Towards Explainable and Scalable Crop Recommendation Systems: A Deep Learning Survey from Maharashtra to Global Agriculture

The Indian economy relies on agriculture and Maharashtra state is one of the most agricultural states. Nonetheless, farmers are struggling with enduring problems such as climatic variability, land degeneration, as well as inactive crop consultative procedures, which do not focus on current realities. The recent developments in artificial intelligence (AI) and deep learning have provided new opportunities to the intelligent crop recommendation systems, which can examine the nutrients, pH, and moisture levels in the soil along with the weather indices to recommend the most appropriate crops. This review paper outlines the details of the deep learning-based crop recommendation systems with a pilot study on Maharashtra and India and the world outlook. The survey mentions the Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) models, and combinations of CNNs and LSTMs used on soil-climate data, as well as explainable AI methods, including Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) which can increase transparency and trust between farmers and AI. The contribution of the IoT sensors, weather APIs and multi-modal data fusion algorithms, like DeepG2P is examined to comprehend how real time and genomic data can be incorporated into crop planning. There are some comparative tables that are given to compare the accuracy, scalability, and adaptability of various models. Lastly, the paper concludes with the formulation of major challenges such as data scarcity, interpretability, scalability, and adoption challenges and offers future paths such as edge AI, federated learning, climate-resilient recommendations, and comprehensive integration with supply chain platforms. This survey makes deep learning-based crop recommendation systems an important facilitator of precision agriculture and sustainable food security by targeting Maharashtra and then extending to India and worldwide developments.