Lu Ning, Zhou Jie, Han Zixu, Li Dong, Cao Qiang, Yao Xia, Tian Yongchao, Zhu Yan, Cao Weixing, Cheng Tao
National Engineering and Technology Center for Information Agriculture (NETCIA), Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, One Weigang, Nanjing, 210095 Jiangsu China.
Plant Methods. 2019 Feb 20;15:17. doi: 10.1186/s13007-019-0402-3. eCollection 2019.
Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF).
Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy ( = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics.
Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.
地上生物量（AGB）是表征作物生长状况和预测粮食产量的一个广泛应用的农艺参数。以无损方式快速准确地估算AGB，有助于在精准作物管理方面做出明智决策。以往研究探讨了利用无人机（UAV）数据得出的植被指数（VIs）和冠层高度指标来估算各种作物的AGB。然而，输入变量要么来自单一类型的数据，要么来自无人机上的不同传感器。来自单个低成本无人机系统的VIs和冠层高度指标的组合能否提高AGB估算精度仍不明确。本研究使用低成本无人机系统，于关键生长阶段在中国东部如皋地区小麦30米飞行高度处获取图像。实验于2016年和2017年进行，涉及36个田间地块，代表了品种、施氮水平和播种密度的变化。我们采用逐步多元线性回归（SMLR）和三种机器学习算法（支持向量回归，SVR；极限学习机，ELM；随机森林，RF）评估了VIs、冠层高度指标及其组合在小麦AGB估算中的性能。
我们的结果表明，VIs和冠层高度指标的组合比单独使用VIs或冠层高度指标提高了小麦AGB的估算精度。具体而言，无论使用所有原始变量还是SMLR选择的变量，在SMLR和三种机器学习算法中，RF表现最佳。将RF应用于VIs和冠层高度指标的组合时，获得了最佳精度（R² = 0.78，RMSE = 1.34 t/ha，rRMSE = 28.98%）。
我们的研究结果表明，一种由RF算法以及来自消费级低成本无人机系统的RGB图像和点云数据组合而成的低成本方法，可用于提高AGB估算精度，并在快速估算其他生长参数的实际应用中具有潜力。
