融合改进YOLO v8s-obb与NPRP-A的无人机遥感水稻估产方法

UAV-based Rice Yield Estimation Method Integrating Improved YOLO v8s-obb and NPRP-A

摘要:

针对现有水稻估产方法忽略同种作物个体差异性及种植密度与单穗质量耦合影响，导致估产精度受限的核心问题，提出了一种融合改进YOLO v8s-obb模型与NPRP-A的水稻估产方法。通过优化YOLO v8s-obb结构，引入C2f_DCNv4模块、GSConv、EPSANet和DAT注意力机制，提升模型对稻穗的多尺度精准检测能力。为提升估产结果的可信度，在水稻成熟期实地收割样本小区作物，采集实测产量数据作为模型验证参考，并引入高斯核密度估计与NPRP-A单穗质量建模方法，建立密度调控与单穗质量之间的非线性映射关系，实现估产精度提升。实验结果显示，在3个1m2估产小区中，本方法预测误差均低于5.3%，最小误差为2.2%，优于传统方法，展示出良好的实际应用前景。该研究为水稻高效、精准估产提供了可靠技术方案，也为智慧农业中的作物表型识别与产量分析提供了思路。

Abstract:

Aiming to address the core limitations of existing rice yield estimation methods, particularly the neglect of intra-species variability and the coupled influence of planting density and single-panicle weight, which compromise accuracy, a novel approach was proposed integrating an improved YOLO v8s-obb model with the NPRP-A method. The YOLO v8s-obb architecture was enhanced by incorporating the C2f_DCNv4 module, GSConv, EPSANet, and DAT attention mechanism to strengthen multi-scale detection of rice panicles. To ensure estimation reliability, ground-truth yield data were collected through field harvesting of sample plots at maturity. Gaussian kernel density estimation and the NPRP-A-based single-panicle weight modeling were further introduced to establish a nonlinear mapping between planting density and panicle weight, capturing their interactive effects. Experimental validation was conducted across three 1m2 plots. Results showed prediction errors consistently below 5.3%, with the lowest error at 2.2%, significantly outperforming traditional methods. This demonstrated the method’s high accuracy and robustness in real-world conditions. The proposed framework not only delivered a reliable technical solution for precise and efficient rice yield estimation but also advanced crop phenotyping and yield analysis in smart agriculture. By explicitly accounting for individual plant variation and density-yield interactions, the approach bridged a critical gap in current remote sensing-based estimation practices. Its design supported scalable deployment and offered practical value for precision farming applications, highlighting strong potential for broader adoption in agricultural monitoring systems.