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Author: search.filters.author.Kaewkabthong, Apidul

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    Interpretable Machine Learning for Sugarcane Harvester Performance: A Comparison of Additive and Tree-Based Models on Telematics Data
    (2026-07-01)
    Kaewkabthong, Apidul
    ;
    Saijai, Jedsada
    ;
    Sriphuk, Pisitwitthaya
    ;
    Sitorus, Agustami
    ;
    Udompetaikul, Vasu
    Sugarcane harvester performance varies substantially with field geometry, crop, and operator factors, yet separating these sources from telematics data while preserving engineering interpretability remains a methodological gap. This study models field efficiency (Eff) and harvesting capacity (C<inf>a</inf>) separately from JDLink telematics, aligning model structure with each target’s response behavior. Operational data covered 105 plots across four seasons (2019/20–2022/23) from three John Deere CH570 chopper harvesters in eastern Thailand. Six engineering-relevant predictors were retained after multicollinearity screening, and linear (MLR), additive nonlinear (GAM), and tree-based models were compared under 5-fold grouped cross-validation by BaseField (87 groups). Eff was assigned to GAM (R<sup>2</sup><inf>CV</inf> = 0.621 ± 0.114) on the basis of its threshold-like response to turning frequency; C<inf>a</inf> was retained for MLR (R<sup>2</sup><inf>CV</inf> = 0.681 ± 0.121), with GAM essentially tied. Train–validation gaps were substantially smaller for additive models (0.096–0.118) than for tuned tree-based candidates (GBR 0.210–0.302, RF 0.322–0.358). Turning frequency (TF) and perimeter-to-area ratio (PAR) were the strongest predictors, and a constant-turn-time partial-out test indicated that TF’s univariate effect on Eff is largely mediated by the time-budget identity. Tactical interventions (path planning, operator training, machine–field allocation) are immediately feasible, although strategic field-layout change remains constrained by smallholder land tenure.
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    Development of an automatic tracking system to determine field efficiency of agricultural machines
    (2018-08-14)
    Tamrai, Natussapol
    ;
    Masarn, Boripun
    ;
    Kaewkabthong, Apidul
    ;
    Lapcharoensuk, Ravipat
    ;
    Sangchan, Songvoot
    Field efficiency of machines tells how efficient the farm machines are operating in the field. Measuring of the field efficiency used to be a tedious and laborious work which is not worth to collect for further operational optimization. The objective of this study was to develop an automatic system for monitoring the field activities and then evaluation of the field efficiency of farm machines. The system consisted of a microcontroller to collect working data including position, speed heading, and working status of the machine. The system was installed on a farm tractor with plowing disc to test on two fields with the same size, but in different traveling directions, i.e., lengthwise and crosswise. The results showed lengthwise operation yielded a higher field efficiency due to less number of turning at headlands. The proposed system allowed to collect necessary information for detailed efficiency evaluation of farm machines. This technique enables further utilization of the operational information and benefit to use in the optimization of the farm works.