PDDD-PreTrain:A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis

Plant diseases threaten global food security by reducing crop yield;thus,diagnosing plant diseases is critical to agricultural production.Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming,costly,inefficient,and subjective disadvantages.As a mainstream AI method,deep learning has substantially improved plant disease detection and diagnosis for precision agriculture.In the meantime,most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves.However,the commonly used pre-trained models are from the computer vision dataset,not the botany dataset,which barely provides the pre-trained models sufficient domain knowledge about plant disease.Furthermore,this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision.To address this issue,we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis.In addition,we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification,plant disease detection,plant disease segmentation,and other subtasks.The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time,thereby supporting the better diagnosis of plant diseases.In addition,our pre-trained models will be open-sourced at https://pd.samlab.cn/and Zenodo platform https://doi.org/10.5281/zenodo.7856293.