Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework
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Published:2024-07
Issue:
Volume:222
Page:109021
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ISSN:0168-1699
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Container-title:Computers and Electronics in Agriculture
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language:en
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Short-container-title:Computers and Electronics in Agriculture
Author:
Wayama Ryosuke,
Sasaki Yuki,
Kagiwada Satoshi,
Iwasaki Nobusuke,
Iyatomi HitoshiORCID
Reference40 articles.
1. Bollis, E., Pedrini, H., Avila, S., 2020. Weakly supervised learning guided by activation mapping applied to a novel citrus pest benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. pp. 70–71.
2. LeafGAN: An effective data augmentation method for practical plant disease diagnosis;Cap;IEEE Trans. Autom. Sci. Eng.,2020
3. Deep learning models for plant disease detection and diagnosis;Ferentinos;Comput. Electron. Agric.,2018
4. New standards to curb the global spread of plant pests and diseases;Food,2019
5. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition;Fuentes;Sensors,2017
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