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

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...

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Bibliographic Details
Published in:AgriEngineering Vol. 7; no. 3; p. 75
Main Authors: Asiminari, Gavriela, Benos, Lefteris, Kateris, Dimitrios, Busato, Patrizia, Achillas, Charisios, Grøn Sørensen, Claus, Pearson, Simon, Bochtis, Dionysis
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Agricultural equipment
Agricultural land
agricultural machinery
Agricultural technology
Agriculture
Algorithms
Computing time
Cost control
coverage path planning
Datasets
Efficiency
Energy consumption
Farm machinery
Farm management
farm management information system (FMIS)
Geographic information systems
Learning algorithms
Machine learning
machine learning regression algorithms
Management information systems
Optimization
precision agriculture
predictive modeling
Productivity
Regression models
Resource allocation
ISSN:2624-7402, 2624-7402
Online Access:Get full text
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Summary: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 value of 0.931 in predicting field efficiency, by taking into account only basic geometric field indices. The developed model showed also strong predictive performance for indicative agricultural fields located in Europe and North America, reducing considerably the computational time by an average of 73.4% compared to the corresponding analytical approach. Overall, the results of this study highlight the potential of machine learning for simplifying field efficiency prediction without requiring detailed knowledge of a plethora of variables associated with agricultural operations. This can be particularly valuable for farmers who need to make informed decisions about resource allocation and operational planning.
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ISSN:2624-7402
2624-7402
DOI:10.3390/agriengineering7030075