YOLOv5-T: A precise real-time detection method for maize tassels based on UAV low altitude remote sensing images
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Published:2024-06
Issue:
Volume:221
Page:108991
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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:
Gao RuiORCID, Jin Yishu, Tian Xin, Ma Zheng, Liu Siqi, Su Zhongbin
Reference44 articles.
1. Maize tassel detection from UAV imagery using deep learning;Alzadjali;Front Robot AI,2021 2. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.J.I.t.o.p.a., intelligence, m., 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. 40, 834-848. 3. Falahat, S., Karami, A.J.M.T., Applications, 2023. Maize tassel detection and counting using a YOLOv5-based model. 82, 19521-19538. 4. Fan, J., Zhou, J., Wang, B., de Leon, N., Kaeppler, S.M., Lima, D.C., Zhang, Z.J.R.S., 2022. Estimation of maize yield and flowering time using multi-temporal UAV-based hyperspectral data. 14, 3052. 5. Gao, M., Yang, F., Wei, H., Liu, X.J.R.S., 2022. Individual maize location and height estimation in field from uav-borne lidar and rgb images. 14, 2292.
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