Mapping rapeseed ( Brassica napus L. ) aboveground biomass in different periods using optical and phenotypic metrics derived from UAV hyperspectral and RGB imagery.

Abstract

Aboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass. However, the saturation effect makes it challenging for spectral indices to accurately reflect crop changes at higher biomass levels. It is well established that rapeseed biomass during different growth stages is closely related to phenotypic traits. This study aims to explore the potential of using optical and phenotypic metrics to estimate rapeseed AGB. Vegetation indices (VI), texture features (TF), and structural features (SF) were extracted from UAV hyperspectral and ultra-high-resolution RGB images to assess their correlation with rapeseed biomass at different growth stages. Deep neural network (DNN), random forest (RF), and support vector regression (SVR) were employed to estimate rapeseed AGB. We compared the accuracy of various feature combinations and evaluated model performance at different growth stages. The results indicated strong correlations between rapeseed AGB at the three growth stages and the corresponding indices. The estimation model incorporating VI, TF, and SF showed higher accuracy in estimating rapeseed AGB compared to models using individual feature sets. Furthermore, the DNN model (R 2 = 0.878, RMSE = 447.02 kg/ha) with the combined features outperformed both the RF (R 2 = 0.812, RMSE = 530.15 kg/ha) and SVR (R 2 = 0.781, RMSE = 563.24 kg/ha) models. Among the growth stages, the bolting stage yielded slightly higher estimation accuracy than the seedling and early blossoming stages. The optimal model combined DNN with VI, TF, and SF features. These findings demonstrate that integrating hyperspectral and RGB data with advanced artificial intelligence models, particularly DNN, provides an effective approach for estimating rapeseed AGB.

Plant phenotyping relevance

UAVのハイパースペクトル・RGB画像から構造的および光学的特徴を抽出し、成長段階別のナタネ地上部バイオマスを推定する画像解析・計算手法が研究の中心であるため。

Code and data availability

The supplied article blocks describe UAV hyperspectral/RGB data collection, AGB/PH measurements, and DNN/RF/SVR modeling for rapeseed biomass estimation, but contain no data availability statement, no public dataset deposit, no author code repository, and no supplement (pmc-prop-has-supplement no). The only URL present

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