SoyCountNet: a deep learning framework for counting and locating soybean seeds in field environment.

Abstract

Introduction: Accurate counting and spatial localization of soybean seeds-particularly Seeds Per Plant (SPP)-are critical for yield estimation and cultivar evaluation. In field environments, however, complex backgrounds, pod occlusion, and uneven grain filling make high-precision counting challenging, and traditional methods often struggle to balance accuracy and robustness. Methods: To address these challenges, this study proposes SoyCountNet, a deep learning framework for automatic soybean seed counting and localization at the single-plant level under field conditions. The model is built on a self-constructed field-based phenotyping platform and optimized using the lightweight Point-to-Point Network (P2PNet). For feature extraction, a VGG19_BN backbone and a Super Token Sampling Vision Transformer (SViT) module are employed to enhance local feature representation and global contextual understanding. During feature fusion, the Efficient Channel Attention (ECA) mechanism strengthens seed-related features while suppressing interference from leaves, stems, and soil. Furthermore, an improved loss function that combines point-distance constraints with overlap penalties enhances both counting precision and spatial consistency. Results: Experimental results demonstrate that SoyCountNet outperforms existing approaches on the field soybean dataset. It achieves a mean absolute error (MAE) of 4.61, a root mean square error (RMSE) of 6.03, and a coefficient of determination (R²) of 0.94. The model demonstrates consistent performance across the tested soybean cultivars, providing reliable SPP estimates within the evaluated dataset. Discussion: These findings indicate that SoyCountNet offers a reliable and scalable solution for precise soybean seed counting and localization in complex field environments. Its lightweight architecture allows deployment on intelligent agricultural platforms, supporting high-throughput phenotyping, yield prediction, and precision breeding, while providing a foundation for the future development of intelligent and sustainable agricultural technologies.

Plant phenotyping relevance

単一個体の種子数（SPP）を画像から自動計数・位置推定する手法を開発し、圃場データで性能評価しているため、植物表現型取得が中心である。

Code and data availability

The paper's soybean seed counting dataset (800 point-annotated field images) and SoyCountNet model are not publicly available; the Data Availability Statement restricts access to requests to the corresponding author. No public repository, code, or dataset URL is provided in the supplied blocks.

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