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

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(3), P. 75 - 75

Published: March 10, 2025

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 is influenced several unpredictable stochastic factors that are difficult to determine due inherent variability in configurations operational conditions. This study aimed simplify estimation training machine learning regression algorithms on data generated from a information system covering combination of different areas shapes, working patterns, machine-related parameters. The gradient-boosting regression-based model was most effective, achieving high mean R2 value 0.931 predicting efficiency, taking into account only basic geometric indices. developed showed also strong predictive performance indicative fields located Europe North America, considerably computational time an average 73.4% compared corresponding analytical approach. Overall, results this highlight potential simplifying prediction without requiring detailed knowledge plethora variables associated with operations. can be particularly valuable farmers who need make informed decisions about resource allocation planning.

Language: Английский

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A Study on the Spatial Perception and Inclusive Characteristics of Outdoor Activity Spaces in Residential Areas for Diverse Populations from the Perspective of All-Age Friendly Design

Buildings, Journal Year: 2025, Volume and Issue: 15(6), P. 895 - 895

Published: March 13, 2025

With the transformation of urban development patterns and profound changes in population structure China, outdoor activity spaces residential areas are facing common issues such as obsolete infrastructure, insufficient barrier-free facilities, intergenerational conflicts, which severely impact residents’ quality life hinder high-quality development. Guided by principles all-age friendly inclusive design, this study innovatively integrates eye-tracking multi-modal physiological monitoring technologies to collect both subjective objective perception data different age groups regarding through human factor experiments empirical interviews. Machine learning methods utilized analyze data, uncovering differentiated response mechanisms among diverse clarifying characteristics these spaces. The findings reveal that: (1) Common Demands: All prioritize spatial features unobstructed views, adequate space, landscapes, proximity accessibility, smooth pavement surfaces, with similar levels concern. (2) Differentiated Characteristics: Children place greater emphasis on environmental familiarity children’s play while middle-aged elderly show heightened concern for efficient parking management, facilities. (3) Technical Validation: Heart Rate Variability (HRV) was identified core indicator inclusivity dimensionality reduction using Self-Organizing Maps (SOM), Extra Trees model demonstrated superior performance prediction. By integrating multi-group standardizing experimental environments, applying intelligent mining, achieves fusion in-depth analysis, providing theoretical methodological support precisely optimizing advancing communities.

Language: Английский

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Bayesian optimization and neural network prediction of graphene-titanium fiber metal laminates

Materials Today Communications, Journal Year: 2025, Volume and Issue: 45, P. 112355 - 112355

Published: April 1, 2025

Language: Английский

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Comparative study on filter and wrapper methods for selecting ground motion intensity measures in machine learning-based seismic damage assessment of urban reinforced concrete frame structures

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112515 - 112515

Published: April 1, 2025

Language: Английский

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Prediction of residual stresses in GFRP strips under wind-sand erosion by interpretable machine learning methods: feature engineering and SHAP analysis

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(6)

Published: April 15, 2025

Language: Английский

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Evaluation of the mechanical damage of recycled aggregate concrete by acoustic emission

Structures, Journal Year: 2025, Volume and Issue: 76, P. 108948 - 108948

Published: April 18, 2025

Language: Английский

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A Novel Hybrid Machine Learning Framework for Engineered Cementitious Composites Strain Capacity Prediction Enhanced by K-Means Stratified Sampling

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112570 - 112570

Published: April 1, 2025

Language: Английский

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Hybrid machine learning approach with FHO algorithm and WERCS method for predicting fire resistance of timber columns

Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112679 - 112679

Published: April 1, 2025

Language: Английский

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Stress prediction model of oil and gas pipeline based on magnetic-force coupling and machine learning

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117818 - 117818

Published: May 1, 2025

Language: Английский

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An optimized machine-learning tool to predict heat treatment response of hot-work tool steels

Results in Engineering, Journal Year: 2025, Volume and Issue: 26, P. 105260 - 105260

Published: May 9, 2025

Language: Английский

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