Purpose

The use of Unmanned aerial vehicle (UAV) data for predicting crop above-ground biomass (AGB) is becoming a more feasible alternative to destructive methods. However, canopy height, vegetation index (VI), and other traditional features can become saturated during the mid to late stages of crop growth, significantly impacting the accuracy of AGB prediction.

Methods

In 2022 and 2023, UAV multispectral, RGB, and light detection and ranging point cloud data of wheat populations were collected at seven growth stages across two experimental fields. The point cloud depth features were extracted using the improved PointNet++ network, and AGB was predicted by fusion with VI, color index (CI), and texture index (TI) raster image features.

Results

The findings indicate that when the point cloud depth features were fused, the R² values predicted from VI, CI, TI, and canopy height model images increased by 0.05, 0.08, 0.06, and 0.07, respectively. For the combination of VI, CI, and TI, R² increased from 0.86 to a maximum of 0.9, while the root-mean-square error (RMSE) and mean absolute error were 1.80 t ha⁻¹ and 1.36 t ha⁻¹, respectively. Additionally, our findings revealed that the hybrid fusion exhibits the highest accuracy, it demonstrates robust adaptability in predicting AGB across various years, growth stages, crop varieties, nitrogen fertilizer applications, and densities.

Conclusion

This study effectively addresses the saturation in spectral and chemical information, provides valuable insights for high-precision phenotyping and advanced crop field management, and serves as a reference for studying other crops and phenotypic parameters.

中文翻译：

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结合二维图像和点云深度学习预测小麦地上生物量

 目的

使用无人机（UAV）数据来预测作物地上生物量（AGB）正在成为破坏性方法的更可行的替代方案。然而，冠层高度、植被指数（VI）等传统特征在作物生长的中后期可能会饱和，严重影响AGB预测的准确性。

 方法

2022年和2023年，在两个试验田收集了小麦种群七个生长阶段的无人机多光谱、RGB和光探测和测距点云数据。使用改进的PointNet++网络提取点云深度特征，并通过融合VI、颜色索引（CI）和纹理索引（TI）栅格图像特征来预测AGB。

 结果

研究结果表明，当融合点云深度特征时，根据VI、CI、TI和树冠高度模型图像预测的R ²值分别增加了0.05、0.08、0.06和0.07。对于VI、CI和TI的组合， R ²从0.86增加到最大值0.9，而均方根误差(RMSE)和平均绝对误差分别为1.80 t ha ^-1和1.36 t ha ^-1 ，分别。此外，我们的研究结果表明，混合融合表现出最高的准确性，它在预测不同年份、生长阶段、作物品种、氮肥施用和密度的 AGB 方面表现出强大的适应性。

 结论

该研究有效解决了光谱和化学信息的饱和问题，为高精度表型分析和先进作物田间管理提供了宝贵的见解，并为研究其他作物和表型参数提供了参考。