生物
提取器
分割
表型
交货地点
模式识别(心理学)
园艺
人工智能
计算机科学
遗传学
基因
工艺工程
工程类
作者
Zhou Wan,Yijie Chen,Weihao Li,Cong Zhang,Yajun Xiong,Wei Zhan,Lan Huang,Jun Wang,Lijuan Qiu
出处
期刊:Crop Journal
[Elsevier]
日期:2023-10-01
卷期号:11 (5): 1569-1578
被引量:2
标识
DOI:10.1016/j.cj.2023.04.012
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 developed 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.
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