文献类型：期刊文献

英文题名：Identification of varieties in Camellia oleifera leaf based on deep learning technology

作者：Dong, Zhipeng[1,2] Yang, Fan[2] Du, Jiayi[2] Wang, Kailiang[1] Lv, Leyan[3] Long, Wei[1]

第一作者：Dong, Zhipeng

通信作者：Long, W[1]

机构：[1]Chinese Acad Forestry, Res Inst Subtrop Forestry, State key Lab Tree Genet & Breeding, Hangzhou 311400, Zhejiang, Peoples R China;[2]Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Changsha 410004, Hunan, Peoples R China;[3]Zhejiang Tongji Vocat Coll Sci & Technol, Coll Hydraul Engn, Hangzhou 311231, Zhejiang, Peoples R China

年份：2024

卷号：216

外文期刊名：INDUSTRIAL CROPS AND PRODUCTS

收录：;EI(收录号：20242016094351);Scopus(收录号：2-s2.0-85192759431);WOS:【SCI-EXPANDED(收录号:WOS:001241637300001)】；

基金：Acknowledgments This research was funded by Pioneer and Leading Goose R & D Pro-gram of Zhejiang, China and (2021C02038) ; Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding, China (2021C02070-2) .

语种：英文

外文关键词：Camellia oleifera; Varieties identification; Deep learning; RegNetY-4.0GF-CBAM

摘要：Camellia oleifera, a woody oil tree, is widely recognized for its valuable oil. Different cultivars of C.oleifera exhibit distinct growth characteristics, oil content, and oil composition. Therefore, the classification of C.oleifera cultivars can aid in the better utilization of C.oleifera resources and improve yield and quality. However, the identification of cultivars remains challenging due to genetic diversity, similarities in leaf morphology, and the influence of geographical environment, among other factors. Comprehensive cultivar identification methods for studying C.oleifera must be established to overcome these obstacles. We selected 118 varieties that grew under natural light conditions and collected whole pest-free mature leaves. After filtering out invalid images, we constructed a leaf cultivar dataset consisting of 30890 images of C. oleifera. The results showed that RegNetY-4.0GF-Convolutional Block Attention Module provides significant advantages over other methods in cultivar recognition, including VGG16, ResNet50, EffificientNet-B4, and EffificientNet-B4-CBAM. It achieved an overall accuracy of 93.7 % and an F1-score of 0.945, much higher than the accuracy of other compared methods. CBAM can significantly improve the accuracy of varieties recognized. The overall results showed that deep learning could effectively distinguish C.oleiera leaves of different varieties. This method provided an effective way to identify C.oleifera varieties quickly and nondestructively.