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Appl. Sci. 2019, 9(3), 558; https://doi.org/10.3390/app9030558

Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery

1
College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2
National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Wushan Road, Guangzhou 510642, China
3
College of Electronic Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
4
College of Agriculture, South China Agricultural University, Wushan Road, Guangzhou 510642, China
5
Engineering Fundamental Teaching and Training Center, South China Agricultural University, Wushan Road, Guangzhou 510642, China
6
USDA, Agricultural Research Service, Water Management Research Unit, 2150 Centre Ave., Building D, Suite 320, Fort Collins, CO 80526-8119, USA
*
Authors to whom correspondence should be addressed.
Received: 31 December 2018 / Revised: 31 January 2019 / Accepted: 4 February 2019 / Published: 8 February 2019
(This article belongs to the Section Computing and Artificial Intelligence)
Full-Text   |   PDF [5596 KB, uploaded 14 February 2019]   |  

Abstract

Helminthosporium leaf blotch (HLB) is a serious disease of wheat causing yield reduction globally. Usually, HLB disease is controlled by uniform chemical spraying, which is adopted by most farmers. However, increased use of chemical controls have caused agronomic and environmental problems. To solve these problems, an accurate spraying system must be applied. In this case, the disease detection over the whole field can provide decision support information for the spraying machines. The objective of this paper is to evaluate the potential of unmanned aerial vehicle (UAV) remote sensing for HLB detection. In this work, the UAV imagery acquisition and ground investigation were conducted in Central China on April 22th, 2017. Four disease categories (normal, light, medium, and heavy) were established based on different severity degrees. A convolutional neural network (CNN) was proposed for HLB disease classification. The experiments on data preprocessing, classification, and hyper-parameters tuning were conducted. The overall accuracy and standard error of the CNN method was 91.43% and 0.83%, which outperformed other methods in terms of accuracy and stabilization. Especially for the detection of the diseased samples, the CNN method significantly outperformed others. Experimental results showed that the HLB infected areas and healthy areas can be precisely discriminated based on UAV remote sensing data, indicating that UAV remote sensing can be proposed as an efficient tool for HLB disease detection. View Full-Text
Keywords: UAV imagery; remote sensing; Helminthosporium leaf blotch; convolution neural network; SVM UAV imagery; remote sensing; Helminthosporium leaf blotch; convolution neural network; SVM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Huang, H.; Deng, J.; Lan, Y.; Yang, A.; Zhang, L.; Wen, S.; Zhang, H.; Zhang, Y.; Deng, Y. Detection of Helminthosporium Leaf Blotch Disease Based on UAV Imagery. Appl. Sci. 2019, 9, 558.

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