| 刊名 | Plant Diseases and Pests |
| 作者 | Mengmeng ZHANG, Xiujuan WANG, Mengzhen KANG, Jing HUA, Haoyu WANG, FeiYue WANG |
| 作者单位 | State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences |
| DOI | 10.19579/j.cnki.plant-d.p.2024.02.003 |
| 年份 | 2024 |
| 刊期 | 2 |
| 页码 | 9-16 |
| 关键词 | Rice disease and pest identification; Convolutional neural networks; Distributed training; Federated learning(FL); Open-source data sharing platform |
| 摘要 | In this paper, a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method. We demonstrated that the training dataset has a significant impact on the training results, in addition to the optimization achieved through the model structure. However, the lack of open-source agricultural data, combined with the absence of a comprehensive open-source data sharing platform, remains a substantial obstacle. This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data, the low level of education of most employees, underdeveloped distributed training systems and unsecured data security. To address these challenges, this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning (FL) framework, aiming to overcome the deficiency of high-quality data in agricultural field training. |
