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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
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    Saijai, Jedsada
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    Sriphuk, Pisitwitthaya
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    Sitorus, Agustami
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    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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    Multi-Regime GRU-Scheduled PID Control for Greenhouse VPD Regulation with Real-Time PLC Deployment
    (2026-01-01)
    Wanna, Natthanan
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    Nabudda, Kriengkrai
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    Sitthaphanit, Suphasit
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    Thasnas, Natakorn
    Vapor pressure deficit is a physiologically meaningful climate variable governing plant transpiration and water transport in greenhouse cultivation. However, accurate regulation in evaporative-cooled greenhouses is challenging due to nonlinear temperature-humidity coupling, time-varying dynamics, and rapidly changing solar disturbances. Conventional fixed-gain proportional-integral-derivative controllers, tuned for a single operating condition, often exhibit performance degradation under regime transitions. This study proposes a multi-regime admissible-region-constrained gain-scheduled control framework in which a gated recurrent unit network serves as a supervisory scheduler to generate smooth, near-optimal controller gains for real-time vapor pressure deficit regulation. Control-oriented first-order-plus-dead-time models are identified under representative operating regimes, and an interval-bounded plant ensemble is constructed to capture dynamic variability. Regime-wise optimal gains are obtained offline using hybrid evolutionary optimization and used as supervisory training labels. To support safe deployment, all scheduled gains are constrained within a pre-validated admissible stability region, such that each deployed gain configuration remains inside an offline-validated frozen-time stabilizing set. The controller is implemented on an industrial programmable logic controller and experimentally validated in a full-scale evaporative greenhouse under real environmental disturbances. Comparative multi-day experiments demonstrate improved tracking accuracy, reduced overshoot, smoother actuator behavior, and enhanced disturbance attenuation relative to fixed-gain strategies, while maintaining deterministic real-time execution. The results establish an industrially deployable learning-based gain-scheduling framework for practical greenhouse climate control.
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    Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques
    (2024-11-01)
    Veerasakulwat, Siramet
    ;
    Sitorus, Agustami
    ;
    Udompetaikul, Vasu
    Accurate and rapid discrimination between nodes and internodes in sugarcane is vital for automating planting processes, particularly for minimizing bud damage and optimizing planting material quality. This study investigates the potential of visible-shortwave near-infrared (Vis–SWNIR) spectroscopy (400–1000 nm) combined with machine learning for this classification task. Spectral data were acquired from the sugarcane cultivar Khon Kaen 3 at multiple orientations, and various preprocessing techniques were employed to enhance spectral features. Three machine learning algorithms, linear discriminant analysis (LDA), K-Nearest Neighbors (KNNs), and artificial neural networks (ANNs), were evaluated for their classification performance. The results demonstrated high accuracy across all models, with ANN coupled with derivative preprocessing achieving an F1-score of 0.93 on both calibration and validation datasets, and 0.92 on an independent test set. This study underscores the feasibility of Vis–SWNIR spectroscopy and machine learning for rapid and precise node/internode classification, paving the way for automation in sugarcane billet preparation and other precision agriculture applications.