Advancements in GIS for precision agriculture: Enhancing soil management and crop yield prediction

Introduction

The global agricultural sector faces the dual challenge of maximising production to feed a growing population while minimising environmental degradation. Traditional farming methods, which often treat large fields as homogeneous units, result in the inefficient allocation of resources such as water, fertilisers, and pesticides. Precision Agriculture (PA) represents a paradigm shift away from this uniform approach, advocating for management practices that account for inter-field and intra-field variability [1,2] . At the heart of this transformation lies the Geographic Information System (GIS), a technology that captures, stores, analyses, and displays spatially referenced data. GIS serves as the digital backbone of precision agriculture, enabling farmers and agronomists to visualise complex relationships between soil types, topography, weather patterns, and crop performance [2,3] . The evolution of GIS in agriculture has moved beyond simple mapping to become a sophisticated decision support system. Early applications focused primarily on digitising field boundaries and basic land-use planning [3,4] . However, contemporary GIS frameworks integrate real-time data from Global Positioning Systems (GPS), satellite imagery, and on-ground sensors to facilitate dynamic decision-making [4,5] . This integration allows for the creation of multilayered spatial databases that inform every stage of the crop cycle, from pre-sowing soil analysis to post-harvest yield assessment [5,6] . The ability to overlay diverse datasets-such as soil nutrient status, moisture levels, and pest infestation rates-empowers stakeholders to implement site-specific management strategies that enhance productivity and sustainability [3,6] .

Geospatial Frameworks for Soil Management

Soil is the fundamental asset of agricultural production, yet its physical and chemical properties exhibit significant spatial variability [6,7] . GIS facilitates detailed characterisation of this variability through geostatistical techniques such as kriging and inverse distance weighting, which interpolate soil sample data to create continuous surface maps [7,8] . These digital soil maps provide critical insights into the spatial distribution of macronutrients (nitrogen, phosphorus, potassium) and micronutrients, as well as pH levels and organic matter content. By visualising these parameters, farmers can identify nutrient-deficient zones and areas with optimal fertility, enabling the formulation of precise nutrient management plans [8, 9] . One of the most practical applications of GIS in soil management is Variable Rate Technology (VRT). VRT systems use prescription maps generated in a GIS environment to automatically adjust input application rates as farm machinery moves across the field [9,10] . For instance, a tractor equipped with a GPS receiver and a VRT controller can apply higher fertiliser doses in nutrient-poor areas while reducing application in nutrient-rich areas [10,11] . This targeted approach not only maximises crop response but also minimises nutrient runoff, thereby reducing the risk of eutrophication in nearby water bodies [11,12] . Research indicates that GIS-guided VRT can lead to substantial cost savings on fertilisers and significantly lower the environmental footprint of intensive agriculture [12,13] . Beyond nutrient management, GIS is instrumental in soil conservation efforts. Digital Elevation Models (DEMs) derived from satellite data or drone surveys enable precise analysis of terrain attributes, such as slope and aspect [13,14] . These topographic layers are essential for modelling soil erosion potential and identifying areas susceptible to waterlogging. By integrating topographic data with soil erodibility indices, land managers can design effective conservation structures, such as contour bunds and terraces, to mitigate soil loss [14,15] . In regions prone to degradation, GIS-based monitoring tracks changes in soil health over time, facilitating timely interventions to restore productivity [15,16] . Creation of Variable Rate Technology (VRT) prescription maps to optimise fertiliser use [16,17] .

Erosion Control Digital Elevation Models (DEM)

Topography, Slope, Rainfall data Identification of high-risk erosion zones for constructing contour bunds or terraces [17,18] . Moisture Conservation Soil Moisture Mapping Interpolated sensor data; Thermal imagery Precision irrigation scheduling to prevent waterlogging and reduce water wastage [18,19] . Salinity Management Spectral Mapping Electrical Conductivity (EC) surveys Delineation of saline patches for gypsum application or specific crop selection [19,20] .

Predictive Modelling for Crop Yield

Predicting crop yield before harvest is crucial to ensuring food security, optimising logistics, and stabilising market prices. GIS advances enable predictive modelling by synthesising data from multiple sources, primarily remote sensing platforms [20,21] .

Satellite imagery provides temporal data on crop phenology, which is analysed using Vegetation Indices (VIs), such as the Normalised Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). These indices correlate strongly with biomass accumulation and plant vigour [21,22] . GIS environments enable these raster datasets to be aggregated and analysed alongside historical yield records to identify trends and anomalies [22,23] . The integration of Machine Learning (ML) algorithms with GIS has further refined yield forecasting capabilities [24,25] .

Traditional regression models often fail to capture the complex, non-linear interactions between crop growth and environmental factors. In contrast, ML algorithms such as Random Forest and Support Vector Machines can process vast multidimensional datasets-including soil properties, weather variables, and satellite spectral data-to generate highly accurate yield predictions [26,27] . For example, studies integrating the maximum EVI during the growing season with ML regression models have demonstrated high predictive accuracy for wheat and rice yields, enabling forecasts weeks before harvest [28,29] . Synthetic Aperture Radar (SAR) technology has addressed a significant limitation of optical remote sensing: cloud cover. In monsoon-dependent agricultural regions, optical satellites often fail to acquire clear images during critical growth stages [30,31] . SAR sensors, which can penetrate cloud cover, provide reliable data on crop structure and growth. When assimilated into crop simulation models within a GIS framework, SAR data significantly reduces uncertainties in yield estimation [32,33] . This capability is particularly vital for crops like rice, which are grown in areas where flooding and cloud cover are common during the growing season.

Technological Synergies

The efficacy of GIS in precision agriculture is amplified by its convergence with complementary technologies [36,37] . Uncrewed Aerial Vehicles (UAVs), or drones, have revolutionised data acquisition by providing high-resolution, low-altitude imagery that bridges the gap between satellite data and ground observations. Drones equipped with multispectral and thermal cameras can detect water stress and nutrient deficiencies at the individual plant level [37,38] . This hyper-local data is ingested into GIS platforms to generate ultra-high-resolution prescription maps for spot spraying or irrigation, representing a leap in precision over satellite-based approaches. The Internet of Things (IoT) further enhances the temporal resolution of agricultural GIS. Networks of wireless soil moisture sensors and weather stations transmit real-time data to central GIS repositories [39,40] . This continuous stream of information allows for dynamic irrigation scheduling and immediate pest alerts. For instance, a rise in humidity and temperature detected by IoT sensors can trigger a GIS-based risk model for fungal diseases, prompting preventative fungicide application only in vulnerable sectors of the field. This synergy between real-time sensing and spatial analysis transforms farms into responsive, data-driven ecosystems [41,42] .

Regional Implementation and Case Studies: The Indian Context

India presents a unique context for the application of GIS in agriculture, characterised by diverse agro-climatic zones and a predominance of smallholder farmers. Both government initiatives and private sector innovation have catalysed the adoption of geospatial technologies in India. For instance, the use of GIS in the Pradhan Mantri Fasal Bima Yojana (PMFBY), India's flagship crop insurance scheme, has streamlined yield estimation and claims settlement [43,44] . By utilising satellitederived indices and Crop Cutting Experiments (CCEs) coordinated via mobile GIS applications, the government has improved transparency and the speed of insurance payouts. Specific regional studies highlight the versatility of GIS in Indian agriculture. In the Bhandara district of Maharashtra, GISsupported initiatives under the state' s organic farming mission have helped identify clusters suitable for natural farming [45,46] . By mapping soil organic carbon and land use patterns, authorities could target training and resource distribution to farmers most likely to succeed in transitioning away from chemical inputs. Similarly, in Bangalore Urban District, NDVI mapping has been employed to monitor crop health and urbanisation pressures, providing policymakers with data to protect prime agricultural land from encroachment [46,47] . Furthermore, research in the Nimar region of central India has utilised GIS to assess the adoption of organic conservation agriculture. The spatial analysis revealed that adoption rates were heavily influenced by proximity to markets and water sources, which were visualised and analysed using geospatial tools [47,48] . In paddy cultivation, studies have shown that GISbased site-specific nutrient management (SSNM) significantly increases nitrogen use efficiency compared to blanket fertiliser recommendations typically followed by Indian farmers [48,49] . These examples underscore that while landholding sizes in India are small, the aggregation of small plots into GIS-managed clusters or cooperative frameworks allows for the practical application of precision agriculture technologies [50,51] .

Challenges and Future Trajectories

Despite the evident benefits, the widespread adoption of GIS in precision agriculture faces substantial hurdles. A primary challenge is data quality and interoperability. Agricultural data is often siloed in proprietary formats, making it challenging to integrate data from different machinery manufacturers and software providers [52,53] . This fragmentation hampers the creation of unified farm management systems. Additionally, the technical complexity of GIS software necessitates a skilled workforce [54,55] . In many developing regions, the lack of trained personnel capable of interpreting spatial data remains a bottleneck, limiting GIS applications to research institutions rather than bringing them to the farm level [55,56] . Data privacy and ownership also emerge as critical concerns. As farms become increasingly digitised, vast amounts of sensitive data regarding yield, soil health, and financial inputs are generated [57,58] . Farmers are often wary of sharing this data with agribusiness corporations or technology providers, fearing market manipulation or a loss of autonomy. Establishing robust data governance frameworks that prioritise farmer rights is essential for building trust and ensuring the ethical scaling of digital agriculture [54,56] . Looking forward, the future of GIS in agriculture lies in automation and artificial intelligence. We are moving toward autonomous farming systems where GIS data directly drives robotic machinery for planting, weeding, and harvesting without human intervention. The integration of edge computing will enable data processing directly on sensors or drones, reducing latency and enabling instant decision-making in areas with poor internet connectivity. Moreover, the democratisation of GIS through user-friendly mobile applications will likely accelerate adoption among smallholder farmers, allowing them to access satellite insights via smartphones [55,59] .

Conclusion

The integration of Geographic Information Systems into precision agriculture represents a pivotal advancement in the quest for sustainable food systems. By providing the tools to measure, map, and manage spatial variability, GIS enables a transition from resource-intensive conventional farming to knowledge-intensive precision management. The capabilities of GIS in detailed soil property mapping, erosion control, and highaccuracy crop yield prediction have been validated across diverse agricultural landscapes, from the mechanised farms of the West to the smallholder plots of India. While challenges regarding data interoperability, cost, and technical capacity persist, the continued convergence of GIS with AI, IoT, and remote sensing promises to lower these barriers. Ultimately, the successful deployment of geospatial technologies will not only enhance farm profitability but also ensure the environmental resilience required to sustain global agricultural production in a changing climate.

References

1. Raihan A. A systematic review of Geographic Information Systems (GIS) in agriculture for evidence-based decision making and sustainability. Glob Sustain Res. 2024;3(1):124.
2. Saha S, Kucher OD, Utkina AO, Rebouh NY. Precision agriculture for improving crop yield predictions: a literature review. Front Agron. 2025;7:1566201.
3. Suleymanov A, Abakumov E, Suleymanov R, Gabbasova I, Komissarov M. The soil nutrient digital mapping for precision agriculture cases in the trans-ural steppe zone of Russia using topographic attributes. ISPRS Int J Geo-Inf. 2021;10(4):243.
4. Katkani D, Babbar A, Mishra VK, Trivedi A, Tiwari S, Kumawat RK. A review on applications and utility of remote sensing and geographic information systems in agriculture and natural resource management. Int J Environ Clim Change. 2022;12(4):1-18.
5. Mani PK, Mandal A, Biswas S, Sarkar B, Mitran T, Meena RS. Remote sensing and geographic information system: a tool for precision farming. In: Geospatial technologies for crops and soils. Singapore: Springer Singapore; 2020. p. 49111.
6. Ge Y, Thomasson JA, Sui R. Remote sensing of soil properties in precision agriculture: a review. Front Earth Sci. 2011;5(3):229-38.
7. Brisco B, Brown RJ, Hirose T, McNairn H, Staenz K. Precision agriculture and the role of remote sensing: a review. Can J Remote Sens. 1998;24(3):315-27.
8. Shaheb MR, Sarker A, Shearer SA. Precision agriculture for sustainable soil and crop. In: Soil Science-Emerging Technologies, Global Perspectives and Applications. 2022. p. 49.
9. Surendran U, Nagakumar KCV, Samuel MP. Remote sensing in precision agriculture. In: Digital agriculture: A solution for sustainable food and nutritional security. Cham: Springer; 2024. p. 201-23.
10. Trivedi A, Rao KVR, Rajwade Y, Yadav D, Verma NS. Remote sensing and geographic information system applications for precision farming and natural resource management. Indian J Ecol. 2022;49(5):1624-33.
11. Sishodia RP, Ray RL, Singh SK. Applications of remote sensing in precision agriculture: a review. Remote Sens. 2020;12(19):3136.
12. Gorai T, Yadav PK, Choudhary GL, Kumar A. Site-specific crop nutrient management for precision agriculture-A review. Curr J Appl Sci Technol. 2021;40:37-52.
13. Raza I, Zubair M, Zaib M, Khalil MH, Haidar A, Sikandar A, et al. Precision nutrient application techniques to improve soil fertility and crop yield: a review with future prospect. Int Res J Educ Technol. 2023;5.
14. Andreo V. Remote sensing and geographic information systems in precision farming. Instituto de Altos Estudios Espaciales " Mario Gulich" -CONAE/UNC Facultad de Matematica. Astronomia y Física-UNC; 2013.
15. Chen X, Zhang H, Wong CUI. Dynamic monitoring and precision fertilization decision system for agricultural soil nutrients using UAV remote sensing and GIS. Agriculture. 2025;15(15):1627.
16. Kingsley J, Lawani SO, Esther AO, Ndiye KM, Sunday OJ, Penížek V. Predictive mapping of soil properties for precision agriculture using geographic information system (GIS) based geostatistics models. Mod Appl Sci. 2019;13(10):60-77.
17. Markoski M, Arsov S, Mitkova T, Janeska-Stamenkovska I. The benefit of GIS technologies and precision agriculture principles in soil nutrient management for agricultural crop production. Bulg J Agric Sci. 2015;21(3):554-9.
18. Udoumoh UI, Ikrang EG, Ehiomogue PO. Precision farming and fertilizer recommendation using geographic information system (GIS): A review. Int J Agric Earth Sci. 2021;7(2):1-8.
19. Patil VC, Maru A, Shashidhara GB, Shanwad UK. Remote sensing, geographical information system and precision farming in India: Opportunities and challenges. In: Proc Third Asian Conf Info Technol Agric. 2002 Oct;26-28.
20. Thenkabail PS, Lyon JG, Huete A. Hyperspectral remote sensing of vegetation. CRC Press; 2016.
21. Justice C, et al. Earth Observation for Food Security and Agriculture: GEOGLAM. Remote Sens Environ. 2013;136:3-13.
22. Xiong J, et al. Automated cropland mapping using MODIS time-series data: Global trends. Remote Sens Environ. 2017;204:187-201.
23. Velpuri NM, et al. Drought monitoring in Africa with satellite and climate data. J Hydrol. 2016;543:524-37.
24. Srivastava SK, et al. FASAL: Forecasting Agricultural output using Space, Argo-meteorology, and Land-based observations. ISRO Technical Report. 2016.
25. Sahoo RN. Applications of Geoinformatics in Precision Agriculture for Enhancing Input Use Efficiency. Training Manual. 2012 Sep 4:103.
26. Sharma S. Precision Agriculture: Reviewing the Advancements Technologies and Applications in Precision Agriculture for Improved Crop Productivity and Resource Management. Rev Food Agric. 2023;4(2):45-9.
27. Verma P, Chauhan A, Ladon T. Site specific nutrient management: A review. J Pharmacogn Phytochem. 2020;9(5S):233-6.
28. Swathy R, Geethalakshmi V, Pazhanivelan S, Kannan P, Annamalai S, Hwang S. Real-time nitrogen monitoring and management to augment N use efficiency and ecosystem sustainability-A review. J Hazard Mater Adv. 2024 Sep 15:100466.
29. Yadav N, Sidana N. Precision Agriculture Technologies: Analysing the Use of Advanced Technologies, Such as Drones, Sensors, and GPS, In Precision Agriculture for Optimizing Resource Management, Crop Monitoring, and Yield Prediction. J Adv Zool. 2023 Sep 4;44.
30. Patel NR, Pande LM, Roy PS. Precision farming technologies for sustainable agriculture in India-Current status and prospects. Int J Ecol Environ Sci. 2004;30:299308.
31. Roma E, Laudicina VA, Vallone M, Catania P. Application of precision agriculture for the sustainable management of fertilization in olive groves. Agronomy. 2023 Jan 20;13(2):324.
32. Pratyusha P, Sinha G, Maruthi B, Khan O. Site Specific Nutrient Management. Rajat Yadav. 2023;63:149.
33. He L. Variable rate technologies for precision agriculture. In: Encyclopedia of Digital Agricultural Technologies. Cham: Springer; 2023 Oct 12. p. 1533-42.
34. Fulton J, Darr M. GPS, GIS, Guidance, and Variable-rate Technologies for Conservation Management. Precision Conservation: Geospatial Techniques for Agricultural and Natural Resources Conservation. 2017 Dec 15;59:65-81.
35. Meena BR, Jatav HS, Dudwal BL, Kumawat P, Meena SS, Singh VK, et al. Fertilizer recommendations by using different geospatial technologies in precision farming or nanotechnology. Ecosyst Serv. 2022;14:241-57.
36. Amarhetti K. Data-driven harvesting: The role of IoT and precision agriculture in modern farming. Agrifrontline. 2025;1(3):15-18. https://agrifrontline.com/wp-content/uploads/2025/01/4.- DATA-DRIVEN-HARVESTING-1.pdf
37. Singh M, Meena HL, Najmusaqib S. Optimizing harvest management: Strategies for maximizing crop yield and quality. Agrifrontline. 2025;1(3):1-5. https://agrifrontline.com/wp-content/uploads/2025/01/1.- OPTIMIZINGHARVEST.pdf.pagespeed.ce.pPjKCngJDr.pdf
38. Hasan W, Ahmad M. The science of insect behaviour and its impact on agriculture. Agrifrontline. 2025;1(6):5-9.
39. Pedersen SM, Lind KM. Precision agriculture-from mapping to site-specific application. Precision agriculture: Technology and economic perspectives. 2017:1-20.
40. Ahmad SF, Dar AH. Precision farming for resource use efficiency. In: Resources Use Efficiency in Agriculture. 2020. p. 109-35.
41. Pradhan AM. Transcriptomics in plant stress physiology: Bridging genes and phenotypes. Agrifrontline. 2025;1(6):15-19.
42. Getahun S, Kefale H, Gelaye Y. Application of precision agriculture technologies for sustainable crop production and environmental sustainability: A systematic review. Sci World J. 2024;2024(1):2126734.
43. Magrey AH, Belagalla N. Plastic pollution in agriculture: The unsung impact on soil fertility. Agrifrontline. 2025;1(3):24-28. https://agrifrontline.com/wp-content/uploads/2025/02/6.pdf
44. Bhamini K, Kumar A, Magrey AH, Fatima Jafri SK, Najmusaqib S, Raising PL, et al. The role of GIS and GPS in precision fertilization: Optimizing nutrient management for sustainable cropping. Int J Res Agron. 2025;8(2):260-7. DOI: 10.33545/2618060X.2025.v8.i2d.2574
45. Mulla DJ. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosyst Eng. 2013;114(4):358-71.
46. Farid HU, Mustafa B, Khan ZM, Anjum MN, Ahmad I, Mubeen M, et al. An overview of precision agricultural technologies for crop yield enhancement and environmental sustainability. In: Climate Change Impacts on Agriculture: Concepts, Issues and Policies for Developing Countries. 2023. p. 239-57.
47. Dong W, Wu T, Sun Y, Luo J. Digital mapping of soil available phosphorus supported by AI technology for precision agriculture. In: 2018 7th Int Conf Agrogeoinformatics (Agro-geoinformatics). IEEE; 2018. p. 1-5.
48. Nadaf SA, Amara DMK, Patil PL. Distribution of micronutrients in selected pedons of sugarcane-growing Vertisols in Northern Karnataka State of India. Open J Soil Sci. 2022;12:1-12. doi:10.4236/ojss.2022.121001.
49. Amara DMK, Nadaf SA, Saidu DH, Vonu OS, Musa RM, Kamanda PJ, et al. Studies on land resource inventory for agricultural land use planning in Northern Transition Zone of India through remote sensing and GIS techniques. J Geogr Inf Syst. 2021;13:710-728. doi:10.4236/jgis.2021.136039.
50. Nadaf SA, Patil PL, Dasog GS. Identification of micronutrient constraints in sugarcane-growing Vertisols of Northern Transition Zone of Karnataka by GIS technique. Karnataka J Agric Sci. 2015;28(1).