开发基于主动冠层传感器和机器学习的新型水稻当季氮素状况诊断和推荐策略

IF 5.6 1区 农林科学 Q1 AGRONOMY
Junjun Lu , Erfu Dai , Yuxin Miao , Krzysztof Kusnierek
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引用次数: 0

摘要

背景传统的用于确定氮营养指数(NNI)的临界氮(N)稀释曲线(CNDC)构建方法在不同农田条件下对作物当季氮诊断和推荐具有局限性。方法在中国东北七星农场的 14 个地点对 3 个粳稻品种进行了为期 5 年的小区和农场试验。采用随机森林(RF)和扩展梯度提升(XGBoost)回归两种机器学习(ML)方法融合多源数据,包括来自便携式主动冠层传感器 RapidSCAN 的基因型、环境、管理、生长阶段、归一化差异植被指数(NDVI)和归一化差异红边(NDRE)。利用 NDVI 和 NDRE 代替破坏性取样测量的地上生物量 (AGB),建立了 CNDC。结果基于 NDVI 或 NDRE 的新 CNDC 使用 ML 模型与环境和农艺因素相结合,解释了评估数据集中 94-96% 的 Nc 变异性。与评估数据集中使用 NDVI 或 NDRE 的简单回归模型相比,基于 ML 的 PNC 和 NNI 预测模型对变异的解释率分别提高了 85% 和 21-36%。使用基于 NDVI 和 NDRE 的 CNDC 以及射频模型和多源数据融合预测的植物氮浓度 (PNC) 的新季节氮诊断策略比直接 NNI 预测略胜一筹,对 NNI 变异的解释率高 7%,在所有评估实验中的氮诊断区域一致性达到 89%。将这一新的氮管理策略整合到水稻精确管理系统(ML_PRM)中,与农民实践(FP)相比,产量、氮利用效率(NUE)和经济效益分别提高了 7%-15%、11%-71% 和 4%-16%(161-596 美元/公顷-1),与农场条件下水稻氮过剩状态下的区域最佳水稻管理(RORM)相比,氮利用效率提高了 11%-26%,经济效益提高了 8-97 美元/公顷-1。基于主动冠层传感器和多源数据融合的 ML 模型预测的 CNDC 和 PNC 可改善水稻当季氮状况诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing a new active canopy sensor- and machine learning-based in-season rice nitrogen status diagnosis and recommendation strategy

Context

Traditional critical nitrogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determination has limitations for in-season crop N diagnosis and recommendation under diverse on-farm conditions.

Objectives

This study was conducted to (i) develop a new rice (Oryza sativa L.) critical N concentration (Nc) determination approach using vegetation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Nc determination approach and evaluate its reliability and practicality.

Methods

Five years of plot and on-farm experiments involving three japonica rice varieties were conducted at fourteen sites in Qixing Farm, Northeast China. Two machine learning (ML) methods, random forest (RF) and extended gradient boosting (XGBoost) regression, were used to fuse multi-source data including genotype, environment, management, growth stage, normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) from portable active canopy sensor RapidSCAN. The CNDC was established using NDVI and NDRE instead of aboveground biomass (AGB) measured by destructive sampling. A new in-season N diagnosis and recommendation strategy was further developed using direct and indirect NNI prediction using multi-source data fusion and ML models.

Results

The new CNDC based on NDVI or NDRE explained 94−96 % of Nc variability in the evaluation dataset when it was coupled with environmental and agronomic factors using ML models. The ML-based PNC and NNI prediction models explained 85 % and 21–36 % more variability over simple regression models using NDVI or NDRE in the evaluation dataset, respectively. The new in-season N diagnosis strategy using the NDVI and NDRE-based CNDCs and plant N concentration (PNC) predicted with RF model and multi-source data fusion performed slightly better than direct NNI prediction, explaining 7 % more of NNI variability and achieving 89 % of the areal agreement for N diagnosis across all evaluation experiments. Integrating this new N management strategy into the precision rice management system (as ML_PRM) increased yield, N use efficiency (NUE) and economic benefits over farmer’s practice (FP) by 7–15 %, 11–71 % and 4–16 % (161–596 $ ha−1), respectively, and increased NUE by 11–26 % and economic benefits by 8–97 $ ha−1 than regional optimum rice management (RORM) under rice N surplus status under on-farm conditions.

Conclusions

In-season rice N status diagnosis can be improved using NDVI- and NDRE-based CNDC and PNC predicted by ML modeling with multi-source data fusion.

Implications

The active canopy sensor- and ML-based in-season N diagnosis and management strategy is more practical for applications under diverse on-farm conditions and has the potential to improve rice yield and ecological and economic benefits.

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来源期刊
Field Crops Research
Field Crops Research 农林科学-农艺学
CiteScore
9.60
自引率
12.10%
发文量
307
审稿时长
46 days
期刊介绍: Field Crops Research is an international journal publishing scientific articles on: √ experimental and modelling research at field, farm and landscape levels on temperate and tropical crops and cropping systems, with a focus on crop ecology and physiology, agronomy, and plant genetics and breeding.
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