Integrating Machine Learning and Deep Learning for Enhanced Soil Fertility Prediction and Crop Recommendation in Precision Agriculture

In this research, a soil fertility prediction system is proposed to increase the efficiency of yield production and vegetation cover. Based on the nitrogen (NO₃), phosphorous (P), and potassium (K) levels in the dataset, our method combines machine learning and deep learning techniques to identify soil nutrients and suggest ideal crops. We combine and preprocess datasets, and then use different deep learning architectures and regression models, such as MLP Regressor, Linear Regressor, and Random Forest Regressor, to precisely estimate vegetation cover. Our findings show that when it comes to accuracy and error measures, the MLP Regressor performs the best. In summary, this study provides a significant understanding of soil nutrient analysis and crop suggestion for environmentally friendly farming methods.