Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Huiqin Ma,Qiong Zheng,Chenghai Yang,Chao Ruan,Yuanshu Jing,Wenjiang Huang,Yingying Dong,Huichun Ye,Liangxiu Han,Linyi Liu,Yue Shi

doi:10.3390/rs11070846

Abstract

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.

Highlights

Powdery mildew (Blumeria graminis), a crop disease, and aphids (Sitobion avenae), an insect pest, are both destructive and occur almost each year in major winter wheat growing regions in China [1,2]
The results proved that the bi-temporal growth indices and environmental factors-based synthetic minority oversampling technique (SMOTE)-back propagation neural network (BPNN) were an effective approach for automatic discrimination among healthy wheat, powdery mildew infected wheat and aphid damaged wheat
Our goals were to improve the accuracy of the discrimination models through the integration of multi-source and multi-temporal remotely sensed data, providing a detailed spatial distribution of crop diseases and pests to meet the current needs of precision agriculture

Summary

Introduction

Powdery mildew (Blumeria graminis), a crop disease, and aphids (Sitobion avenae), an insect pest, are both destructive and occur almost each year in major winter wheat growing regions in China [1,2]. Based on an advanced hyperspectral analysis technique, continuous wavelet analysis, Shi et al [4] determined the most sensitive wavelet features (WFs) for the identification of yellow rust and powdery mildew in winter wheat These hyperspectral based studies gave more detailed information and demonstrated the effectiveness of hyperspectral sensors in detecting and discriminating crop diseases and pests, its high hardware and computational costs restrict its application over large areas [12,13]. By developing a set of normalized bi-temporal vegetation indices using PlanetScope image datasets at a 3-m spatial resolution, Shi et al [8] mapped and evaluated the damage caused by rice dwarf, rice blast, and glume blight at fine spatial scales These results motivate us to attempt to discriminate wheat powdery mildew and aphid using multispectral satellite imagery

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