PREDICTIVE ANALYTICS FOR SMART FARMING: INTEGRATING IOT SENSOR DATA WITH MACHINE LEARNING MODELS

Abstract

Smart farming is a modern approach to agriculture that uses advanced technologies to enhance farming processes and boost productivity. In this research, we develop a smart farming system that integrates IoT sensors and machine learning to support farmers in making informed decisions. Sensors are deployed in the field to gather real-time data on soil moisture, temperature, humidity, and other environmental parameters. Additionally, weather forecast data is incorporated to anticipate future conditions. The collected data is cleaned to eliminate errors and then analyzed using machine learning algorithms such as Decision Tree and Random Forest. The system can predict crop conditions and irrigation needs and provides actionable recommendations to farmers. For instance, if the soil is dry but rainfall is anticipated, the system advises delaying irrigation, thereby conserving water. By merging sensor data and weather insights, the system achieves higher accuracy and utility. It minimizes manual labor and optimizes resource utilization, including water and fertilizers. Results indicate that this system delivers more accurate predictions and enables farmers to make timely and effective decisions compared to traditional methods. In summary, this study demonstrates that integrating IoT, machine learning, and intelligent recommendations can establish an efficient and smart farming solution for modern agriculture.