Wongrujira, Krit

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Wongrujira, Krit
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krit.wo@kmitl.ac.th

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    Item type:Publication,
    Golden Ratio-Based Assessment of Nam Dok Mai Mango Shape Using Image Processing
    (2025-11-13)
    Wongrujira, Krit
    ;
    Pun, Umed Kumar
    ;
    Pornchaloempong, Samak
    This study presents an application of the golden ratio (<p - 1.618) to evaluate the shape aesthetics of the Nam Dok Mai mango variety. Mango shape plays a critical role in determining both market value and consumer preferences. However, the assessment of visually appealing mango shapes based on human perception is inherently subjective and susceptible to inconsistencies. To address this, the proposed model utilizes one-dimensional (ID) top-view images processed using automated image analysis techniques, including segmentation, contour detection, and feature extraction. Four geometric components (G1, G2, G3, and G4) were derived from the images and statistically analyzed. The results revealed that the computed ratios closely aligned with the golden ratio. The experiment was conducted using a dataset of 100 Nam Dok Mai mangoes. The shapes perceived as visually desirable by the human evaluators corresponded closely with those identified by the model as exhibiting golden ratio proportions. Furthermore, for each mango, consistent values of G1 through G4 were obtained from both the top A and top B views, confirming the model's repeatability across symmetrical perspectives. This study demonstrates the potential of incorporating mathematical aesthetics, specifically the golden ratio, into agricultural quality assessment frameworks to enhance objectivity and standardization in fruit grading.
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    Item type:Publication,
    Implementation of LoRa-Based Remote Monitoring System for Precision Mango Farming
    (2025-11-13)
    Wongrujira, Krit
    ;
    Pun, Umed Kumar
    ;
    Rakmae, Samak
    This study investigated the effectiveness of a LoRa-based remote monitoring system for enhancing precision mango farming. The main objective was to evaluate how real-time environmental data collected from soil. air. and light sensors can support improved cultivation practices. A sensor network was deployed in a commercial mango orchard in Chachoengsao. Thailand, focusing on two areas: one managed with good agricultural practices, and the other with conventional methods. Ten mango trees were randomly selected from these areas for sensor installation to monitor environmental conditions. Data were collected hourly from July 2023 to April 2024. transmitted to a cloud-based MQTT broker, and visualized using a web dashboard. The study hypothesizes that sensor-guided interventions, such as optimized irrigation and pruning, lead to better environmental conditions and higher fruit yield. The results confirmed that trees under good practices, where the soil effectively retained rainwater, showed higher moisture retention. In contrast, some trees subjected to conventional practices exhibited lower retention rates. These findings support the potential of IoT-based LoRa systems to enable data-driven sustainable mango cultivation.