A Novel Ensembled Enabled Edge Computing Model for Precision Crop Recommendation

Smart farming involves leveraging modern technol- ogy to optimize agricultural practices, aiming to enhance both the quantity and quality of agricultural yields. One such approach is edge-based smart farming, which focuses on field crop monitoring using sensors and automating irrigation systems to meet agricultural demands efficiently. Unlike traditional cloud-based systems reliant on IoT models, edge-based systems offer reduced latency, extended battery life for IoT devices, cost-effective information management, and improved access to knowledge management and AI. In this paper, we propose the Ensembled Enabled Edge computing algorithm for developing a smarter farming system, particularly for crop recommendation, aiming to achieve superior results. Comparative analysis with established classifiers like SVM and Naïve-Bayes reveals that our algorithm attains the highest accuracy, reaching 95%.