MACHINE LEARNING IN PRECISION AGRICULTURE

Precision agriculture has emerged as a vital approach for improving agricultural efficiency and sustainability by leveraging advanced technologies. This project focuses on developing a machine learning model that utilizes historical data for precision agriculture applications. The system aims to assist farmers in making data-driven decisions by predicting suitable crops, forecasting crop yields, and detecting potential crop diseases. The proposed solution integrates wireless sensor networks to collect environmental parameters such as rainfall, temperature, humidity, Potassium, Nitrogen and soil pH, which are combined with historical agricultural data. Machine learning models, including Voting Classifier (Support Vector Machines, Gaussian Naïve Bayes, Random Forest) and Linear regression model, are trained and deployed to analyze the data for actionable insights. The Voting classifier model achieved a 99.3% accuracy in predicting suitable crops, while the Linear regression model provided yield forecasts with an R² score of 0.91. The Convolutional Neural Network (CNN) detected crop diseases with an accuracy of 93.8%. The system’s implementation demonstrates the effectiveness of combining historical data with machine learning techniques to enhance precision agriculture practices. By providing accurate and timely information, this solution helps optimize crop selection, improve resource allocation, and mitigate the risk of crop diseases. Recommendations for integration with mobile applications, weather data integration and farmer education and training are proposed to further enhance the system’s usability and impact. This project offers a promising step toward sustainable and smart farming practices, contributing to food security and agricultural productivity.