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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 (R2<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 (R2<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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Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques
(2024-11-01)
Veerasakulwat, Siramet
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Sitorus, Agustami
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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.
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Machine learning-based prediction of nutritional status in oil palm leaves using proximal multispectral images
(2022-07-01)
Chungcharoen, Thatchapol
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Donis-Gonzalez, Irwin
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Phetpan, Kittisak
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Udompetaikul, Vasu
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Sirisomboon, Panmanas
This study evaluated the application of proximal multispectral images accompanied by 4 machine learning approaches for estimating the nutritional status of oil palm leaves. The image responded for five bands: blue, green, red, red edge, and near-infrared regions with a center wavelength of 475, 560, 668, 717, and 840 nm. Average and standard deviation (SD) values from the leaf pixels of each band were extracted, obtaining 5 average and 5 SD values from 5 bands. Thirty-four vegetation variables were generated based on those average and SD values. In total, forty-four variables consisted of 10 average-and SD-based features, and 34 vegetation variables were used as the input candidates for analyses against 10 target variables: nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), manganese (Mn), zinc (Zn), boron (B), and chlorophyll (SPAD). No significant input came out for modeling with P and Zn based on the stepwise selection. Therefore, 8 nutritional models were proposed in this study. A training set with 50 samples was used to be modeled for each target, and a test set with 15 samples was employed to evaluate the models' performances. Based on random forest (RF), support vector regression (SVR), partial least square regression (PLSR), and artificial neuron network (ANN) applied to be modeled, the models for chlorophyll, N, and Ca predictions were acceptable for screening, and those for K and Mg predictions were acceptable for rough screening. The chlorophyll model developed based on the RF had the predictive statistics in terms of coefficient of determination for prediction (r2), root mean square error of prediction (RMSEP), and standard error of prediction (SEP) of 0.752, 5.46 SPAD, and 5.65 SPAD, respectively. The other 2 screening models developed based on SVR and RF for N and Ca, respectively, gave the performances with the r2, RMSEP, and SEP ranging from 0.655 to 0.718, 0.12 to 0.17%, and 0.12 to 0.18%, respectively. In the case of the 2 rough screening models established using the RF algorithm, the predictive statistics ranged from 0.496 to 0.530 for the r2 and 0.07–0.16% for both RMSEP and SEP. In this study, the Fe, Mn, and B models had poor results presenting the range of r2, RMSEP, and SEP of 0.308–0.491, 2.39–72.9 ppm, and 2.45–62.8 ppm, respectively. Based on the results, this study confirmed that the proximal multispectral information of oil palm leaves had enough significance to account for the status of chlorophyll and macro-nutrients: N, K, Ca, and Mg in the leaves.
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Evaluation of informative spectral wavelengths for estimating soluble solids content in sugarcane billets
(2022-02-22)
Udompetaikul, Vasu
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Phetpan, Kittisak
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Sirisomboon, Panmanas
This study proposed individual spectral wavelengths significant to estimate soluble solids content (SSC) in sugarcane billets moving on the conveyor. At the same time, an all-in-one quality and yield monitor using those wavelengths for a sugarcane harvester was also proposed. Seven wavelengths, 475, 560, 668, 717, 755, 840, and 890 nm, were arranged into three groups for modeling. Group 1, consisting of 475, 560, 668, 717, and 840 nm, was based on the spectral responses of a commercial multispectral camera, while group 2 (717 and 840 nm) was based on the invisible (RedEdge and near-infrared or NIR) responses of the camera. For group 3, two sugar-related wavelengths at 755 and 890 nm were selected as the candidates for modeling. Partial least squares regression (PLSR) was employed to model those three groups with corresponding soluble solids content (SSC). The results showed that the developed models based on two sugar-related wavelengths at 755 and 890 nm provided the best performance, explaining 80.2 % of the variance in the SSC and displaying a root mean square error of calibration (RMSEC) of 0.32 ºBrix. The predictive performance had the root mean square error of prediction (RMSEP) of 0.33 ºBrix. This finding confirmed the effectiveness of the sugar wavelengths and conveyed the possibility to develop the sugarcane quality and yield monitor.
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Development of the partial least-squares model to determine the soluble solids content of sugarcane billets on an elevator conveyor
(2021-01-01)
Udompetaikul, Vasu
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Phetpan, Kittisak
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Sirisomboon, Panmanas
This study aimed to determine the optimum multivariate model for monitoring the soluble solids content (SSC) of sugarcane billets being transferred on a conveyor. The study covered two main issues: the exploration of an appropriate spectral range (450–900 nm versus 700–900 nm) and the assessment of the influence of different levels of cane billets on an elevator via modelling to predict the SSC values. Partial least squares regression (PLSR) was used for model development. Modelling using the range of 450–900 nm employed 4 latent variables (LVs) and showed the coefficient of determination (R2) and root mean squares error of prediction (RMSEP) of 0.83 and 0.29 °Brix, respectively. This caused the model established using the range of 700–900 nm, employed 3 LVs and provided the R2 and RMSEP values of 0.81 and 0.31 °Brix, respectively, seems more appropriate. In case of assessing the different cane levels on the conveyor, the outcomes presented model performance of the full and half cane levels in predicting half and full cane datasets with R2 and RMSEP of 0.52 and 0.55 °Brix and 0.53 and 0.48 °Brix, respectively. This showed that the different levels affected the SSC predictive accuracy of the model. The combined model was developed to cover variations of this difference and was used to predict two external sets. The predictions of ninety and thirty samples that were collected from the same and different growing seasons as the samples for the modelling presented the R2, RMSEP and RPD of 0.70, 0.42 °Brix and 1.83 and 0.56, 0.42 °Brix and 2.00, respectively.

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