An Iot Low-cost Fertilizer For Crop Recommendation Using Machine Learning

Résumé: Agriculture plays a crucial role in sustaining the world's growing population, yet farmers face challenges in optimizing crop production and resource management. This thesis introduces an innovative IoT device empowered by machine learning (ML) to monitor soil nutrients and offer precise crop recommendations. The device integrates sensors such as, an NPK sensor and MAX485 TTL to gather real-time data on soil composition, humidity, temperature, rainfall, ph, and nutrient levels. This data is then transmitted to a server via protocol. ML algorithms analyse the collected data to generate tailored recommendations, including optimal crop choices, suitable fertilizer types, and application rates based on crop needs and soil conditions. Field experiments validate the system's efficacy compared to traditional methods, demonstrating its ability to boost crop productivity, optimize resource allocation, and promote sustainable agricultural practices for enhanced food security.

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