| 引用本文: | 韩红亮,石浩磊,曹红霞,等.基于无人机遥感和可解释机器学习的棉花地上生物量监测[J].灌溉排水学报,2026,45(8):34-42. |
| Han Hongliang,Shi Haolei,Cao Hongxia,et al.基于无人机遥感和可解释机器学习的棉花地上生物量监测[J].灌溉排水学报,2026,45(8):34-42. |
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| 摘要: |
| 【目的】利用无人机遥感技术实现棉花地上生物量的精准监测。【方法】基于2021—2022年田间试验，利用无人机搭载的多光谱和RGB相机获取棉花蕾期和花期的棉田冠层影像，从影像中提取与棉花地上生物量（AGB）相关的颜色指数和植被指数，采用极限树（Extra Trees）、K最近邻（KNN）、支持向量机（SVM）3种机器学习模型构建了基于全特征参数的棉花AGB监测模型，并使用Shapley additive explanation（SHAP）事后检验法进行模型的可解释性分析，提出了基于SHAP的事后检验特征选择方法，并与常见的事前检验方法进行比较。【结果】Extra Trees模型是基于所有特征参数下的最优机器学习模型，最优模型测试集的决定系数（R2）、平均绝对值误差（MAE）和均方根误差（RMSE）分别为0.84、1.22 t/hm2、1.48 t/hm2；归一化差植被指数（NDVI），红绿比率指数（RGRI）和超绿指数（EXG）是对各模型预测精度贡献较强的特征指数；Extra Trees模型更善于挖掘植被指数中的信息，而KNN和SVM模型在对棉花AGB模型构建过程中更依赖颜色指数；相较于事前检验，采用事后检验进行特征选择可减少最优模型的特征数，提升最优模型的模拟精度。事后检验中，最优模型为KNN模型，最优模型的测试集R2、MAE、RMSE分别为0.85、1.07 t/hm2、1.22 t/hm2。【结论】对基于无人机遥感技术和机器学习模型结合所构建的模型进行事后检验可以更准确地监测棉花AGB，为棉花科学田间管理提供技术支持。 |
| 关键词: 棉花；无人机遥感；机器学习；事后检验；地上生物量；可解释性 |
| DOI：10.13522/j.cnki.ggps.2026015 |
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| Estimating above-ground biomass of cotton using UAV remote sensing and interpretable machine learning with post-hoc feature selection |
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Han Hongliang, Shi Haolei, Cao Hongxia, Wang Xuemei
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1. School of Mechanical and Electrical Engineering, Shaanxi A&F Technology University, Yangling 712100, China;
2. Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China; 3. School of Water Conservancy Engineering, Shaanxi A&F Technology University, Yangling 712100, China
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| Abstract: |
| 【Objective】Above-ground biomass (AGB) is an important indicator for evaluating healthy crop growth, and its traditional measurement was destructive and labour-intensive. The development of unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) has enabled quick and accurate estimation of AGB. This study proposes interpretable ML models for cotton AGB estimation using UAV-acquired multispectral and RGB data.【Method】The field experiment was conducted from 2021 to 2022 during the cotton growing season. UAV equipped with multispectral and RGB sensors captured canopy imagery of the cotton field during the bud and flowering stages, from which a series of AGB-correlated colour and vegetation indices were extracted. Three ML algorithms, including Extra Trees, k-nearest neighbours (KNN), and support vector machine (SVM), were used to build AGB estimation models based on full-feature datasets. Shapley Additive Explanations (SHAP) were used to interpret the internal mechanisms of the constructed ML models. We proposed a SHAP-based post-hoc feature selection method and compared its performance with traditional priori feature selection approaches. Datasets from 2021 and 2022, in their independence or combination, were divided into training and test sets at a 7∶3 ratio. Ten-fold cross-validation was implemented during model training to alleviate overfitting and improve model robustness.【Result】Among all compared models, the Extra Trees algorithm performed optimally, with a coefficient of determination (R2) of 0.84, mean absolute error (MAE) of 1.22 t/hm2 and root mean square error (RMSE) of 1.48 t/hm2 for the test set. The combined 2021–2022 dataset required more feature indices to achieve optimal estimation accuracy than single-year dataset. SHAP interpretation effectively clarified the complex relationships between feature variables, AGB values, and ML predictions; model estimation accuracy depended on both data quality and the matching degree between feature selection schemes and algorithm characteristics. The vegetation index (NDVI) and colour indices (RGRI and EXG) were the dominant predictive features in all models. All three algorithms achieved reliable AGB estimation yet exhibited distinct feature utilization preferences. The Extra Trees model fully exploited vegetation index information, while KNN and SVM models relied more on colour indices. Compared with traditional priori feature selection methods, the proposed method effectively reduced the number of optimal feature variables and significantly improved estimation accuracy, with the SVM algorithm requiring the fewest input features. The optimal model varied with dataset types: the KNN model achieved the highest accuracy for the 2021 dataset (R2=0.85, MAE=1.07 t/hm2, RMSE=1.22 t/hm2); the Extra Trees model performed best for the 2022 dataset (R2=0.85, MAE=1.25 t/hm2, RMSE=1.63 t/hm2); the SVM model yielded the optimal performance for the combined 2021–2022 dataset (R2=0.80, MAE=1.48 t/hm2, RMSE=1.82 t/hm2).【Conclusion】Integrating the proposed post-hoc feature selection strategy, UAV remote sensing data, and interpretable ML algorithms can achieve accurate, non-destructive monitoring of cotton AGB in the field. |
| Key words: cotton; UAV remote sensing; machine learning; after-test; above-ground biomass; explainability |
