{"title":"Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images","authors":"Razieh Barzin, G. Bora","doi":"10.13031/TRANS.14305","DOIUrl":null,"url":null,"abstract":"HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions of the ASABE","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.13031/TRANS.14305","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 5
Abstract
HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.
利用无人机获取的各种植被指数估算了玉米叶片氮含量。比较了八种机器学习方法,找到了最准确的氮估计模型。确定了对叶片氮估算影响最大的植被指数。氮(N)是健康植物最关键的成分。它对光合作用和植物繁殖有重要影响。由于多光谱传感技术和机器学习技术的最新发展,植物的理化特征如叶片氮含量可以在空间和时间上进行估计。本研究的目的是利用光谱数据进行玉米叶片氮估计,比较不同的ML模型,找到最适合的模型。此外,对植被指数(VIs)和光谱波长的性能进行了单独和集体比较,以确定VIs组合是否比原始光谱数据显著改善了结果。本研究在密西西比州立大学玉米田进行,将玉米田分为16块,施氮量分别为0、90、180和270 kg hm -1。从多光谱图像中去除裸露土壤像元,并基于蓝、绿、红、红边和近红外5个光谱波段计算26 VIs。利用安装在无人机上的红边多光谱传感器获取的26个VIs和5个光谱波段进行分析,建立了玉米叶片%N估算的ML模型。各模型输入变量对叶绿素和氮含量影响最大,且与叶片氮含量相关性较高。八种机器学习算法(随机森林、梯度增强、支持向量机、多层感知器、脊回归、lasso回归和弹性网)应用于三种不同类别的变量。结果表明,梯度增强和随机森林是估计叶片%N的最佳拟合模型,对于不同类别的变量,其决定系数约为80%。此外,在光谱带中加入VIs改善了结果。与其他预测玉米叶片%N含量的变量相比,SCCCI、NDRE和红边组合的决定系数(R2)最大。关键词:玉米,梯度增强,机器学习,多光谱图像,氮估计,随机森林,无人机,植被指数
期刊介绍:
This peer-reviewed journal publishes research that advances the engineering of agricultural, food, and biological systems. Submissions must include original data, analysis or design, or synthesis of existing information; research information for the improvement of education, design, construction, or manufacturing practice; or significant and convincing evidence that confirms and strengthens the findings of others or that revises ideas or challenges accepted theory.