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.

茶叶疾病的机器学习诊断方法的系统综述，评估了多种图像分类方法，包括 Inception 卷积视觉变压器、GreenViT、PlantXViT、PlantViT、MSCVT、迁移学习模型和视觉变压器、IterationViT 和 IEM-ViT，以及 DenseNet、ResNet-50V2、YOLOv5、YOLOv7、CNN、Deep CNN、NSGA-II、MobileNetv2 和 Lesion-Aware Visual Transformer 模型的优势和限制，提供了茶叶疾病检测和分类机器学习方向的有价值的见解。