o the website. X.D., Q.H., Q.G., and Xue Wu conducted the experiments. Q.W., L.L. and G.H. provided funding support. All authors contributed equally to the writing of the manuscript. Competing interests : The authors declare that they have no competing interests. Data Availability All data and codes are available on the website https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293 . References 1. Food and Agriculture Organization. World food and agriculture—statistical yearbook 2020. Rome (Italy): FAO; 2020. 2. Bruinsma J. The resource outlook to 2050: By how much do land, water and crop yields need to increase by 2050? How to feed the World in 2
Open resource ↗pd.samlab.cn · lines:707-779Paper record
PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis.
Plant phenomics (Washington, D.C.) · 18 May 2023 · 10.34133/plantphenomics.0054
Abstract
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.
Code and data availability
The authors publicly release their PDDD plant disease dataset, pre-trained model weights, and code via their project website and a Zenodo deposit, as stated in the abstract and Data Availability section.
u conducted the experiments. Q.W., L.L. and G.H. provided funding support. All authors contributed equally to the writing of the manuscript. Competing interests : The authors declare that they have no competing interests. Data Availability All data and codes are available on the website https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293 . References 1. Food and Agriculture Organization. World food and agriculture—statistical yearbook 2020. Rome (Italy): FAO; 2020. 2. Bruinsma J. The resource outlook to 2050: By how much do land, water and crop yields need to increase by 2050? How to feed the World in 2050. Paper presnted at: Proceedings of a Technical Meeting
Open resource ↗Zenodo · 10.5281/zenodo.7856293 · lines:707-779