Estimating canopy chlorophyll content of powdery mildew stressed winter wheat by different spatial resolutions of UAV-imagery

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Yang Liu , Mingjia Liu , Guohui Liu , Hong Sun , Lulu An , Ruomei Zhao , Weijie Tang , Fangkui Zhao , Xiaojing Yan , Yuntao Ma , Minzan Li
{"title":"Estimating canopy chlorophyll content of powdery mildew stressed winter wheat by different spatial resolutions of UAV-imagery","authors":"Yang Liu ,&nbsp;Mingjia Liu ,&nbsp;Guohui Liu ,&nbsp;Hong Sun ,&nbsp;Lulu An ,&nbsp;Ruomei Zhao ,&nbsp;Weijie Tang ,&nbsp;Fangkui Zhao ,&nbsp;Xiaojing Yan ,&nbsp;Yuntao Ma ,&nbsp;Minzan Li","doi":"10.1016/j.compag.2024.109621","DOIUrl":null,"url":null,"abstract":"<div><div>The wheat powdery mildew (WPM) always alters the pigment and structure of the leaf and canopy, disrupting crop growth. A challenge on the WPM monitoring is the limited capability of unmanned aerial vehicle (UAV)-based canopy images to directly indicate complex infection symptoms. However, the WPM infection markedly changed canopy chlorophyll content (CCC), which encompassed both leaf and canopy attributes, and this change was relatively easy to capture by UAV remote sensing. Thus, this study aimed to estimate CCC to indirectly explore WPM using different scales of UAV image features. UAV-based winter wheat canopy images were acquired continuously in the field during the early, middle, and late infection stages after being artificially inoculated with fungal pathogens at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China, in 2022. The study evaluated the potential of spectral (Spe) and textural (Tex) features and their combination to estimate CCC and characterize WPM dynamic. Considering the impacts of spatial scales, the selected Spe and Tex textures were calculated from images with 1, 2, 5, 10, 15, and 20 cm spatial resolution. The changes in different types of features under WPM stress and their response to CCC were analyzed. Three regression methods, including extreme gradient boosting regression (XGBR), multilayer perceptron regression (MLPR), and partial least squares regression (PLSR) were used to estimate CCC based on the acquired sensitive features and track the infection status. Results showed that the image spatial resolution barely affected the Spe performance while notably affecting the Tex performance. The performance of estimating CCC under WPM stress was superior for Tex (ranging from 1 to 20 cm spatial resolution imagery) compared to Spe features. The best modeling result was the combination of Spe with Tex features from 1 and 10 cm (R<sup>2</sup> = 0.82, RMSE = 28.49 mg/L, NRMSE = 12.38 %), which could be related to information captured from different viewpoints. Although finer spatial resolution was advantageous for capturing the complex symptoms caused by WPM, it increased the burden on UAV missions. UAV multispectral imagery with the 10 cm spatial resolution using XGBR (R<sup>2</sup> = 0.74, RMSE = 33.48 mg/L, NRMSE = 14.55 %) might be used as an optimization scheme for estimating CCC and exploring WPM stress, as it decreased the cost associated with data processing and time in the actual operation. This study indirectly characterizes the condition of WPM infection by estimating CCC, which provides promising and valuable insights for disease management and control in the field.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109621"},"PeriodicalIF":7.7000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169924010123","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

The wheat powdery mildew (WPM) always alters the pigment and structure of the leaf and canopy, disrupting crop growth. A challenge on the WPM monitoring is the limited capability of unmanned aerial vehicle (UAV)-based canopy images to directly indicate complex infection symptoms. However, the WPM infection markedly changed canopy chlorophyll content (CCC), which encompassed both leaf and canopy attributes, and this change was relatively easy to capture by UAV remote sensing. Thus, this study aimed to estimate CCC to indirectly explore WPM using different scales of UAV image features. UAV-based winter wheat canopy images were acquired continuously in the field during the early, middle, and late infection stages after being artificially inoculated with fungal pathogens at the Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Xinxiang, China, in 2022. The study evaluated the potential of spectral (Spe) and textural (Tex) features and their combination to estimate CCC and characterize WPM dynamic. Considering the impacts of spatial scales, the selected Spe and Tex textures were calculated from images with 1, 2, 5, 10, 15, and 20 cm spatial resolution. The changes in different types of features under WPM stress and their response to CCC were analyzed. Three regression methods, including extreme gradient boosting regression (XGBR), multilayer perceptron regression (MLPR), and partial least squares regression (PLSR) were used to estimate CCC based on the acquired sensitive features and track the infection status. Results showed that the image spatial resolution barely affected the Spe performance while notably affecting the Tex performance. The performance of estimating CCC under WPM stress was superior for Tex (ranging from 1 to 20 cm spatial resolution imagery) compared to Spe features. The best modeling result was the combination of Spe with Tex features from 1 and 10 cm (R2 = 0.82, RMSE = 28.49 mg/L, NRMSE = 12.38 %), which could be related to information captured from different viewpoints. Although finer spatial resolution was advantageous for capturing the complex symptoms caused by WPM, it increased the burden on UAV missions. UAV multispectral imagery with the 10 cm spatial resolution using XGBR (R2 = 0.74, RMSE = 33.48 mg/L, NRMSE = 14.55 %) might be used as an optimization scheme for estimating CCC and exploring WPM stress, as it decreased the cost associated with data processing and time in the actual operation. This study indirectly characterizes the condition of WPM infection by estimating CCC, which provides promising and valuable insights for disease management and control in the field.
通过不同空间分辨率的无人机成像估算白粉病胁迫冬小麦冠层叶绿素含量
小麦白粉病(WPM)总是会改变叶片和冠层的色素和结构,干扰作物生长。基于无人机(UAV)的冠层图像直接显示复杂感染症状的能力有限,这是 WPM 监测面临的一个挑战。然而,WPM 感染明显改变了包括叶片和冠层属性在内的冠层叶绿素含量(CCC),而这种变化相对容易被无人机遥感捕捉。因此,本研究旨在利用不同尺度的无人机图像特征估算 CCC,以间接探索 WPM。2022 年,在中国农业科学院新乡植物保护研究所,基于无人机的冬小麦冠层图像是在人工接种真菌病原体后的早期、中期和晚期感染阶段在田间连续获取的。该研究评估了光谱(Spe)和纹理(Tex)特征及其组合在估算 CCC 和描述 WPM 动态特征方面的潜力。考虑到空间尺度的影响,所选的 Spe 和 Tex 纹理是通过 1、2、5、10、15 和 20 厘米空间分辨率的图像计算得出的。分析了 WPM 压力下不同类型地物的变化及其对 CCC 的响应。使用了三种回归方法,包括极梯度提升回归(XGBR)、多层感知器回归(MLPR)和偏最小二乘回归(PLSR),根据获得的敏感特征估计 CCC 并跟踪感染状态。结果表明,图像空间分辨率对 Spe 性能几乎没有影响,但对 Tex 性能有显著影响。与 Spe 特征相比,Tex(空间分辨率从 1 厘米到 20 厘米不等)在 WPM 压力下估计 CCC 的性能更优。最佳建模结果是将 Spe 与 1 厘米和 10 厘米的 Tex 特征相结合(R2 = 0.82,RMSE = 28.49 mg/L,NRMSE = 12.38 %),这可能与从不同视角获取的信息有关。虽然更精细的空间分辨率有利于捕捉水稻病虫害造成的复杂症状,但却增加了无人机任务的负担。利用 XGBR(R2 = 0.74,RMSE = 33.48 mg/L,NRMSE = 14.55 %)进行空间分辨率为 10 cm 的无人机多光谱成像可作为估算 CCC 和探索 WPM 压力的优化方案,因为它降低了与数据处理相关的成本和实际操作中的时间。本研究通过估算 CCC 间接描述了 WPM 感染的状况,为田间病害管理和控制提供了有前景、有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信