Combining Explainable Machine Learning with Accurate Crop Suggestion and Yeild Calculation

Agriculture is a keystone of food security and economic constancy, but it facing serious challenges due to abnormal behavior of climate, soil degradation, and resource limitations. The modern machine learning techniques are good for classification and yield predicton, but they are very small and limited in datasets, single-crop prediction, and static data sources. To address these limitations, the study introduces an enhanced framework that combines Internet of Things (IoT) devices, satellite images, and economic indicators with advanced machine and deep learning models. Our system uses ML technology to help with farming. Convolutional Neural Networks (CNNs) and also identifies problems like diseases early on, and Long Short-Term Memory (LSTM) networks to handle on how much yield to expect. To make sure our predictions are reliable, we also decided to use ensemble techniques. our system can predict yields for multiple crops, give real-time updates, and also offers recommendations for specific regions. It really boosts productivity, and reduces the risks, helps farmers use resources smarter, and ultimately, make agriculture more sustainable overall.