Improving the estimation accuracy of alfalfa quality based on UAV hyperspectral imagery by using data enhancement and synergistic band selection strategies

Abstract

Accurate and timely assessment of alfalfa nutritional parameters is crucial for optimizing harvest management, maximizing yield, and ensuring high-quality forage in China’s Hexi Corridor, a key alfalfa-growing region. UAV-based hyperspectral remote sensing offers a nondestructive and efficient method for monitoring these parameters, providing high-resolution data and covering large areas efficiently. Previous studies have faced challenges related to the scarcity and imbalance of hyperspectral samples and the effective selection of spectral bands for evaluating crop nutrients. Additionally, the simultaneous evaluation of multiple nutrient parameters using a common set of spectral bands has rarely been reported. Least Absolute Shrinkage and Selection Operator (LASSO) is an important method for hyperspectral band selection, but its linear fitting process is challenged by the complex relationship between spectral reflectance and plant properties. In this study, we propose a new band selection strategy that identifies the most informative spectral bands and improves model performance by combining the strengths of both LASSO selection of bands and machine learning’s ability to fit complex relationships. To address the issue of imbalanced field samples, we generated high-quality synthetic data using the synthetic minority oversampling technique for regression with Gaussian noise (SMOGN) algorithm. Three machine learning models (ANN, RF, and SVM) were then employed to predict alfalfa nutritional parameters. Our findings show that the proposed synergistic band selection strategy significantly improves model performance, yielding a 14–25 % reduction in RMSE while requiring only 37–59 % of the original spectral bands. By integrating this band selection strategy with the SMOGN method, our optimal model for estimating alfalfa nutrient parameters achieved R² values of 0.92–0.95 and PRMSE values of 5.1–7.1 %. We observed the importance of the spectral regions around 730 nm and 960 nm for predicting alfalfa quality parameters. This finding suggests that existing satellite platforms such as Sentinel-2 and Landsat could improve the accuracy and efficiency of alfalfa quality monitoring by incorporating these specific spectral bands. Overall, our approach provides a robust and transferable framework for improving the accuracy and reliability of remote sensing-based crop quality monitoring, which is important for optimizing the spectral band configurations of future satellite sensors for precision agriculture.

Plant phenotyping relevance

UAVハイパースペクトル画像からアルファルファの栄養形質を推定するため、バンド選択、データ拡張、機械学習モデルを開発・評価しており、形質取得手法が研究の中心である。

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