数字农科院2.0

SPP-extractor: Automatic phenotype extraction for densely grown soybean plants

文献类型： 外文期刊

作者： Zhou, Wan;Chen, Yijie;Li, Weihao;Zhang, Cong;Xiong, Yajun;Zhan, Wei;Huang, Lan;Wang, Jun;Qiu, Lijuan

作者机构：

关键词： Soybean phenotype;Branch length;Computer vision;Phenotype acquisition;A* algorithm

期刊名称： CROP JOURNAL

ISSN： 2095-5421

年卷期： 2023 年 11 卷 5 期

页码：

收录情况： SCIE（2023版） ; ; CSCD（2023-2024年度） ; ; 科技核心（2023版） ; ; 农林核心（2020版）

摘要： Automatic collecting of phenotypic information from plants has become a trend in breeding and smart agriculture. Targeting mature soybean plants at the harvesting stage, which are dense and overlapping, we have proposed the SPP-extractor (soybean plant phenotype extractor) algorithm to acquire phenotypic traits. First, to address the mutual occultation of pods, we augmented the standard YOLOv5s model for target detection with an additional attention mechanism. The resulting model could accurately identify pods and stems and could count the entire pod set of a plant in a single scan. Second, considering that mature branches are usually bent and covered with pods, we designed a branch recognition and measurement module combining image processing, target detection, semantic segmentation, and heuristic search. Experimental results on real plants showed that SPP-extractor achieved respective R2 scores of 0.93-0.99 for four phenotypic traits, based on regression on manual measurements.(c) 2023 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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