Review Paper on Soil Quality Detection and Crop Prediction Using Machine Learning

minimizing environmental impact. Climate adaptation strategies allow farmers to anticipate crop growth under varying weather conditions, reducing risks associated with climate change. IoTbased systems and digital farming solutions further enhance automation by providing real-time soil health monitoring and data- driven insights. Moreover, AI-driven predictions improve economic stability for farmers by minimizing financial risks and increasing profitability. This study explored various machine learning (ML) techniques for crop classification, yield prediction, and soil fertility assessment to enhance precision agriculture. Methods such as Support Vector Machines (SVM), Decision Trees, Random Forest, Neural Networks, and ensemble learning approaches have been employed across different agricultural datasets. Key applications include optimizing crop selection based on soil macronutrients, predicting yield variability under climate change scenarios, and automating seed germination assessment. Studies have demonstrated that ML models outperform traditional statistical and process-based models, thereby improving the accuracy of crop suitability, yield forecasting, and price prediction. Hy