Interpretable Machine Learning for Sugarcane Harvester Performance: A Comparison of Additive and Tree-Based Models on Telematics Data

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dc.contributor.authorKaewkabthong, Apidul
dc.contributor.authorSaijai, Jedsada
dc.contributor.authorSriphuk, Pisitwitthaya
dc.contributor.authorSitorus, Agustami
dc.contributor.authorUdompetaikul, Vasu
dc.date.accessioned2026-08-06T10:55:57Z
dc.date.available2026-08-06T10:55:57Z
dc.date.issued2026-07-01
dc.description.abstractSugarcane 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.
dc.identifier.citationAgriengineering, 8(7), 2026
dc.identifier.doi10.3390/agriengineering8070259
dc.identifier.issn26247402
dc.identifier.other2-s2.0-105045866611
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/18217
dc.sourceAgriengineering
dc.subjectfield efficiency
dc.subjectfield geometry
dc.subjectgeneralized additive model
dc.subjectgrouped cross-validation
dc.subjectharvesting capacity
dc.subjectinterpretable machine learning
dc.subjectJDLink telematics
dc.subjectpartial dependence analysis
dc.subjectprecision agriculture
dc.subjectsugarcane harvester
dc.titleInterpretable Machine Learning for Sugarcane Harvester Performance: A Comparison of Additive and Tree-Based Models on Telematics Data
dc.typeArticle

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