Sustainable Crop Forecasting for Food Security: A Review of AI and Remote Sensing Methods

As the climate changes increase, forecasting crop yields correctly is very important for the achievement of agricultural sustainability, using resources effectively, and ensuring food security. The complex nonlinear connections between crop, soil, and climate factors cannot be fully shown by traditional methods like regression and ARIMA models. To improve the study, recent advancements in remote sensing and machine learning have made it possible to gather and combine data from different sources. In this work, we look at how ML and DL models combined with the outline include hybrid DL networks such as CNN-LSTM, and algorithms like RF, SVM, and Gradient Boosting that can capture both temporal and spatial needs in crop growth dynamics. The study's results displayed that, about calculating accuracy and scalability, hybrid DL models achieve much better results than traditional and standalone ML models. The presented system provides a path to data-driven, renewable agricultural decision-making and underlines the possibility of joining data from multiple sources and using detailed information for effective crop yield forecasting. RS indices, soil properties and weather variables to give a reliable evaluation of crop yield.