KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Saijai, Jedsada

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Develop the Precise Vacuum Seeder for Nursery Plug Tray Sowing by Using the Vacuum Cleaner
    (2025-01-15)
    Yenphayab, Charatchai
    ;
    Saijai, Jedsada
    ;
    Hongwiangjan, Jeerayut
    The manual sowing of vegetable seeds is the general practice of vegetable nurseries in Thailand. It is the slow and labour-intensive process that causes the low production capacity. This study aims to design and develop a vegetable seed sowing machine for 200-cell plug tray plantings to substitute human labor. The prototype utilizes the vacuum cleaner to develop the suction head pressure that simultaneously offers the 200 seeds-planting per tray. The vacuum seeder mechanism is composed of the suction head unit and seeds tray unit. The prototype is operated and controlled by the automation system for continuous planting. Cantonese vegetable seed is selected to evaluate the planting precision of the prototype. Based on the design parameters obtained in laboratory experiments, the optimal condition for planting the Cantonese vegetable seeds in the 200-cell plug tray is verified by adjusting two parameters - the suction pressures and the nozzle implement type. On the other hand, the planting precision is indicated by the single seed sowing index. The results showed that the average quality of feed index is 70.53% when utilizing the large nozzle implement type with a vacuum pressure of 1019 Pa. The average working cycle time per tray and the machine capacity per hour are 51.0 seconds and 70 trays, respectively. Moreover, the prototype machine costs about 43% of the average cost of the existing commercial seeders in terms of cost-effectiveness.