Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

Linyi Liu,Wenjiang Huang,Juhua Luo,Xiaoping Du,Huiqin Ma,Yingying Dong,Yue Shi

doi:10.3390/rs11030298

Abstract

In order to monitor the prevalence of wheat powdery mildew, current methods require sufficient sample data to obtain results with higher accuracy and stable validation. However, it is difficult to collect data on wheat powdery mildew in some regions, and this limitation in sampling restricts the accuracy of monitoring regional prevalence of the disease. In this study, an instance-based transfer learning method, i.e., TrAdaBoost, was applied to improve the monitoring accuracy with limited field samples by using auxiliary samples from another region. By taking into account the representativeness of contributions of auxiliary samples to adjust the weight placed on auxiliary samples, an optimized TrAdaBoost algorithm, named OpTrAdaBoost, was generated to map regional wheat powdery mildew. The algorithm conducts this by: (1) producing uncertainty associated with each prediction based on the similarities, and calculating the representativeness contribution of all auxiliary samples by taking into account the overall uncertainty of the wheat powdery mildew map; (2) calculating the errors of the weak learners during the training process and using boosting to filter out the unreliable auxiliary samples by adjusting the weights of auxiliary samples; (3) combining all weak learners according to the weights of training instances to build a strong learner to classify disease severity. OpTrAdaBoost was tested using a dataset with 39 study area samples and 106 auxiliary samples. The overall monitoring accuracy was 82%, and the kappa coefficient was 0.72. Moreover, OpTrAdaBoost performed better than other algorithms that are commonly used to monitor wheat powdery mildew at the regional level. Experimental results demonstrated that OpTrAdaBoost was effective in improving the accuracy of monitoring wheat powdery mildew using limited field samples.

Highlights

Wheat powdery mildew is caused by the fungus Blumeria graminis and is one of the most common diseases that result in significant loss of crop yield and quality in China [1,2,3]
Monitoring of wheat powdery mildew at a regional level is of practical importance for agricultural management
We proposed an algorithm, OpTrAdaBoost, to monitor wheat powdery mildew in the Western Guanzhong Plain using auxiliary field data acquired from the suburban area of Shijiazhuang

Summary

Introduction

Wheat powdery mildew is caused by the fungus Blumeria graminis and is one of the most common diseases that result in significant loss of crop yield and quality in China [1,2,3]. Wheat powdery mildew has spread from Southwestern China to Eastern and Northern China. According to the statistics of the China’s National Agricultural Technology Extension and Service Center (NATESC), the annual average outbreak area for powdery mildew was 10 million ha over the last 17 years [5]. Accurate monitoring of wheat powdery mildew at the regional level is important for food security and environmental protection [6]. Wheat powdery mildew is monitored by visual inspection of individual plants, which is time-consuming and inefficient [7]. A new satellite-based remote sensing technology has become a more viable option for managing and controlling agricultural practices [3,6,8,9,10]

Methods

Results

Conclusion