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

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.

Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

“…The prediction method of crop disease combining multiple factors usually considers four key points. The susceptible growth stages of the crop were mostly based on the records of agrometeorological stations or field investigation to choose the suitable growth stages of the crop as the study time [14,15]. Growth conditions: collecting the remote sensing data during the selected stages.…”

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

“…This index was based on the sensitive bands B4 (Red), B5 (Re1), and B7 (Re3) and validated with an overall identification accuracy of 85.2% using the optimal threshold. SVM was deployed in [93] for disease detection in winter wheat. The proposed approach was based on growth indices and environmental factors calculated from Landsat-8 images.…”

The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

“…In response, the colour and temperature of the canopy may change and result in variation in canopy reflectance characteristics, which can be measured by RS [98,99]. Existing RS approaches can discriminate various diseases and pests, evaluate their infection severities and map their spatial distribution at various scales [47][48][49][50]. For example, using Landsat images, it was possible to map the severe infestation of the take-all disease in wheat [100].…”

“…For example, using Landsat images, it was possible to map the severe infestation of the take-all disease in wheat [100]. Ma et al [50] developed a multi-temporal satellite data-based early detection method for regional mapping of powdery mildew disease. In addition, remote sensing estimation of soil moisture was used to plan desert locust surveys for preventive management [101].…”

“…• Differentiate among diseases and pests, infectious severities and map their spatial distribution [47][48][49][50].…”