Crop Prediction Using Sensors and Machine Learning

Abstract: The agricultural industry is rapidly embracing emerging technologies to enhance crop forecasting and resource utilization. This project suggests an intelligent crop forecasting system based on IoT and machine learning for sustainable agriculture. Crop selection is usually done based on guesswork by the farmers, resulting in low harvest and wasted resources. Crop yield forecasting before harvesting is also a severe problem in developing countries. The system relies on Arduino-based hardware interfaced with sensors for measuring temperature, humidity, rain, soil moisture, and water levels. The values are applied to train a logistic regression model for crop recommendations. Crops taken into consideration are millets, tomato, sugarcane, strawberry, cotton, and rice. Early tests with simulated data have reflected encouraging accuracy. This locationaware, real-time solution can enable farmers to take informed decisions. It also facilitates schemes such as the Soil Health Card Scheme and PM-KISAN. Challenges involve maintaining sensor accuracy and dealing with environmental noise. Periodic calibration will be required for reliable performance. The system decreases reliance on conventional methods and enhances farm profitability. Satellite data and sophisticated ML models could be added in the future. This project provides a cost-effective, scalable step towards smart, sustainable agriculture.