CLIP-TLNet:Canopy light interception prediction with Transformer-LSTM network through 3D complexity-temporal dynamics modelingOA
CLIP-TLNet:Canopy light interception prediction with Transformer-LSTM network through 3D complexity-temporal dynamics modeling
Meng Yang;Yuying Gao;Benye Xi;Xin Wang;Qingqing Huang;Weiliang Meng
School of Information Science and Technology,Beijing Forestry University,Beijing,100083,ChinaSchool of Information Science and Technology,Beijing Forestry University,Beijing,100083,ChinaCollege of Forestry,Beijing Forestry University,Beijing,100083,ChinaSchool of Landscape Architecture,Beijing Forestry University,Beijing,100083,ChinaSchool of Technology,Beijing Forestry University,Beijing,100083,ChinaState Key Laboratory of Multimodal Artificial Intelligence Systems,Institute of Automation,Chinese Academy of Sciences,Beijing,100190,China||School of Artificial Intelligence,University of Chinese Academy of Sciences,Beijing,100049,China
Canopy light interception predictionTransformer-LSTM hybrid networkRegional canopy complexity
Canopy light interception predictionTransformer-LSTM hybrid networkRegional canopy complexity
《植物表型组学（英文）》 2026 (2)
22-31,10
This research was supported by 5·5 Engineering Research & Innova-tion Team Project of Beijing Forestry University(No:BLRC2023C05),National Natural Science Foundation of China(Nos.32271983,62376271,U22B2034,62262043,62172416,62365014,and 62572059),Beijing Natural Science Foundation(No.L241056,JQ23014),the Fundamental Research Funds for the Central Universities(2021ZY35),Shenzhen S&T programme(No.CJGJZD20240729141906008),and Jiangxi Provincial Natural Science Foundation No.20253BAC280104.
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