Timely access to crop above-ground biomass (AGB) information is crucial for estimating crop yields and managing water and fertilizer efficiently. Unmanned aerial vehicle (UAV) imagery offers promising avenues for AGB estimation due to its high efficiency and flexibility. However, the accuracy of these estimations can be influenced by various factors, including crop growth stages, the spectral resolution of UAV sensors, and flight altitudes. These factors need thorough investigation, especially in diversified cropping systems where crop diversity and growth stages interplay complexly, challenging the accuracy of AGB estimation. This study aims to estimate AGB of oats planted under different agricultural regimes—monoculture, crop rotation, and intercropping—at various growth stages (jointing, flowering, and grain-filling) and across all stages combined, using multispectral and RGB UAV images collected at different flight altitudes (25 m, 50 m, and 100 m). Three feature selection methods—maximal information coefficient (MIC), least absolute shrinkage and selection operator (LAS), and recursive feature elimination (RFE)—were tested. Four machine learning models—ridge regression (RR), multilayer perceptron (MLP), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost)—were used for estimating AGB. Each feature selection method was combined with each machine learning model (e.g., MIC-RR, MIC-MLP, MIC-LGBM, MIC-XGBoost, LAS-RR) to evaluate their performance. Results revealed that the highest accuracy in AGB estimation was achieved with images acquired at a flight altitude of 25 m. The RFE-MLP model demonstrated superior results during the jointing stage (R² = 0.84, root mean squared error (RMSE) = 217.45 kg/ha, root mean squared logarithmic error (RMSLE) = 0.16, mean absolute percentage error (MAPE) = 4.15 %), the LAS-RR model excelled in the flowering stage (R² = 0.85, RMSE = 263.03 kg/ha, RMSLE = 0.05, MAPE = 14.44 %), and the RFE-XGBoost model was most effective during the grain-filling stage (R² = 0.68, RMSE = 865.03 kg/ha, RMSLE = 0.12, MAPE = 8.88 %). For cross-stage modelling, the RFE-MLP achieved remarkable results (R² = 0.93, RMSE = 680.44 kg/ha, RMSLE = 0.16, MAPE = 12.12 %). This study demonstrates the efficacy of combining feature selection methods with machine learning algorithms to enhance the accuracy of oat AGB estimations. The involvement of multiple cropping patterns enhances the generalizability of our findings, facilitating real-time and rapid monitoring of crop growth in future diversified cropping systems.

中文翻译：

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利用无人机多光谱和 RGB 影像监测燕麦多农作物地上生物量


及时获取作物地上生物量 （AGB） 信息对于估算作物产量和有效管理水和肥料至关重要。无人机 （UAV） 影像因其高效和灵活性而为 AGB 估计提供了有前途的途径。然而，这些估计的准确性可能会受到多种因素的影响，包括作物生长阶段、无人机传感器的光谱分辨率和飞行高度。这些因素需要深入研究，尤其是在作物多样性和生长阶段复杂相互作用的多元化种植系统中，这对 AGB 估计的准确性提出了挑战。本研究旨在使用在不同飞行高度（25 m、50 m 和 100 m）收集的多光谱和 RGB 无人机图像，估计在不同农业制度（单一种植、作物轮作和间作）和所有阶段组合种植的燕麦的 AGB。测试了三种特征选择方法——最大信息系数 （MIC）、最小绝对收缩和选择运算符 （LAS） 以及递归特征消除 （RFE）。四个机器学习模型——岭回归 （RR）、多层感知器 （MLP）、光梯度提升机 （LGBM） 和极端梯度提升 （XGBoost）——用于估计 AGB。将每种特征选择方法与每种机器学习模型 （例如 MIC-RR、MIC-MLP、MIC-LGBM、MIC-XGBoost、LAS-RR） 相结合，以评估其性能。结果显示，在 25 m 飞行高度采集的图像实现了 AGB 估计的最高精度。RFE-MLP 模型在拔节阶段表现出优异的结果 （R² = 0.84，均方根误差 （RMSE） = 217。45 kg/hm2，均方根对数误差 （RMSLE） = 0.16，平均绝对百分比误差 （MAPE） = 4.15 %），LAS-RR 模型在开花期表现出色（R² = 0.85，RMSE = 263.03 kg/ha，RMSLE = 0.05，MAPE = 14.44 %），RFE-XGBoost 模型在籽粒灌浆阶段最有效（R² = 0.68，RMSE = 865.03 kg/ha，RMSLE = 0.12，MAPE = 8.88 %）。对于跨阶段建模，RFE-MLP 取得了显着的结果 （R² = 0.93，RMSE = 680.44 kg/ha，RMSLE = 0.16，MAPE = 12.12 %）。本研究证明了将特征选择方法与机器学习算法相结合以提高燕麦 AGB 估计准确性的有效性。多种种植模式的参与增强了我们研究结果的普遍性，有助于在未来的多元化种植系统中实时和快速监测作物生长。