Monitoring Maize Leaf Spot Disease Using Multi-Source UAV Imagery

Author:

Jia Xiao123,Yin Dameng23ORCID,Bai Yali23,Yu Xun23,Song Yang23ORCID,Cheng Minghan23,Liu Shuaibing23,Bai Yi2,Meng Lin23,Liu Yadong23,Liu Qian2,Nan Fei23,Nie Chenwei23,Shi Lei23,Dong Ping1ORCID,Guo Wei1ORCID,Jin Xiuliang23ORCID

Affiliation:

1. College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China

3. National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572025, China

Abstract

Maize leaf spot is a common disease that hampers the photosynthesis of maize by destroying the pigment structure of maize leaves, thus reducing the yield. Traditional disease monitoring is time-consuming and laborious. Therefore, a fast and effective method for maize leaf spot disease monitoring is needed to facilitate the efficient management of maize yield and safety. In this study, we adopted UAV multispectral and thermal remote sensing techniques to monitor two types of maize leaf spot diseases, i.e., southern leaf blight caused by Bipolaris maydis and Curvularia leaf spot caused by Curvularia lutana. Four state-of-the-art classifiers (back propagation neural network, random forest (RF), support vector machine, and extreme gradient boosting) were compared to establish an optimal classification model to monitor the incidence of these diseases. Recursive feature elimination (RFE) was employed to select features that are most effective in maize leaf spot disease identification in four stages (4, 12, 19, and 30 days after inoculation). The results showed that multispectral indices involving the red, red edge, and near-infrared bands were the most sensitive to maize leaf spot incidence. In addition, the two thermal features tested (i.e., canopy temperature and normalized canopy temperature) were both found to be important to identify maize leaf spot. Using features filtered with the RFE algorithm and the RF classifier, maize infected with leaf spot diseases were successfully distinguished from healthy maize after 19 days of inoculation, with precision >0.9 and recall >0.95. Nevertheless, the accuracy was much lower (precision = 0.4, recall = 0.53) when disease development was in the early stages. We anticipate that the monitoring of maize leaf spot disease at the early stages might benefit from using hyperspectral and oblique observations.

Funder

Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Agricultural Sciences

Nanfan special project, CAAS

National Natural Science Foundation of China

Research and application of key technologies of smart brain for farm decision-making platform

The Henan Provincial Science and Technology Major Project

The Joint Fund of Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province, China

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

Reference63 articles.

1. Global maize production; utilization, and consumption;Ranum;Ann. N. Y. Acad. Sci.,2014

2. Du Plessis, J. (2003). Maize Production, Department of Agriculture.

3. Climatic changes and the potential future importance of maize diseases: A short review;Juroszek;J. Plant Dis. Prot.,2013

4. A review on important maize diseases and their management in Nepal;Subedi;J. Maize Res. Dev.,2015

5. Monitoring plant diseases and pests through remote sensing technology: A review;Zhang;Comput. Electron. Agric.,2019

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3