Forage yield and quality estimation by means of UAV and hyperspectral imaging
Forage yield and quality estimation by means of UAV and hyperspectral imaging
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DOI：
10.1007/s11119-021-09790-2
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发表时间：
2021-03-18
影响因子：
6.2
通讯作者：
Korsaeth，A.
中科院分区：
文献类型：
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作者：
Geipel，J.;Bakken，A. K.;Korsaeth，A.
本研究探讨了季节性机载高光谱成像在校正稳健的牧草产量和质量估计模型方面的潜力。利用无人机（UAV）和高光谱成像仪在450 ~ 800 nm范围内捕获草-豆科植物混合物的冠层反射。在挪威东南部和中部的两个地点进行了两年多的测量。所有图像都经过辐射和几何校正，然后处理成携带冠层反射率信息的正射影像。将数据（n = 707）分成两部分，使用一半数据用于模型校准，其余一半用于验证。利用几种功率偏最小二乘回归（PPLSR）模型对反射率数据进行拟合，估算新鲜（FM）和干物质（DM）产量，以及粗蛋白质（CP）、干物质消化率（DMD）、中性洗涤纤维（NDF）和不可消化中性洗涤纤维（iNDF）含量。将这些模型的预测性能与基于选定植被指数和株高的简单线性回归（SLR）模型的预测性能进行比较。基于汇总数据的一般模型预测精度最高，PPLSR预测FM、DM、CP、DMD、NDF和iNDF含量的相对验证均方根误差分别为14.2% （2550 kg FM ha−1）、15.2% （555 kg DM ha−1）、11.7% （1.32 g CP 100 g−1DM）、2.4% （1.71 g DMD 100 g−1DM）、4.8% （2.72 g NDF 100 g−1DM）和12.8% （1.32 g iNDF 100 g−1DM）。测试的单反模型都没有达到可接受的预测精度。
This study investigated the potential of in-season airborne hyperspectral imaging for the calibration of robust forage yield and quality estimation models. An unmanned aerial vehicle (UAV) and a hyperspectral imager were used to capture canopy reflections of a grass-legume mixture in the range of 450 nm to 800 nm. Measurements were performed over two years at two locations in Southeast and Central Norway. All images were subject to radiometric and geometric corrections before being processed to ortho-images, carrying canopy reflectance information. The data (n = 707) was split in two, using half the data for model calibration and the remaining half for validation. Several powered partial least squares regression (PPLSR) models were fitted to the reflectance data to estimate fresh (FM) and dry matter (DM) yields, as well as crude protein (CP), dry matter digestibility (DMD), neutral detergent fibre (NDF), and indigestible neutral detergent fibre (iNDF) content. Prediction performance of these models was compared with the prediction performance of simple linear regression (SLR) models, which were based on selected vegetation indices and plant height. The highest prediction accuracies for general models, based on the pooled data, were achieved by means of PPLSR, with relative root-mean-square errors of validation of 14.2% (2550 kg FM ha−1), 15.2% (555 kg DM ha−1), 11.7% (1.32 g CP 100 g−1DM), 2.4% (1.71 g DMD 100 g−1DM), 4.8% (2.72 g NDF 100 g−1DM), and 12.8% (1.32 g iNDF 100 g−1DM) for the prediction of FM, DM, CP, DMD, NDF, and iNDF content, respectively. None of the tested SLR models achieved acceptable prediction accuracies.