Exploring the transferability of wheat nitrogen status estimation with multisource data and Evolutionary Algorithm-Deep Learning (EA-DL) framework

水准点(测量) 氮气 氮缺乏 随机森林 机器学习 超参数 计算机科学 深度学习 人工智能 可转让性 环境科学 农学 生物 地理 化学 地图学 罗伊特 有机化学
作者
Guojie Ruan,Urs Schmidhalter,Fei Yuan,Davide Cammarano,Xiaojun Liu,Yongchao Tian,Senthold Asseng,Weixing Cao,Qiang Cao
出处
期刊:European Journal of Agronomy [Elsevier]
卷期号:143: 126727-126727 被引量:9
标识
DOI:10.1016/j.eja.2022.126727
摘要

Accurate and transferable wheat nitrogen status estimation is very important to plant phenotyping and smart agricultural management. The goal of this study was to establish a wheat nitrogen status estimation model across all growth stages by combining proximal sensing and meteorological data. From 2010–2020, nine multi-nitrogen rates field trials were conducted at five sites involving different wheat varieties. Proximal sensing data were acquired from a Crop Circle sensor at key growth stages and meteorological data were aggregated from planting to the corresponding sensing date. Deep neural network (DNN) and long short-term memory (LSTM) were adopted to estimate above-ground biomass, plant nitrogen uptake, plant nitrogen concentration, and the nitrogen nutrition index. Random forest (RF) was used as a benchmark regression model. Multi-task learning (MTL) based on DNN was conducted to estimate the four nitrogen indicators simultaneously. A genetic algorithm (GA) was tested to optimize the hyperparameters, connection weights, and loss function weights (for MTL) of neural networks separately. The results revealed that DNN (R2 =0.83–0.96) and MTL (R2 =0.81–0.96) achieved an overall comparable high accuracy with RF (R2 =0.83–0.97), whereas LSTM (R2 =0.76–0.93) did not improve the nitrogen status estimation in our dataset. This study presented a concise and efficient framework dedicated to exploring the transferability of phenotypic predictions and provided insights into understanding crop growth and nitrogen dynamics in response to environmental conditions.
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