{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:33:34Z","timestamp":1763458414075,"version":"build-2065373602"},"reference-count":73,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tree Breeding Research Project of Zhejiang Province","award":["2021C02070-1"],"award-info":[{"award-number":["2021C02070-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Betula luminifera, an indigenous hardwood tree in South China, possesses significant economic and ecological value. In view of the current severe drought situation, it is urgent to enhance this tree\u2019s drought tolerance. However, traditional artificial methods fall short of meeting the demands of breeding efforts due to their inefficiency. To monitor drought situations in a high-throughput and automatic approach, a deep learning model based on phenotype characteristics was proposed to identify and classify drought stress in B. luminifera seedlings. Firstly, visible-light images were obtained from a drought stress experiment conducted on B. luminifera shoots. Considering the images\u2019 characteristics, we proposed an SAM-CNN architecture by incorporating spatial attention modules into classical CNN models. Among the four classical CNNs compared, ResNet50 exhibited superior performance and was, thus, selected for the construction of the SAM-CNN. Subsequently, we analyzed the classification performance of the SAM-ResNet50 model in terms of transfer learning, training from scratch, model robustness, and visualization. The results revealed that SAM-ResNet50 achieved an accuracy of 1.48% higher than that of ResNet50, at 99.6%. Furthermore, there was a remarkable improvement of 18.98% in accuracy, reaching 82.31% for the spatial transform images generated from the test set images by applying movement and rotation for robustness testing. In conclusion, the SAM-ResNet50 model achieved outstanding performance, with 99.6% accuracy and realized high-throughput automatic monitoring based on phenotype, providing a new perspective for drought stress classification and technical support for B. luminifera-related breeding work.<\/jats:p>","DOI":"10.3390\/rs16224141","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:16:51Z","timestamp":1730884611000},"page":"4141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["SAM-ResNet50: A Deep Learning Model for the Identification and Classification of Drought Stress in the Seedling Stage of Betula luminifera"],"prefix":"10.3390","volume":"16","author":[{"given":"Shiya","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Zhejiang International Science and Technology Cooperation Base for Plant Germplasm Resources Conservation and Utilization, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Hao","family":"Liang","sequence":"additional","affiliation":[{"name":"Zhejiang International Science and Technology Cooperation Base for Plant Germplasm Resources Conservation and Utilization, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2417-119X","authenticated-orcid":false,"given":"Dong","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Xiange","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Zhejiang International Science and Technology Cooperation Base for Plant Germplasm Resources Conservation and Utilization, Zhejiang A&F University, Hangzhou 311300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7578-2869","authenticated-orcid":false,"given":"Erpei","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Zhejiang International Science and Technology Cooperation Base for Plant Germplasm Resources Conservation and Utilization, Zhejiang A&F University, Hangzhou 311300, China"}]},{"given":"Huahong","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, China"},{"name":"Zhejiang International Science and Technology Cooperation Base for Plant Germplasm Resources Conservation and Utilization, Zhejiang A&F University, Hangzhou 311300, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, L., Yuan, X., Xie, Z., Wu, P., and Li, Y. 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