Smartcrop Solutions A Machine Learning-Based System for Soil Classification and Crop Recommendation

Smartcrop Solutions A Machine Learning-Based System for Soil Classification and Crop Recommendation

Mr. Rajendra P. Sabale1, Mr. Ganesh D. Ugale2

1Assistant Professor, Department of Computer Engineering, Pravara Rural Engineering College Loni, Maharashtra, India.

2Student, Department of Computer Engineering, Sir Visvesvaraya Institute of Technology, Nashik, Maharashtra, India.

ABSTRACT

The Smart AgroTech system utilizes machine learning and real-time sensor data to enhance soil classification and recommend suitable crops for farmers. By integrating various sensors like the EC (Electrical Conductivity) Sensor, pH Sensor, and Temperature and Humidity Sensor, the system collects real-time data from the soil, enabling accurate analysis of soil health and conditions. Machine learning algorithms such as Random Forest and Support Vector Machines (SVM) are employed to process this data and classify soil into various categories, such as sandy, loamy, or clayey. Based on the classification, the system suggests optimal crop recommendations tailored to the soil type, ensuring increased agricultural productivity. Additionally, the system provides insights into the moisture content and temperature, which are critical factors for crop growth. This solution is particularly beneficial for uneducated farmers, as it simplifies complex agricultural decisions and promotes sustainable farming practices. The integration of these technologies makes it easier to monitor and improve soil health, increase crop yield, and reduce the environmental impact of conventional farming methods. The Smart AgroTech system has the potential to revolutionize agricultural practices by providing a data-driven approach to farming, particularly for regions with limited access to expert agricultural advice.

Keywords: Soil Classification, Crop Recommendation, Machine Learning, EC Sensor, pH Sensor, Temperature Sensor, Humidity Sensor, Precision Agriculture.