Design of an Integrated New Multiple Stage Deep Learning Framework for Real-Time Agro-Advisory Using Spatiotemporal Weather Analysis and Multilingual Crop Recommendation

Resumo

Increased unpredictability of weather events and soil degradation in Maharashtra's agricultural zones necessitates the development of intelligent, context-aware, and region-specific agro-advisories. Due to factors such as limited data integration, low localization, low interpretability, and lack of multilingual support, such systems retain limited usability and impact for different farming communities. In addition, the processes of most previous models are built into unconnected pipelines usually without adaptive learning or integration of real-time feedback. This study thus proposes a novel five-stage deep learning-based framework for agro-advisory systems that contains such aspects as real-time weather prediction, soil classification, multilingual crop recommendation, performance evaluation, and adaptive learning. This pipeline starts with DI-SATNet, a Dual-Input Self-Calibrating Spatiotemporal Attention Network intended for localized weather prediction based on fusion of satellite and IoT sensor data, with an overall accuracy of 92.7%. This feeds land surface statistics that are passed on to MS-DMED, or the Multiscale Deep Morphological Encoder-Decoder, working for soil types classification based on drone imagery and sensor values and achieving 95.1% accuracy. Then, we have the Tri-Language Adaptive Crop Recommendation Transformer (TACR-Trans), which contextualizes recommendations in Marathi, Hindi, and English through integrated soil, weather, and market data. A multi-criteria meta-optimizer (MC-MetaEval) is used to objectively evaluate the performance of these models, adaptively ranking methods according to regional priorities and dynamic agricultural conditions. Finally, G-FAAR, a Generative Feedback-Augmented Auto Reinforcement module, endlessly improves the system by simulated scenarios and real-world feedbacks. The integrated system thus enables high-resolution, multilingual, adaptive, and regionally relevant crop decision-making sets. The system tremendously strengthens accuracy in predictions, relevance in recommendations, and sustainability of the model for the long term, thus providing a scalable solution toward the goal of precision agriculture attributable to climate change sensitivity sets.