Abstract:

Using an ESP32 microcontroller linked to a variety of sensors—a NPK for soil nutrient levels, a DHT11 for temperature and humidity, an LDR for sunlight intensity, and a moisture sensor for soil water content—this cutting-edge crop recommendation system combines the Internet of Things (IoT) with machine learning to provide accurate, location-specific advice to farmers. This technology reliably gathers environmental data in real-time and sends it to a cloud-based platform for analysis, as opposed to conventional farming approaches that depend on seasonal intuition or broad agricultural principles. In order to choose the best crops to sow, sophisticated machine learning algorithms compare the present situation to past crop yields, soil health trends, and weather patterns. The algorithm also takes demand and price trends into account in order to provide economically sound suggestions. A sustainable and scalable solution for contemporary agriculture in both resource-limited and developed locations, this end-to-end data-driven strategy improves decision-making, raises crop yield potential, and lowers input waste.

Description:

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