Research Article | Open Access

Volume 2025 |Article ID 100083 | https://doi.org/10.1016/j.plaphe.2025.100083

Field phenotyping for soybean density tolerance using time-series prediction and dynamic modeling

Guangyao Sun,¹ Yong Zhang,² Lei Meng,³ Yaling Liu,⁴ Lei Wang,² Shuaipeng Fei,¹ Yingpu Che,⁵ Yinghui Li,⁵ Lijuan Qiu,⁵ Chunli Lv,¹ Yun Xu,¹ and Yuntao Ma ¹

¹College of Information and Electrical Engineering, China Agricultural University, East Campus, Beijing, China
²Keshan Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar, China
³Lei Meng, School of Environment, Geography, and Sustainability, Western Michigan University, Kalamazoo, MI, 49008, USA
⁴Inner Mongolia Pratacultural Technology Innovation Center Co. Ltd, Inner Mongolia, China
⁵Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

Abstract

With the increasing global demand for food, breeding soybean varieties resistant to dense planting is crucial for achieving high and stable yields. Traditional phenotyping methods are limited by insufficient temporal resolution and challenges in dynamic modeling continuity, making it difficult to elucidate the intrinsic relationship between canopy development rate and yield stability. Moreover, existing machine learning models often neglect temporal dependencies in time series predictions, leading to insufficient biological interpretability. This study proposes an innovative approach integrating spatiotemporal deep learning and dynamic modeling to quantify the dynamic changes in canopy parameters using UAV high-throughput phenotyping technology, revealing the key regulatory mechanisms of traits associated with resistance to dense planting. Based on a two-year field experiment (2022–2023) in northeast China (Qiqihaer, black soil region), this study set high (50w plants/ha) and low density (30w plants/ha) treatments across 208 soybean varieties, combined with multispectral UAV imagery (15–18 times per season) and ground-truth data, to develop a time series prediction model for leaf area index (LAI). Comparing the performance of spatiotemporal residual networks (ST-ResNet), long short-term memory networks (LSTM), and traditional random forests (RF), the ST-ResNet model demonstrated significantly superior prediction accuracy (R2 = 0.90, RMSE = 0.23 m2/m2), effectively capturing the continuous dynamics of canopy growth through its spatiotemporal feature fusion ability. By fitting the time series curves of LAI, canopy cover (CC), and plant height (PH) with P-spline, 15 intermediate traits (e.g., ΔMeanLAI-mid) were extracted. Mixed models and SHAP interpretability analysis showed that ΔMeanLAI-mid was most correlated with the dense planting yield index (ΔYield, r = 0.51). Furthermore, the high-frequency data acquisition and automated analysis framework using UAVs enabled high-throughput phenotypic screening for 208 varieties per year, significantly improving efficiency compared to traditional methods that rely on manual sampling. This study pioneers the integration of spatiotemporal deep learning with dynamic trait modeling, markedly improving the temporal continuity and stability of LAI estimation compared to traditional single-time-point prediction methods. This advancement allows for more precise quantification of canopy development rates across various growth stages, enabling a systematic analysis of how these dynamic patterns influence resistance to dense planting. By elucidating the dynamic relationship between intermediate traits and yield, this approach offers a high-precision, interpretable phenotypic analysis framework for effectively screening soybean varieties resilient to dense planting.