Deep Learning Applications in Indian Agricultural Markets: A Systematic Review

Abstract

Agricultural markets in India are increasingly characterized by price volatility, fragmented supply chains, information asymmetry, climate uncertainty, and rapidly changing consumer demand, creating significant challenges for farmers, policymakers, and market stakeholders. Conventional statistical and econometric forecasting methods often struggle to capture the complex nonlinear relationships inherent in agricultural market systems. In recent years, deep learning has emerged as a transformative technology capable of improving agricultural market intelligence through advanced prediction, classification, optimization, and decision-support capabilities. This study presents a systematic review of the literature on deep learning applications in Indian agricultural markets with the objective of synthesizing current knowledge, identifying research trends, evaluating methodological approaches, and highlighting future research opportunities. The review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework to ensure a transparent and rigorous literature selection process. Relevant studies published between 2015 and 2026 were identified through major scientific databases, including Scopus, Web of Science, IEEE Xplore, SpringerLink, ScienceDirect, and Google Scholar, using predefined search strategies and eligibility criteria. The selected literature was systematically analyzed according to application domains, deep learning architectures, data sources, evaluation methodologies, implementation challenges, and policy implications.
The review reveals that deep learning models, particularly Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Transformer-based architectures, and hybrid deep learning models, consistently outperform conventional forecasting approaches in agricultural price prediction, demand estimation, crop yield forecasting, supply chain optimization, market intelligence, and risk assessment. However, widespread implementation in India remains constrained by fragmented datasets, inconsistent data quality, limited digital infrastructure, computational resource requirements, model interpretability concerns, and insufficient institutional coordination. The review further identifies significant research gaps, including the limited adoption of Explainable Artificial Intelligence (XAI), multimodal learning, graph neural networks, federated learning, and real-time decision-support systems tailored to Indian agricultural markets.
This review contributes to the literature by providing a comprehensive synthesis of deep learning applications in the Indian agricultural context, critically evaluating existing research, and proposing a future research agenda emphasizing standardized agricultural data ecosystems, explainable and trustworthy artificial intelligence, interdisciplinary collaboration, and evidence-based policy interventions. The findings offer valuable guidance for researchers, policymakers, agribusiness organizations, and technology developers seeking to accelerate the adoption of intelligent, data-driven agricultural market management systems that enhance market efficiency, strengthen food security, and promote sustainable agricultural development in India.