Veerasakulwat, Siramet

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Veerasakulwat, Siramet
Alternative Name
Veerasakulwat, S.
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Email
siramet.ve@kmitl.ac.th

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    Item type:Publication,
    Comparison of cost and return for rubber farmers on innovations to increase latex production in Ban Khai District, Rayong Province, Thailand
    (2024-03-01)
    Orsuwan, W.
    ;
    Veerasakulwat, S.
    ;
    Suwanmaneepong, S.
    The findings revealed important distinctions between the two groups of farmers. Rubber farmers who opted for ethylene gas incurred a total cost of 13,973.50 baht, comprising a total fixed cost of 1,202.02 baht and a total variable cost of 12,771.48 baht. These farmers achieved a total return of 17,955 baht per rai. On the other hand, rubber farmers who refrained from using ethylene gas reported a significantly different financial performance. For this group, the total cost amounted to 9,813.49 baht, consisting of a total fixed cost of 1,202.02 baht and a total variable cost of 8,611.48 baht, while the total return per rai amounted to 10,299.57 baht. Statistical analysis indicated that the total variable costs and returns for rubber cultivation significantly differed between the two groups of farmers at a significant level of .05. However, there was no statistically significant difference in the total fixed costs. Extension services and training programmes should be provided to educate farmers on the proper and safe use of ethylene gas to maximise its benefits and minimise potential risks. Furthermore, further research could explore the long-term sustainability and environmental impact of using ethylene gas in rubber cultivation to better understand its overall implications for the industry.
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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.