Leveraging Large Language Models (LLMs) for GeoAI-enabled Digital Agro-advisory

Abstract

Digital agriculture has undergone a profound transformation driven by rapid advances in Earth observation, unmanned aerial vehicles and advanced data processing techniques. Despite this progress, climate-smart agriculture management faces a critical challenge: a farmer-centric agro-advisory system. Farmers and extension workers have limited access to GeoAI-based agro-advisory services, primarily due to limited data access and insufficient technical support. This study synthesises the potential of Large Language Models to address this challenge by translating complex GeoAI data into actionable and human-centric advice. The review examines gaps, recent advancements, and the model architectures required to adapt general-purpose LLMs for remote-sensing-based crop monitoring and management. We distinguish between experimentally validated capabilities of LLMs and the broader prospective applications proposed for agricultural remote sensing, noting that many current advances are primarily driven by multimodal foundation models, computer vision, and GeoAI systems rather than standalone LLMs. The study highlights the importance of advanced Retrieval-Augmented Generation and Supervised Fine-Tuning in agronomic science and mitigating the risk of misinterpretation. Further, we examine the emerging capabilities of multimodal approaches, which can seamlessly integrate visualisation and textual reasoning to support stakeholders in assessing crop health conditions and biophysical anomalies. The representative case studies, spanning multiple geographies and including voice-based advisory systems, demonstrate the shift from static advisory tools to dynamic, interactive recommendation systems. We highlight current challenges, such as the need for region-specific fine-tuning, data governance, and operation in low-connectivity environments, and advocate for a “human-in-the-loop” approach to decision-making. Through this, the LLMs will function as co-pilots assisting multiple stakeholders rather than as autonomous decision-makers vulnerable to biased output or hallucination problems. The review concludes by outlining a forward-looking research scope and closed-loop systems that iteratively learn from field outcomes.

1 Introduction

Over the past decade, digital agriculture has undergone a profound transformation driven by rapid advances in Earth observation, unmanned aerial vehicles (UAVs), Internet of Things (IoT), and proximal sensing technologies. These developments have enabled continuous, multiscale, and unprecedented volumes of spatiotemporal data to be generated for monitoring crops, soils, hydro-ecology, and agroecosystem processes (). Operational satellite missions such as Landsat, Sentinel, MODIS, LISS, Cartosat, WorldView, PlanetScope, combined with very high-resolution UAV imagery, now form the backbone of precision agriculture, supporting applications including crop monitoring, yield estimation, cropping practices and the implementation of climate-smart agricultural practices (; ). Despite the growing maturity of remote sensing technologies, the increasing availability of data has exposed a fundamental limitation in digital agriculture: the interpretation and convergence bottleneck. Remote sensing data are typically expressed as spectral reflectance or backscatter values, vegetation indices, and derived biophysical parameters, which require substantial agronomic and contextual expertise to interpret and apply to improve crop management. Translating these quantitative indicators into actionable recommendations requires an understanding of crop type, phenological stage, soil characteristics, climatic conditions, and management history, which is complex (). This complexity constrains the timely delivery of decision-support information, particularly in smallholder-dominated agricultural systems where access to trained extension services remains limited and costly (). Recent advances in Large Language Models (LLMs) offer a promising framework for addressing these challenges. LLMs such as the Generative Pre-trained Transformer (GPT), Large Language Model Meta AI (LLaMA), and Pathways Language Model (PaLM) are based on transformer architectures that model long-range dependencies, support contextual reasoning, and enable large-scale knowledge synthesis (; ). Unlike conventional machine learning models, which are optimised for narrowly defined prediction tasks, LLMs are designed to interpret, explain, and adapt to complex information across domains. These capabilities position LLMs as an effective interface layer for translating remote-sensing-derived indicators into human-interpretable, decision-relevant guidance for agronomists, extension agents, and farmers.

The potential of LLM-enabled agricultural advisory systems is further enhanced by recent advances in multimodal foundation models and vision-language frameworks that integrate visual, textual, and geospatial information. It is important, however, to distinguish between standalone LLMs and multimodal computer vision systems. Standalone LLMs are primarily designed for language understanding, knowledge synthesis, reasoning, and natural-language generation, whereas remote sensing image interpretation is largely performed by computer vision, vision-language, and multimodal foundation models. Recent vision-language models (VLMs) and geospatial foundation models have demonstrated increasing capability in analysing remote sensing imagery, field photographs, and UAV observations for applications such as crop stress detection, disease identification, canopy trait assessment, phenological monitoring, and interpretation of spectral anomalies (; ). These models generate structured visual insights that can subsequently be translated into actionable recommendations through LLM-based reasoning and conversational interfaces. This distinction is particularly important given the exponential growth of Earth observation imagery, driven by the expansion of satellite constellations and the widespread adoption of UAV platforms for agricultural management. While multimodal foundation models and GeoAI systems increasingly perform perception and analytical tasks associated with image understanding, LLMs primarily contextualise these outputs, integrate agronomic knowledge, retrieve relevant information from external knowledge bases, and communicate recommendations in a human-interpretable form. Therefore, the emerging opportunity lies not in replacing remote sensing analytics with LLMs, but in integrating multimodal perception systems with language-based reasoning frameworks to support end-to-end agricultural decision-making. Despite this rapid progress, a systematic understanding of how LLMs can be effectively integrated into remote sensing-based agricultural workflows remains lacking. The existing literature predominantly focuses on remote sensing for crop monitoring or the application of LLMs in broader agricultural contexts, leaving a crucial gap in current knowledge. To date and to our knowledge, no comprehensive review has synthesised how LLMs specifically enhance the interpretation, communication, and decision-support functionalities of remote sensing in digital agriculture. Moreover, critical deployment challenges such as the risk of model “hallucination”, the necessity for region-specific fine-tuning, and strategies for effective implementation remain largely unexplored. Existing studies tend to focus either on remote sensing-based crop monitoring and modelling (; ) or on the application of LLMs in broader agricultural advisory and knowledge systems () with relatively little synthesis across these domains. As agricultural monitoring systems increasingly transition from descriptive assessment to operational advisory services, the need for intelligent interpretation frameworks becomes more acute. LLMs are efficient in data mining through historical observations and practices, textual reports, and agronomic knowledge bases, thereby reducing human effort. The Earth observation foundation models highlight the growing integration of geospatial AI, multimodal learning, and LLM-assisted agricultural analytics. For instance, the IBM-NASA Prithvi model uses multi-temporal satellite imagery and transformer-based learning to support applications such as crop monitoring, land-use mapping, and environmental assessment (). Similarly, the European Space Agency (ESA) WorldCereal initiative applies AI-driven analysis of Sentinel satellite data to generate global crop maps for major cereals, including wheat and maize (). Although these systems are not standalone LLMs, they demonstrate how foundation models and multimodal geospatial AI are advancing scalable agricultural monitoring and advisory systems. Future integration of these Earth observation models with conversational LLM frameworks could further improve farmer-centric decision support and agro-advisory services (). Therefore, a coherent understanding of how LLMs can be systematically integrated into remote sensing-driven agricultural workflows will improve farm management and decision support.

This review aims to address the following vital gaps: (a) To synthesise the potential for integrating advanced remote sensing tools/techniques and LLMs for precision/digital agriculture and advisory generation. (b) To present representative case studies illustrating the integration of LLMs with satellite- and UAV-based crop monitoring workflows, focusing on rice-based cropping systems. (c) To critically examine technical, operational, and governance challenges, including reliability, validation, and ethical considerations associated with LLM-based advisory. (d) To outline a forward-looking roadmap/framework that prioritise multimodal model development, regionally adaptive fine-tuning, and edge or low-bandwidth deployment strategies.

By bridging remote sensing analytics with LLM-based knowledge representation, this review argues that foundation models have the potential to significantly enhance the scalability, contextual relevance, and inclusivity of digital agriculture. When appropriately grounded in agronomic knowledge and validated against observational data, LLMs can transform geospatial information from an expert-centric analytical resource into a human-centred and actionable advisory ecosystem that supports informed decision-making (). Figure 1 illustrates the knowledge translation pipeline from remote sensing data acquisition through biophysical indicator derivation to LLM-driven farm-level advisory, highlighting the interpretation gap that LLMs are positioned to bridge.

FIGURE 1

1.1 Scope and methodology of this review

This review follows a structured narrative and conceptual review approach to map the intersection of LLMs, remote sensing, and agricultural advisory systems. The literature search was conducted across Web of Science, Scopus, Google Scholar, and the arXiv preprint repository using Boolean combinations of key terms including “Large Language Model,” “LLM,” “Generative AI,” “remote sensing,” “digital agriculture,” “precision farming,” “crop advisory,” “farm advisory chatbot,” “RAG agriculture,” and “multimodal geospatial AI.” The search covered publications from 2010 to 2025, with emphasis on post-2020 literature to capture the rapidly evolving LLM landscape. Inclusion criteria required that studies: (i) explicitly addressed the application of LLMs or conversational AI in agricultural contexts, (ii) incorporated remote sensing data or geospatial analytics, or (iii) presented technical architectures (SFT, RAG, RLHF, multimodal fusion) with relevance to agro-advisory generation. Studies that focused exclusively on conventional machine learning for crop classification, without an advisory or interpretive component, were excluded. Grey literature, including technical reports from CGIAR centres, FAO, and national agricultural extension bodies, was also consulted to capture operationally deployed systems not yet represented in peer-reviewed literature. A total of over 330 sources were screened, of which 97 were retained for detailed synthesis (Figure 2). The review is organised thematically rather than chronologically, progressing from the remote sensing foundation (Section 2) through LLM adaptation pathways (Section 3), representative field deployments (Section 4), deployment challenges (Section 5), and future research frontiers (Section 6).

FIGURE 2

2 Overview of remote sensing in digital agriculture and associated challenges

Remote sensing has become a cornerstone of modern digital agriculture, providing the spatial and temporal multi-dimensional information needed to monitor, assess, and manage complex agroecosystems. The expansion of various satellite platforms, UAV-based imaging systems, and sensor-integrated field networks has revolutionised our ability to observe crop conditions, map spatiotemporal variability, and optimise resource use (; ). These advances fuel precision agriculture, enabling data-driven decisions from crop suitability, fertilisation and pest management, crop health monitoring, water resource utilization, and yield forecasting. Although a large proportion of the latest remote sensing-based crop management uses machine learning models, most of these trained models operate in silos and require human intervention for consistent interpretation and translation into actionable advice for farmers, extension workers, and policymakers.

2.1 Landscaping the diverse remote sensing applications in agriculture

Satellite remote sensing remains the backbone of national, regional, and global agricultural monitoring systems, providing consistent, spatially explicit observations of cropland dynamics over large areas. A diverse constellation of publicly accessible and commercial Earth observation platforms, including multispectral optical sensors such as Sentinel-2, Landsat-8/9, and PlanetScope, MODIS, WorldView, LISS, as well as synthetic aperture radar (SAR) systems such as Sentinel-1, EOS-04, and hyperspectral data such as HySI and PRISMA, and satellite data-derived products such as soil moisture (by SMAP), now deliver high-frequency data streams essential for operational agricultural applications (; ; ). Despite these advances, a key limitation of satellite remote sensing lies in its spatial, spectral, and temporal resolutions. Most available data have revisit times of 5–6 days–20 days and moderate spatial resolution, which limits their application during the crucial crop-monitoring phase. While freely available sensors typically operate at spatial resolutions of 10–30 m, which are often insufficient to capture within-field variability in small farmlands with field sizes below 1 ha (; ). As a result, satellite products are highly effective for strategic monitoring and policy-level assessments in homogeneous fields. However, they are less suited for heterogeneous small field-level decision-making without costly high-resolution imagery.

To overcome the spatial resolution limitations of satellite-based observations, UAVs have emerged as a critical component of field-scale agricultural intelligence. UAV platforms provide centimetre-level spatial detail with flexible deployment schedules, allowing crop condition monitoring at user-defined temporal frequencies in key phenological stages (; ). Advanced UAV systems can be equipped with a diverse suite of lightweight sensors, each contributing complementary information on crop status. High-resolution RGB cameras generate detailed orthomosaics that support plant stand assessment, lodging detection, weed mapping, and canopy structure analysis (). Recent advances in deep learning have further expanded the capabilities of UAV and mobile vision systems beyond traditional vegetation index mapping. Convolutional neural networks and real-time object detection frameworks, particularly the YOLO family of models, have enabled automated crop detection, fruit localisation, segmentation, counting, and growth-stage recognition at field and greenhouse scales. For example, developed an integrated YOLO-based system for capsicum detection, segmentation, growth-stage classification, fruit counting, and real-time mobile identification. Such perception-oriented computer vision models provide detailed crop-level intelligence that can complement satellite-derived biophysical indicators and serve as important upstream information sources for multimodal advisory systems. In future GeoAI-enabled agricultural platforms, outputs from UAV-based detection and crop-stage recognition models could be integrated with LLM reasoning frameworks to generate more context-aware recommendations on harvesting, crop management, and resource optimisation. In parallel, thermal infrared sensors enable the mapping of canopy temperature, which serves as a proxy for plant water status and transpiration efficiency, supporting irrigation scheduling and drought stress detection at fine spatial scales (). These high-fidelity datasets are already reshaping agricultural research and innovation pipelines. For farmers and extension agents, a detailed crop health or thermal anomaly map may indicate variability. However, it does not inherently convey why stress is occurring or what management action should follow. Bridging this interpretive gap remains a challenge in translating UAV-based insights into timely, actionable agricultural decisions.

Recent advances in UAV and mobile vision systems have further extended field-scale monitoring capabilities through deep learning-based crop detection, segmentation, counting, and growth-stage classification. demonstrated the application of YOLO-based frameworks for real-time capsicum detection, segmentation, growth-stage identification, and mobile deployment, highlighting the growing convergence between computer vision and precision horticultural management.

Remote sensing observations from satellite and UAV platforms are rarely interpreted in their raw spectral form. Instead, they are systematically transformed into standardized biophysical and agroecological indicators that provide an interpretable description of crop condition, canopy functioning, and landscape-scale processes. Optical satellite sensors capture surface reflectance across visible, near-infrared, and shortwave infrared wavelengths, enabling the development of widely adopted vegetation indices. Metrics such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Red Edge Index (NDRE), Photochemical Reflectance Index (PRI), and Normalized Difference Moisture Index (NDMI), are routinely used to characterize crop vigour, biomass accumulation, canopy development, and leaf water content throughout the growing season (). Beyond spectral greenness, structural and radiative parameters offer a more process-oriented perspective on crop growth. Leaf Area Index (LAI) characterises canopy structure by quantifying leaf surface area available for light interception, while the fraction of absorbed Photosynthetically Active Radiation (fAPAR) represents the efficiency with which vegetation captures incoming solar energy. These v