A Machine Learning Approach for Crop Yield Prediction Using Weather Condition

Efficient and sustainable farming is essential for human survival, particularly as global population growth and climate change place increasing demands on agricultural systems. Meteorological conditions, including temperature, humidity, and soil moisture, directly affect crop growth and yield. For varying conditions, this research aims to leverage real-time environmental data for predicting best-suited crops and informed decisions made with former and to optimize productivity and food security. Real-time data collection systems monitor key environmental parameters, and the data is stored in cloud-based systems for efficient management and accessibility. This enables scalable and reliable handling of large datasets, which is crucial in agriculture. The study uses two machine learning algorithm Decision Tree with Random Forest (DT-RF) to predict the optimal crop based on the environmental factors. For reducing overfitting and prediction accuracy improvement, the multiple decision trees aggregated with the Random Forest in which the data classified using Decision Tree algorithm. Temperature, humidity, and moisture content are the parameters that are used to gather real-time data and choose the best crop. To consider computational efficiency, accuracy and precision, both algorithms effectiveness assessed with the comparative analysis. Due to real-time meteorological data, the crop selection as the most reliable algorithm for the identification of results. The research provides recommendations on integrating these predictive systems into agricultural practices to improve crop selection, enhance productivity, and promote sustainable farming in the face of climate challenges.