Developing an early warning system for Banana Xanthomonas Wilt (BXW) in Rwanda - Owuor, C. A. (Strathmore University, 2024)
Bananas are crucial for the agricultural economy of the African Great Lakes region, including countries like Kenya, Uganda, Tanzania, Burundi, Rwanda, and parts of the Democratic Republic of Congo, with an annual production exceeding 22 million tonnes. However, banana productivity faces significant threats from pests and diseases such as the Banana Xanthomonas Wilt (BXW), caused by the bacterium Xanthomonas campestris pv. Musacearum. In this study, machine learning techniques were employed to develop an early warning system for BXW. Various classification models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Gradient Boosting Machine (GBM), were trained and evaluated for predicting BXW occurrence. RF outperformed the other models with an accuracy of 94%, followed by GBM (89%), KNN (87%), and SVM (83%). In terms of the area under the curve (AUC), RF outperformed the other models with a score of 96%, followed by GBM (95%), KNN (94%), and SVM (90%). This highlights RF’s effectiveness in creating habitat suitability maps and establishing an early warning system for BXW. The RF model was used to develop a BXW habitat suitability map for Rwanda, aiding agricultural stakeholders in identifying high-risk areas. Furthermore, a Short Message Service (SMS)-based early warning system was implemented to provide timely alerts to farmers, thereby, enhancing BXW mitigation efforts. Additionally, a web portal for real-time BXW risk prediction and analysis was developed, providing accessible information to stakeholders for proactive management strategies. Keywords: BXW, Early Warning System, Rwanda, Remote Sensing, Machine Learning.
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