Leaf disease is a common fatal disease for plants. Early diagnosis and
detection is necessary in order to improve the prognosis of leaf diseases
affecting plant. For predicting leaf disease, several automated systems have
already been developed using different plant pathology imaging modalities. This
paper provides a systematic review of the literature on leaf disease-based
models for the diagnosis of various plant leaf diseases via deep learning. The
advantages and limitations of different deep learning models including Vision
Transformer (ViT), Deep convolutional neural network (DCNN), Convolutional
neural network (CNN), Residual Skip Network-based Super-Resolution for Leaf
Disease Detection (RSNSR-LDD), Disease Detection Network (DDN), and YOLO (You
only look once) are described in this review. The review also shows that the
studies related to leaf disease detection applied different deep learning
models to a number of publicly available datasets. For comparing the
performance of the models, different metrics such as accuracy, precision,
recall, etc. were used in the existing studies.

该研究论文通过深度学习模型系统综述了叶病害诊断领域的多种叶病害基于叶片的模型，包括 Vision Transformer (ViT)、Deep convolutional neural network (DCNN)、Convolutional neural network (CNN)、Residual Skip Network-based Super-Resolution for Leaf Disease Detection (RSNSR-LDD)、Disease Detection Network (DDN) 和 You only look once (YOLO) 等。研究表明，针对叶病害的深度学习模型被应用于多个公开数据集，研究使用了准确率、精确率、召回率等不同的性能指标来比较模型的表现。