PREDICTION OF CROP YIELD & WEATHER FORECASTING IN FARMLAND USING SENSOR DATA

Abstract

The integration of advanced sensor technologies with agricultural practices has opened up new opportunities for precision farming. This project aims to develop a system for predicting crop yield and weather forecasting in farmland using sensor data. The proposed system leverages the advancements in sensor technology and data analytics to provide accurate predictions and actionable insights for farmers. By collecting data from various sensors placed across the farmland, including soil moisture sensors, temperature sensors, humidity sensors, and phosphorus sensors, the system creates a comprehensive dataset. This dataset is then processed and analyzed using machine learning algorithms to identify patterns and correlations between sensor data and crop yield. By considering historical weather data, the system can also generate reliable weather forecasts. The predictions and forecasts generated by the system can assist farmers in making informed decisions related to crop management practices, ultimately improving crop yield and reducing resource wastage. This project addresses the need for precision agriculture techniques and contributes to sustainable farming practices by enabling farmers to optimize their operations based on real-time sensor data and weather forecasts.