High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.

Abstract

The automated collection of plant phenotypic information has become a trend in breeding and smart agriculture. Four YOLOv8-based models were used to segment mature soybean plants placed in a simple background in a laboratory environment, identify pods, distinguish the number of soybeans in each pod, and obtain soybean phenotypes. The YOLOv8-Repvit model yielded the most optimal recognition results, with an R2 coefficient value of 0.96 for both pods and beans, and the RMSE values were 2.89 and 6.90, respectively. Moreover, a novel algorithm was devised to efficiently differentiate between the main stem and branches of soybean plants, called the midpoint coordinate algorithm (MCA). This was accomplished by linking the white pixels representing the stems in each column of the binary image to draw curves that represent the plant structure. The proposed method reduces computational time and spatial complexity in comparison to the A* algorithm, thereby providing an efficient and accurate approach for measuring the phenotypic characteristics of soybean plants. This research lays a technical foundation for obtaining the phenotypic data of densely overlapped and partitioned mature soybean plants under field conditions at harvest.

Plant phenotyping relevance

深層学習とコンピュータビジョンによるダイズの莢数・粒数・植物構造などの表現型抽出手法を開発し、精度比較・検証しており、方法が研究の中心である。

Code and data availability

The paper describes soybean phenotype datasets (442 labeled plant images, 12,110 labeled pods, augmented to 2210 images) and YOLOv8-Repvit/MCA analysis, but the Data Availability Statement states the data are only available upon request due to privacy; no public repository, code deposit, or authors' public URL is given

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