Integration of Multi-Dimensional Geospatial Data for Crop Recommendation and Precision Agriculture

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Tejal Daivajna; Sidharth Manikandan; Shreepaada MC; Soumyadeep Saha

Integration of Multi-Dimensional Geospatial Data for Crop Recommendation and Precision Agriculture

Authors: Tejal Daivajna, Sidharth Manikandan, Shreepaada MC, Soumyadeep Saha

Unique Paper ID: 174379
Volume: 11
Issue: 10
PageNo: 4496-4504

Keywords: Crop recommendation system geospatial analysis machine learning normalized difference vegetation index precision agriculture remote sensing soil nutrient mapping sustainable farming

Abstract:
Precision agriculture represents a paradigm shift in farming methodologies, leveraging geospatial technologies to optimize crop selection and management practices. This paper presents AgroVision, an integrated approach to crop recommendation systems utilizing multi-dimensional geospatial datasets including Normalized Difference Vegetation Index (NDVI), soil physicochemical parameters, and meteorological variables. The methodology encompasses acquisition of satellite imagery from MODIS using Google Earth Engine, soil data from ISRIC SoilGrids, and climatological parameters from NASA POWER datasets. These heterogeneous data streams undergo rigorous preprocessing, normalization, and integration prior to implementation within a multi-criteria decision support framework. The developed system demonstrates significant efficacy in generating spatially-explicit crop suitability maps with validation accuracy of 95.9% across diverse agro-ecological zones. Comparative analysis reveals a 23% improvement in prediction accuracy over traditional methods and potential yield improvements of 18-27% when recommendations are implemented. This research contributes to agricultural sustainability by enabling data-driven decision-making that optimizes resource utilization while maximizing productivity and economic returns, thereby addressing critical challenges in contemporary agricultural systems.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 10
PageNo: 4496-4504

Integration of Multi-Dimensional Geospatial Data for Crop Recommendation and Precision Agriculture

Available:https://ijirt.org/article?manuscript=174379

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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