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
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Cited by
1 articles.
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