnose tea leaf disease via image classification. It thoroughly evaluates the strengths and constraints of various Vision Transformer models, including Inception Convolutional Vision Transformer (ICVT), GreenViT, PlantXViT, PlantViT, MSCVT, Transfer Learning Model & Vision Transformer (TLMViT), IterationViT, IEM-ViT. Moreover, this paper also reviews models like Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, YOLOv5, YOLOv7, Convolutional Neural Network (CNN), Deep CNN, Non-dominated Sorting Genetic Algorithm (NSGA-II), MobileNetv2, and Lesion-Aware Visual Transformer. These machine-learning models have been tested on various datasets, demonstrating their real-world applicability. This review study not only highlights current progress in the field but also provides valuable insights for future research directions in the machine learning-based detection and classification of tea leaf diseases.

翻译：茶叶病害是农业生产力面临的主要挑战，对茶产业的产量和品质具有深远影响。机器学习的兴起推动了应对这些病害的创新方法发展。早期检测与诊断对有效作物管理至关重要。目前，已有多种基于不同图像处理技术的自动化系统被开发用于预测茶叶病害。本文对通过图像分类诊断茶叶病害的机器学习方法进行了系统性文献综述，全面评估了多种视觉Transformer模型的优势与局限，包括Inception卷积视觉Transformer（ICVT）、GreenViT、PlantXViT、PlantViT、MSCVT、迁移学习模型与视觉Transformer（TLMViT）、IterationViT、IEM-ViT。此外，本文还综述了稠密卷积网络（DenseNet）、残差神经网络（ResNet）-50V2、YOLOv5、YOLOv7、卷积神经网络（CNN）、深度CNN、非支配排序遗传算法（NSGA-II）、MobileNetv2以及病灶感知视觉Transformer等模型。这些机器学习模型已在多个数据集上测试，验证了其实际应用可行性。本综述不仅突出了该领域的最新进展，也为基于机器学习的茶叶病害检测与分类的未来研究方向提供了重要参考。