Monitoring aboveground organs biomass of wheat and maize: A novel model combining ensemble learning and allometric theory

IF 4.5 1区 农林科学 Q1 AGRONOMY European Journal of Agronomy Pub Date : 2024-09-11 DOI:10.1016/j.eja.2024.127338
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Abstract

Accurate monitoring of crop organ biomass facilitates optimizing agronomic strategies to maximize yield or economic benefit. Unmanned aerial vehicle (UAV) is extensively employed for aboveground biomass (AGB) monitoring at the farm scale, but previous studies have mostly concentrated on total AGB rather than individual organ biomass. Furthermore, film-mulched crops, a widely used cropping pattern in northwest China, have received less attention for AGB monitoring. We aim to develop a novel model to precisely estimate the AGB of leaf (AGBLeaf), stem (AGBStem), and reproductive organs (AGBR) by UAV for film-mulched wheat and maize. The maize-wheat rotation field experiments with treatments of five nitrogen application amounts and three planting densities were conducted from 2021 to 2023, respectively. Firstly, we constructed allometric models at jointing, heading (tasseling), and grain filling stages by ground sampling data in 2021–2022. Next, the input feature set was obtained by feature selection methods (Lasso and Boruta) using UAV image data, and three traditional methods (partial least squares, ridge regression, and support vector machine) and three ensemble learning models (random forest, extreme gradient boosting, and local cascade ensemble (LCE)) were trained for AGBLeaf inversion based on the physically-based PROSAIL model simulation dataset. Finally, the optimal AGBLeaf inversion hybrid model was coupled with the allometric model to estimate the AGBStem and AGBR in 2022–2023. The results indicated that both wheat and maize organ biomass conformed to the allometric pattern. While feature selection helped reduce computation and complexity, but didn’t improve monitoring accuracy. The normalized root mean square error (NRMSE) of the optimal hybrid model (PROSAIL + Boruta + LCE) on the measured wheat and maize AGBLeaf datasets were 12.72 %–24.93 % and 19.65 %–25.16 %, respectively. After coupling the allometric model, the coefficient of determination (R2) of wheat and maize AGBStem were 0.64–0.85 and 0.63–0.68, and the NRMSE were 15.05 %–25.28 % and 24.10 %–27.06 %, respectively; and the corresponding R2 of AGBR was 0.67–0.76 and 0.72, and the NRMSE were 16.81 %–22.12 % and 21.66 %, for wheat and maize, respectively. Overall, the novel model performed well in film-mulched wheat and maize, providing a cost-effective approach for organ biomass monitoring. In the future, further validation of the model’s transferability is necessary to increase the potential for generalization in production practice.

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监测小麦和玉米的地上部分生物量:结合集合学习和异速理论的新型模型
对作物器官生物量的精确监测有助于优化农艺策略,从而实现产量或经济效益的最大化。无人飞行器(UAV)被广泛应用于农田尺度的地上生物量(AGB)监测,但以往的研究大多集中于AGB总量而非单个器官生物量。此外,覆膜作物是中国西北地区广泛使用的一种种植模式,但其在 AGB 监测方面受到的关注较少。我们旨在开发一种新型模型,利用无人机精确估算覆膜小麦和玉米的叶片(AGBLeaf)、茎(AGBtem)和生殖器官(AGBR)的生物量。玉米-小麦轮作田间试验分别于 2021 年至 2023 年进行,处理为五种施氮量和三种种植密度。首先,我们通过 2021 年至 2022 年的地面取样数据构建了拔节期、抽穗期和籽粒灌浆期的异速模型。然后,利用无人机图像数据,通过特征选择方法(Lasso 和 Boruta)获得输入特征集,并基于基于物理的 PROSAIL 模型模拟数据集,训练了三种传统方法(偏最小二乘法、脊回归和支持向量机)和三种集合学习模型(随机森林、极端梯度提升和局部级联集合(LCE)),用于 AGBLeaf 反演。最后,将最优 AGBLeaf 反演混合模型与异速模型相结合,估算出 2022-2023 年的 AGBStem 和 AGBR。结果表明,小麦和玉米的器官生物量都符合异速模式。虽然特征选择有助于降低计算量和复杂性,但并未提高监测精度。在测量的小麦和玉米 AGBLeaf 数据集上,最优混合模型(PROSAIL + Boruta + LCE)的归一化均方根误差(NRMSE)分别为 12.72 %-24.93 % 和 19.65 %-25.16 %。耦合异速模型后,小麦和玉米 AGBStem 的判定系数(R2)分别为 0.64-0.85 和 0.63-0.68,无显著性差异(NRMSE)分别为 15.05 %-25.28 % 和 24.10 %-27.06 %;小麦和玉米 AGBR 的相应 R2 分别为 0.67-0.76 和 0.72,无显著性差异(NRMSE)分别为 16.81 %-22.12 % 和 21.66 %。总体而言,新型模型在薄膜覆盖的小麦和玉米中表现良好,为器官生物量监测提供了一种经济有效的方法。今后,有必要进一步验证该模型的可移植性,以提高其在生产实践中的推广潜力。
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来源期刊
European Journal of Agronomy
European Journal of Agronomy 农林科学-农艺学
CiteScore
8.30
自引率
7.70%
发文量
187
审稿时长
4.5 months
期刊介绍: The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics: crop physiology crop production and management including irrigation, fertilization and soil management agroclimatology and modelling plant-soil relationships crop quality and post-harvest physiology farming and cropping systems agroecosystems and the environment crop-weed interactions and management organic farming horticultural crops papers from the European Society for Agronomy bi-annual meetings In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.
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