Agriculture Yield Forecasting via Regression and Deep Learning with Machine Learning Techniques

India’s financial system is mostly based on agriculture, which requires human labor to survive. The primary challenge is the growing humanity, which results in a rise in nutrient demand, which threatens nutrition. Farmers must increase their output on the same plots of land in order to fulfill this expanding demand. Technology-assisted agricultural output prediction can considerably help farmers increase productivity. The central aim is to anticipate crop yields utilizing critical factors that seriously endanger agriculture’s long-term viability, such as rainfall, crop type, climatic conditions, geographic location, production data, and historical yield records. Agriculture’s long-term viability is in danger. The decision support tool for farmers is ML-DL-powered crop yield forecasts, which will help them select the best crops and take the most appropriate course of action throughout the growth cycle. Even in distracting or unfavorable environmental conditions, crop choosing using ML-DL algorithms is particularly effective in reducing production losses in farming. This study uses DL techniques, including LSTM, i.e., Long Short-Term Memory Networks, and CNN, i.e., Convolutional Neural Networks, and ML techniques like D.T., i.e., Decision Trees, R.F., i.e., Random Forests, and XGBoost regression. Predictive tools aid small-scale farmers, improving crop yield estimations and planting decisions. CNN outperforms L.S.T.M. However, comprehensive, accurate data, especially environmental and weather information, are essential. In conclusion, India’s agriculture sector grapples with feeding a growing population. Advanced ML and DL provide solutions for long-term sustainability. Future research should explore more deep learning methods and integrate remote sensing and satellite data for precise crop predictions.