An Innovative Process Chain for Precision Agriculture Services

In this work, an innovative process chain is set up for the regular provision of fertilization consultation services to farmers for a variety of crops, within a precision agriculture framework. The central hub of this mechanism is a geographic information system (GIS), while a 5 × 5 m point grid is the information carrier. Potential data sources include soil samples, satellite imagery, meteorological parameters, yield maps, and agronomic information. Whenever big data are available per crop, decision-making is supported by machine learning systems (MLSs). All the map data are uploaded to a farm management information system (FMIS) for visualization and storage. The recipe maps are transmitted wirelessly to variable rate technologies (VRTs) for applications in the field. To a large degree, the process chain has been automated with programming at many levels. Currently, four different service modules based on the new process chain are available in the market.

Authors

Publication Details

Journal
Computers
Published
2025-06-13
DOI
https://doi.org/10.3390/computers14060234
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

An Innovative Process Chain for Precision Agriculture Services

Miltiadis Iatrou, Spiros Mourelatos, Christos Karydas
Computers
Smart Agriculture and AI
article

An Innovative Process Chain for Precision Agriculture Services

Miltiadis Iatrou, Spiros Mourelatos, Christos Karydas
article en

Abstract

In this work, an innovative process chain is set up for the regular provision of fertilization consultation services to farmers for a variety of crops, within a precision agriculture framework. The central hub of this mechanism is a geographic information system (GIS), while a 5 × 5 m point grid is the information carrier. Potential data sources include soil samples, satellite imagery, meteorological parameters, yield maps, and agronomic information. Whenever big data are available per crop, decision-making is supported by machine learning systems (MLSs). All the map data are uploaded to a farm management information system (FMIS) for visualization and storage. The recipe maps are transmitted wirelessly to variable rate technologies (VRTs) for applications in the field. To a large degree, the process chain has been automated with programming at many levels. Currently, four different service modules based on the new process chain are available in the market.

ComputersVol. 14(6)
Openalex Percentile: Top 90%
Smart Agriculture and AI
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