A comprehensive review on Plant Leaf Disease detection using Deep
  learning
Sumaya Mustofa Md Mehedi Hasan Munna Yousuf Rayhan Emon Golam Rabbany Md Taimur Ahad
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.