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

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 3; p. 298
• Main Authors: Liu, Linyi, Dong, Yingying, Huang, Wenjiang, Du, Xiaoping, Luo, Juhua, Shi, Yue, Ma, Huiqin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2019
• Subjects: Accuracy; Airborne microorganisms; Algorithms; Artificial intelligence; Classification; Crop diseases; Laboratories; Learning; Machine learning; Mathematical analysis; Monitoring; Powdery mildew; Remote sensing; Sampling methods; Science; Statistical analysis; TrAdaBoost; Training; Transfer learning; Uncertainty; Wheat; wheat powdery mildew; Beijing China; United States > US; China; Germany
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11030298

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.