The research on detection of crop diseases ranking based on transfer learning
Author | Yang, Mengji |
Author | He, Yu |
Author | Zhang, Haiqing |
Author | Li, DaiWei |
Author | Bouras, Abdelaziz |
Author | Yu, Xi |
Author | Tang, Yiqian |
Available date | 2023-04-09T08:34:51Z |
Publication Date | 2019 |
Publication Name | Proceedings - 2019 6th International Conference on Information Science and Control Engineering, ICISCE 2019 |
Resource | Scopus |
Abstract | Crop diseases are a major global threat to food security. Because the lack of agriculture experts or necessary facilities, it is difficult to determine the type of disease, as well as the degree of disease in time, which became the major factor affecting in crop production. In recent years, with the development of the transfer learning in deep learning domain, the experience of experts can be simulated to detect crop diseases in time. In this paper, we have proposed an improved transfer learning method based on ResNet 50 in crop disease diagnosis. The AI Challenger 2018 dataset has been deeper analyzed, the degree of crops diseases are detected. Comparing with non-transfer learning, the proposed transfer learning method achieved better results, which can significantly improve accuracy results by 5.1%~1.87% with reducing half of the running time. 2019 IEEE. |
Sponsor | ACKNOWLEDGMENT This research thanks for AI Challenger 2018 for providing the agricultural dataset. It is supported by the open fund (MSSB-2019-02) of Key Laboratory of Pattern Recognition and Intelligent Information Processing, Institutions of Higher Education of Sichuan Province, Chengdu University, China and Erasmus+ SHYFTE project (598649-EPP-1-2018-1-FR-EPPKA2-CBHE-JP) which funded with support from the European Commission. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Crop disease Deep learning ResNet 50 Transfer learning |
Type | Conference Paper |
Pagination | 620-624 |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Computer Science & Engineering [2281 items ]