Smart Agriculture Using Machine Learning

Abstract-Crop productivity is paramount to world food security, and precise crop yield forecasting is critical to maximizing farm operations. Classic forecasting techniques hardly consider the intricacies of interaction between climatic factors, soil properties, and crop growth stages. The rapid progress in Machine Learning (ML) and Deep Learning (DL)in recent years has transformed crop prediction by utilizing extensive data from meteorological archives, soil sensors, and remote sensing technologies. This research examines different ML methods, such as Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Hybrid AI models, to improve crop yield prediction. By incorporating major agrarian parameters like temperature, rainfall, humidity, soil moisture, and nutrient levels, AI-based models can offer more accurate and dynamic predictions, supporting farmers and policymakers in decision-making.The paper also addresses issues like data quality, model interpretability, and climate change adaptation, and possible solutions like IoT-based real-time monitoring and Explainable AI (XAI).