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Automatic stem phenotyping in soybean using keypoint detection
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AI
DOI：10.1016/j.engappai.2025.113300.png)
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Abstract
En 中文
Soybean breeding critically relies on stem phenotypes, as they directly impact yield and lodging resistance. Traditional measurement methods are labor-intensive, prone to human error, and often require destructive sampling. Although artificial intelligence (AI) has emerged as a transformative alternative, existing studies on soybean stem phenotyping remain limited and imprecise. An AI implementation integrating keypoint detection and localization provides a promising solution. This study proposes Soybean-pose, a novel approach that models soybean plants as structural “bodily forms” via keypoint detection. By integrating a semi-supervised iterative self-training paradigm with a hybrid Convolutional Neural Network-Swin Vision Transformer (CNN-SViT) architecture, Soybean-pose achieves high-precision detection of soybean stem nodes via limited labeled data and pseudo-label iterative optimization strategies, and integrates phenotypic quantification algorithms to accomplish automated parsing of stem-related phenotypes. To support this research, the Soybean Stem Keypoint (SSK) dataset is constructed and publicly released. Soybean-pose achieves an average precision at 50 % intersection-over-union (AP50) of 91.8 % on the validation set and 93.2 % on the test-dev set. The Pearson correlation coefficients (R) for pitch number, internode length, and main stem length are 0.986, 0.989, and 0.978, respectively. This AI application enables accurate measurement of soybean stem phenotypes, reduces labor costs, and minimizes measurement errors, demonstrating its potential to accelerate breeding processes.
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