An IoT-Enabled Crop Recommendation System Utilizing MQTT for Efficient Data Transmission to AI/ML Model

Precision farming has become a key tactic in today’s agricultural landscape for increasing crop yield and maximizing resource use. This paper presents an innovative Crop Recommendation System (CRS) that uses sensors placed throughout agricultural fields to gather vital environmental and soil data. The system makes use of the Internet of Things (IoT) and the Message Queuing Telemetry Transport (MQTT) protocol. The system employs sophisticated Artificial Intelligence (AI) and Machine Learning (ML) models to tackle the problems of real-time data acquisition, effective data transmission, and dynamic crop recommendation. A network of sensors that continuously track environmental variables like temperature, humidity, soil moisture, and nutrient levels is part of the CRS architecture. After that, the sensor data is sent to a central MQTT server, guaranteeing dependable and low-latency communication even in situations with limited bandwidth, which are common in rural agricultural settings. When the data gets to the server, AI/ML models trained to correlate particular environmental conditions with ideal crop selections and cultivation techniques process and analyze it. To provide customized crop recommendations, these models take into account past crop performance data, ongoing agricultural research, and current field conditions. The accuracy of this implementation is 99% accuracy.