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.
Highlights
Helminthosporium leaf blotch (HLB) is a serious disease of wheat cultivation, which causes yield reduction globally [1]
The unmanned aerial vehicle (UAV) data collection and concurrent ground investigation were conducted in two wheat fields
A convolutional neural network (CNN) was applied for HLB disease category classification
Summary
Helminthosporium leaf blotch (HLB) is a serious disease of wheat cultivation, which causes yield reduction globally [1]. Sci. 2019, 9, x; doi: according to the specific requirement of disease degrees, which may enhance the chemical effects In this case, effective detection of the disease could provide detailed support information for the increased use of chemicals has caused agronomical and environmental problems [4]. Effective detection of the disease could provide detailed support information for the increased use of chemicals has caused agronomical and environmental problems [4] Compared with satellite and aircraft remote sensing, unmanned this case, effective detection of the disease could provide detailed support information for the aerial vehicle (UAV) can fly at a low altitude and capture high resolution imagery [12], which would spraying machines. Thewith rest of this methods, paper is organized as followed: Section introduces the collection and the processing methods; Section 3 demonstrates the experimental results of our methods and the
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