AgriVerse: An AI-Driven Agri-FinTech Decision Intelligence Platform

Small and marginal landholders continue to make up the majority of the agricultural workforce, yet crop planning and financing decisions are frequently made from scattered and disconnected information. Over the past decade, researchers have applied machine learning (ML) and deep learning (DL) methods to individual pieces of this problem — crop recommendation, yield estimation, leaf-disease diagnosis, credit-risk and loan-eligibility scoring, and commodity price forecasting — with each area independently reaching strong, often benchmark-level accuracy. This paper reviews recent literature across these five research strands and additionally surveys operational Agri-FinTech platforms that attempt to combine agricultural and financial services. The review finds that Random Forest and related ensemble classifiers dominate crop recommendation and yield estimation, typically reporting 91–98% accuracy; MobileNetV2-based transfer learning achieves roughly 94–96% accuracy on standard leaf-disease datasets; ensemble credit-risk models increasingly incorporate agricultural and remote-sensing variables; and LSTM/GRU architectures lead commodity price forecasting. However, very few peer-reviewed systems combine agronomic and financial reasoning into a single, jointly evaluated recommendation, and commercial platforms that do integrate these services rarely publish reproducible model evaluations. Using the AgriVerse decision-intelligence platform as an illustrative case, this review argues that the next research priority is not incremental accuracy gains within any one module, but the unification of agronomic and financial decision layers, the replacement of rule-based financial logic with trained models, and rigorous field-level validation of such integrated systems.