Predicting Crop Yields using Deep Learning Models and Multi-Source Agricultural Data

It is essential to be able to accurately predict crop yield in advance in order to maintain global food security, use resources efficiently, and have sustainable agricultural practices. The availability of massive multi source agricultural data from satellite imagery, weather conditions, soil profile and yield records, which totally takes prediction to a new level is what really raises prediction to a new level of accuracy. This work is about using deep learning models such as CNN, RNN and hybrid architectures to bring about a powerful as well as a scalable framework for crop yield prediction. The creation of a system for prediction capable of scaling and being reliable is based on the multi-source data of the remote-sensing images, the weather conditions, the soil profile, and the yield records. The tests done on datasets of varieties have shown that the proposed system can be adapted to different data and that the accuracy can be improved to a great extent compared to the traditional methods. The performance of the model is gauged by error metrics like the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). Comparative analysis shows that the hybrid model is more efficient in dealing with heterogeneous and incomplete data than other models. Among the main findings are the importance of using both the soil and the climate data and the potential of socio-economic variables to make the predictions richer. This paper is about the agriculture analytics industry, which faces problems such as heterogeneity of data, scalability, and model interpretability. The incorporation of explainable AI tools enables the framework to deliver the most useful insights to the users thus giving support to them in the proper management of resources, crop planning, and risk mitigation. The system is scalable evidenced by the deployment on the cloud which makes it possible to be used in different areas and farming systems.