AI-BASED CROP RECOMMENDATION FOR INTENSIVE FARMING USING WIRELESS SENSOR NETWORKS

COMPUTER ENGINEERING

Intensive farming practices demand precise and efficient management techniques to optimize crop yields while minimizing resource consumption. In this context, leveraging artificial intelligence (AI) algorithms coupled with wireless sensor networks (WSNs) presents a promising solution for enhancing agricultural productivity. This paper proposes a novel framework for AI-based crop recommendation tailored specifically for intensive farming environments. The integration of AI algorithms enables the analysis of vast datasets encompassing various environmental parameters such as soil moisture, temperature, humidity, and nutrient levels collected through WSNs deployed across farmlands. Through advanced machine learning techniques, including data mining, pattern recognition, and predictive modelling, the proposed system processes this data to derive actionable insights regarding optimal crop selection and cultivation strategies. The AI-based crop recommendation system operates in a closed-loop fashion, continuously learning and adapting to dynamic environmental conditions and agronomic factors. By considering historical data, real-time sensor readings, and expert knowledge, the system generates personalized crop recommendations tailored to specific field characteristics, climate patterns, and resource constraints.

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