Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices

Aug 4, 2026

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Enhancing agricultural machinery field efficiency improves farm management outcomes by optimizing available resources to lower costs, raise productivity, and support sustainability. Field efficiency is shaped by multiple unpredictable, stochastic factors tied to the variability of field layouts and operating conditions. The study simplifies field efficiency estimation by training machine learning regression models on farm management information system data spanning diverse field areas and shapes, working patterns, and machine parameters. A gradient-boosting approach achieved a mean R2 of 0.931 using only geometric field indices, while reducing computation time by 73.4% versus an analytical method and maintaining strong predictive performance across representative fields in Europe and North America.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Field efficiency factors in agricultural operations

Role of field geometry and movement optimization

Machine learning approach for simplified estimation

POLITECNICO DI TORINO Repository ISTITUZIONALE Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices Original Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices / Asiminari, Gavriela; Benos, Lefteris; Kateris, Dimitrios; Busato, Patrizia; Achillas, Charisios; Grøn Sørensen, Claus; Pearson, Simon; Bochtis, Dionysis. -In: AGRIENGINEERING. -ISSN 2624-7402. -7:3(2025) . [10 .3390/agriengineering7030075] Availability: This version is available at: 11583/3000491 since: 2025-05-29T10:16:43Z Publisher: Multidisciplinary Digital Publishing Institute (MDPI) Published DOI:10.3390/agriengineering7030075 Terms of use: This article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository Publisher copyright (Article begins on next page) 21 February 2026 Article Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices Gavriela Asiminari 1,2,3, Lefteris Benos 3, Dimitrios Kateris 3, *, Patrizia Busato 4, Charisios Achillas 1, Claus Grøn Sørensen 5, Simon Pearson 6 and Dionysis Bochtis 2,3, * Academic Editors: Sotirios K. Goudos, Shaohua Wan and Achilles Boursianis Received: 26 December 2024 Revised: 18 February 2025 Accepted: 6 March 2025 Published: 10 March 2025 Citation: Asiminari, G.; Benos, L.; Kateris, D.; Busato, P.; Achillas, C.; Grøn Sørensen, C.; Pearson, S.; Bochtis, D. Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices. AgriEngineering 2025, 7, 75 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)agriengineering7030075 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) . 1 Department of Supply Chain Management, International Hellenic University, 57001 Thessaloniki, Greece; [g.asiminari@certh.gr](g.asiminari@certh.gr) (G.A.); [c.achillas@ihu.edu.gr](c.achillas@ihu.edu.gr) (C.A.) 2 farmB Digital Agriculture S.A., 17th November 79, 55534 Thessaloniki, Greece 3 Institute for Bio-Economy and Agri-Technology (IBO), Centre for Research and Technology–Hellas (CERTH), 6th Km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece; [e.benos@certh.gr](e.benos@certh.gr) 4 Interuniversity Department of Regional and Urban Studies and Planning (DIST), Polytechnic of Turin, Viale Mattioli 39, 10125 Torino, Italy; patrizia.busato@polito.it 5 Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark; [claus.soerensen@ece.au.dk](claus.soerensen@ece.au.dk) 6 Lincoln Institute for Agri-Food Technology (LIAT), University of Lincoln, Lincoln LN6 7TS, UK; [spearson@lincoln.ac.uk](spearson@lincoln.ac.uk) * Correspondence: [d.kateris@certh.gr](d.kateris@certh.gr) (D.K.); [d.bochtis@certh.gr](d.bochtis@certh.gr) (D.B.) Abstract: Enhancing agricultural machinery field efficiency offers substantial benefits for farm management by optimizing the available resources, thereby reducing cost, maximizing productivity, and supporting sustainability. Field efficiency is influenced by several unpredictable and stochastic factors that are difficult to determine due to the inherent variability in field configurations and operational conditions. This study aimed to simplify field efficiency estimation by training machine learning regression algorithms on data generated from a farm management information system covering a combination of different field areas and shapes, working patterns, and machine-related parameters. The gradient-boosting regression-based model was the most effective, achieving a high mean R2 valu

FAQs

What is the main goal of the study?

To simplify field efficiency estimation by training machine learning regression algorithms using data from a farm management information system.

Which model performed best and what accuracy was reported?

A gradient-boosting regression model was most effective, achieving a mean R2 value of 0.931 for predicting field efficiency.

How does the proposed method compare with an analytical approach?

It substantially reduces computational time, with an average reduction of 73.4%, while maintaining strong predictive performance for indicative fields.

Aug 4, 2026

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