CatBoost algorithm for estimating maize above-ground biomass using unmanned aerial vehicle-based multi-source sensor data and SPAD values

Abstract

The rapid and accurate estimation of maize above-ground biomass (AGB) is pivotal for precise agricultural management. The rapid evolution of unmanned aerial vehicles (UAVs) and sensor technology has introduced a novel method for obtaining AGB information. Nevertheless, individual sensors may lack comprehensive data, leading to reduced AGB estimation accuracy in certain scenarios. This study collected UAV multi-spectral (MS) and thermal infrared (TIR) data, alongside soil and plant analyzer development (SPAD) values, from maize across multiple growth stages (jointing, trumpet, and big trumpet) during 2022 and 2023. Diverse data fusion programs were devised to explore the potential of combining multi-source sensor data with SPAD values to estimate AGB. The efficacy of CatBoost was evaluated and benchmarked against Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms. For the entire growth, findings reveal that the fusion of multi-source sensor data (MS + TIR) can mitigate the data insufficiency in single-sensor estimations. The resulting R 2 values range from 0.608 to 0.817. Optimal estimation outcomes were achieved by the fusion of multi-source sensor data with SPAD values (MS + TIR + SPAD), yielding R 2 values ranging from 0.685 to 0.872. For a single growth stage, there are variations in the estimation accuracy across different growth stages. From the jointing stage to the big trumpet stage, the estimation accuracy consistently increases, with the highest accuracy observed during the big trumpet stage, with R 2 ranging from 0.721 to 0.901. Additionally, in alignment with the results for the entire growth stage, the fusion of multi-source sensor data with SPAD values still yields the highest estimation accuracy during different growth stages. In a comparison of different machine learning algorithms, for both the entire growth stage and single growth stages, SVR, RFR, and CatBoost achieved R 2 values ranging from 0.305 to 0.824, 0.368 to 0.881, and 0.451 to 0.901, respectively. Notably, the CatBoost algorithm exhibited heightened estimation accuracy. The fusion of multi-source sensor data with SPAD values combined with the CatBoost algorithm results in accurate and reliable maize AGB estimation accuracy. This high-throughput approach to crop phenotyping is characterized by speed and accuracy and serves as a valuable reference for rapidly acquiring AGB information in this geographical region.

Plant phenotyping relevance

UAVマルチスペクトル・熱赤外センサーとSPAD値を融合し、機械学習でトウモロコシの地上部バイオマスを推定する手法を比較・検証しており、植物表現型取得が研究の中心である。

Code and data availability

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