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Plant Science Today (PST)

Review Articles

Vol. 13 No. sp9 (2026): Recent Advances in Agriculture

Leveraging satellite products and machine learning/deep learning models for the upgrading of agro-advisory systems in India: A bibliometric and systematic narrative review

DOI
https://doi.org/10.14719/pst.16505
Submitted
2 July 2026
Published
16-10-2026

Abstract

A comprehensive bibliometric review was conducted to evaluate the most relevant literature retrieved from the Scopus database, resulting in the identification of 125 relevant publications from an initial pool of 1948 records through a screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The bibliometric analysis revealed a significant increasing trend in year-wise publications and citations, wherein India and the US emerged as the leading contributors with total publications of 34 and 25 respectively. Notably, the systematic narrative review highlights that agro-advisory systems (AAS) have increased farmer income by 83 %, improved crop yields by 6–25 % and enhanced farm profitability by 15–26 % in India. Despite these achievements, conventional AAS frameworks that rely predominantly on automatic weather stations (AWS) continue to face critical limitations, including sparse spatial coverage, restricted agro-meteorological observations, data discontinuities and inadequate representation of local microclimatic variability. To address these limitations, the study highlights the importance of integrating AWS observations with high-resolution satellite products through downscaling and subsequent assimilation within the Weather Research and Forecasting Data Assimilation (WRFDA) framework to improve the spatial accuracy of weather forecasts and facilitate the delivery of reliable block-level and village-level agro-advisories. However, the operational implementation of satellite-assisted weather forecasting using the Weather Research and Forecasting (WRF) model is often constrained by latency in data availability, as many high-resolution satellite products require several hours to multiple days for processing and dissemination. In order to overcome this limitation, the review further demonstrates the considerable potential of machine learning (ML) and deep learning (DL) models trained using historical satellite observations along with meteorological predictors derived from numerical weather prediction (NWP) systems for enhancing downscaling capabilities and improving forecast accuracy. The results inferred that the satellite-based weather forecasting using ML and DL architectures achieved strong predictive performance, with reported R² values of up to 0.982, 0.976 and 0.95 for rainfall, temperature and aerosol concentration respectively. These findings demonstrate the increasing potential of satellite-driven ML and DL approaches for high-resolution weather forecasting and their applicability to next-generation, location-specific agro-advisory services. However, their operational utilisation depends on the availability of adequate computational infrastructure, skilled human resources, economic feasibility, institutional coordination and farmer acceptance. Therefore, the future of satellite-based AAS lies in developing computationally efficient, economically viable and farmer centric decision support platforms capable of translating complex forecast information into timely, understandable and actionable recommendations for climate-resilient agriculture.

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