Weed Detection in Peanut Fields Based on Machine Vision
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Cited by:
- Shanxin Zhang & Hao Feng & Shaoyu Han & Zhengkai Shi & Haoran Xu & Yang Liu & Haikuan Feng & Chengquan Zhou & Jibo Yue, 2022. "Monitoring of Soybean Maturity Using UAV Remote Sensing and Deep Learning," Agriculture, MDPI, vol. 13(1), pages 1-21, December.
- Jibo Yue & Chengquan Zhou & Haikuan Feng & Yanjun Yang & Ning Zhang, 2023. "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring," Agriculture, MDPI, vol. 13(10), pages 1-4, October.
- Xinle Zhang & Jian Cui & Huanjun Liu & Yongqi Han & Hongfu Ai & Chang Dong & Jiaru Zhang & Yunxiang Chu, 2023. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
- Marios Vasileiou & Leonidas Sotirios Kyrgiakos & Christina Kleisiari & Georgios Kleftodimos & George Vlontzos & Hatem Belhouchette & Panos M. Pardalos, 2024. "Transforming weed management in sustainable agriculture with artificial intelligence: a systematic literature review towards weed identification and deep learning," Post-Print hal-04297703, HAL.
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Keywords
weed identification; YOLOv4-Tiny; attention mechanism; multiscale detection; precision agriculture;All these keywords.
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