A CNN-Enabled Context-Aware Wireless Sensor Network for Smart Banana Cultivation
Abstract
Banana cultivation, a critical component of tropical agriculture, is highly vulnerable to environmental fluctuations, water stress, and disease outbreaks. Traditional wireless sensor networks (WSNs) used in precision farming lack the adaptability required for dynamic field conditions. This paper proposes a novel context-aware WSN framework integrated with a Convolutional Neural Network (CNN) to address these limitations in banana farming. The system captures and analyzes real-time environmental and soil data—such as temperature, humidity, soil moisture, and pH—using a multi-sensor architecture. A CNN-based model interprets this data to classify field conditions into actionable contexts, enabling intelligent irrigation scheduling, disease risk alerts, and nutrient deficiency detection.Field experiments demonstrate that the framework achieves a context classification accuracy of 95.3%, improves water-use efficiency by 28%, and reduces disease incidence through early intervention. Compared to rule-based systems, the proposed model significantly enhances adaptability, scalability, and resource optimization. This study contributes a robust, scalable approach to smart agriculture, offering a transferable model for other high-value, climate-sensitive crops.References
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