On an intelligent system to plan agricultural operations

This paper focuses on the design and implementation of an intelligent system for planning operations of an agricultural robot. The aim of this intelligent system is to choose automatically the appropriate resources to execute certain agricultural operations, based on the user's preferences, as well as to define and schedule their sequence. The practical background is discussed, with descriptions of the solution space of agricultural applications and the way they can be restrained, through certain assumptions and decisions for minimizing the computing effort. The effectiveness of the intelligent system is demonstrated by comparing specific KPIs that have been calculated in the solution which would probably have been selected by a farmer and the solution proposed by the system, in different scenarios.

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Publication Details

Journal
Smart Agricultural Technology
Published
2024-12-11
DOI
https://doi.org/10.1016/j.atech.2024.100707
Citations
7
Primary Topic
Advanced Scientific Research Methods
Type
article
Field-Weighted Citation Impact
2.44

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article

On an intelligent system to plan agricultural operations

Ard Nieuwenhuizen, Sotiris Makris, Panagiotis Karagiannis, Panagiotis Stylianos Kotsaris et al.
7 citations
Smart Agricultural Technology
Advanced Scientific Research Methods
2.44
article

On an intelligent system to plan agricultural operations

Ard Nieuwenhuizen, Sotiris Makris, Panagiotis Karagiannis, Panagiotis Stylianos Kotsaris, George Michalos, F.K. van Evert, George Chryssolouris, Panagiotis Vasilaros, Spyros Fountas, Vangelis Xanthakis
article en
7 citations

Abstract

This paper focuses on the design and implementation of an intelligent system for planning operations of an agricultural robot. The aim of this intelligent system is to choose automatically the appropriate resources to execute certain agricultural operations, based on the user's preferences, as well as to define and schedule their sequence. The practical background is discussed, with descriptions of the solution space of agricultural applications and the way they can be restrained, through certain assumptions and decisions for minimizing the computing effort. The effectiveness of the intelligent system is demonstrated by comparing specific KPIs that have been calculated in the solution which would probably have been selected by a farmer and the solution proposed by the system, in different scenarios.

Smart Agricultural TechnologyVol. 10
Agricultural University of Athens (GR), University of Patras (GR), Centre for BioSystems Genomics (NL), Wageningen University & Research (NL)
European Commission, Horizon 2020 Framework Programme
Zero hunger
Openalex Percentile: Top 12%
Advanced Scientific Research Methods
2.44
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