AI-driven soil analysis and crop recommendation system

This research introduces an innovative IoT-enabled Soil Analysis and Crop Recommendation System, aimed at transforming agricultural decision-making through the integration of advanced sensor technologies, cloud platforms, and machine learning techniques. Devices such as DHT11 sensor for temperature and humidity, alongside NPK nutrient and pH sensors, gather critical soil and environmental data. The ESP8266 microcontroller, in conjunction with the Blynk IoT platform, facilitates real-time data transmission and analysis, giving farmers useful information on climate and soil health. At the base of this system is a Random Forest Classifier that decides which crops to recommend based on NPK levels, pH, humidity, temperature, and rainfall for a particular set of environmental conditions. A multi-factor recommendation algorithm further refines these predictions by including soil nutrient profiles, pH measurements, temperature variation, and localized climate data for even more accurate crop recommendations. Experimental validation in several sites of agriculture was shown with 98% accuracy on crop selection. IoT and AI technologies will thus become the new future for farming practices. This system helps the farmer to use the resources much more efficiently and decrease the input cost with improved yield