Hongwiangjan, Jeerayut

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Preferred name
Hongwiangjan, Jeerayut
Alternative Name
Hongwiangjan, J.
Main Affiliation
Email
jeerayut.ho@kmitl.ac.th

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    Item type:Publication,
    The energy potential evaluation of biomass fuel from weed pellets
    (2023-03-01)
    Saechua, W.
    ;
    Posom, J.
    ;
    Hongwiangjan, J.
    ;
    Nakawajana, N.
    The giant sensitive plant had the most appropriated qualifications for pellet production, as it contained a low content of moisture (3.07%) and ash (2.68%), and a high percentage of fixed carbon (14.13%) and volatile matter (80.12%) with the highest lower heating value of 18,334.08 J/g. It was found that a higher mixing ratio of water for pelletization led to increasing moisture and ash content but resulted in lower volatile matter and fixed carbon. The best mixing ratio for was 8% of water by weight, at which the evaluated properties of the giant sensitive plant pellets: LHV, FC, VM and ash content, were 18,747.46 J/g, 10.92%, 78.77% and 2.24%, respectively. The evaluation of the physical properties of the pellets revealed that a higher amount of water resulted in a larger diameter, greater length and higher fines content. Comparison of pellets from this work with the standard classes of biomass pellets found that the giant sensitive plant pellets could be classified in class I3. It was clearly seen that the weed can be utilized as a useful biomass fuel and formed in a pellet shape for combustion in industrial sectors.
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    Huanglongbing (HLB) disease detection using drone imagery and deep learning neural networks for early management of HLB
    (2026-08-01)
    Kalbande, Vishal Dashrathrao
    ;
    Phanomsophon, Thitima
    ;
    Hongwiangjan, Jeerayut
    ;
    Sripinyowanich Jongyingcharoen, Jiraporn
    ;
    Sirisomboon, Panmanas
    In the modern world, machine learning and artificial intelligence have become the foundation of the digital revolution and have a significant role in daily life. It is adopted in various applications, such as object detection, recognition and classification. This research aimed to use deep learning algorithms for the detection of Huanglongbing (HLB)-infected disease, healthy and background patch in citrus orchard. HLB-infected patches need to be identified for appropriate treatments of spraying. However, farmers are unaware of infections on plant leaves and therefore adopt manual disease identification methods. This method results in loss of productivity as the infection spreads throughout the field. However, due to a lack of required facilities, instant identification needs to be improved in many aspects of the agricultural sector. To conduct this research, firstly, a dataset was created that contained drone images of citrus orchards that was categorized into three classes, specifically (i) Healthy, (ii) HLB-infected and (iii) Background. Under this research, 6000 image patches of citrus orchard were collected and categorized 2000 images in each class based on appropriate labels. The next step was to train the deep learning models to identify Healthy, HLB-infected and Background. In this research three models viz. EfficientNetV2B0, DenseNet-121 and ResNet-50 were trained to detect the HLB infection patch in the citrus orchard. The models exhibited exceptional performance, with ResNet-50 achieving the highest overall accuracy of 89%, followed by EfficientNet-V2B0 (87%) and DenseNet-121 (85%). ResNet-50 demonstrated superior balanced performance with macro-average precision of 90%, recall of 89%, and F1-score of 89%. The generated HLB detection map effectively visualized disease distribution patterns, identifying 31% of patches as HLB-infected, 7% as Healthy and 62% as Background across the orchard grid. This research establishes a reliable framework for large-scale, automated citrus disease monitoring that enables precision agriculture interventions, potentially reducing economic losses and optimizing resource allocation in citrus cultivation through early and accurate HLB disease detection.
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    Filter-based short-wave infrared imaging combined with machine learning for non-destructive quality assessment of durian pulp
    (2026-09-01)
    Promnioy, Surasak
    ;
    Phetpan, Kittisak
    ;
    Riza, Dimas Firmanda Al
    ;
    Sharma, Sneha
    ;
    Sirisomboon, Panmanas
    The development of affordable, real-time quality monitoring tools is essential for industrial applications involving high-value tropical fruits such as durian. This study presents a cost-effective short-wave infrared multispectral imaging (SWIR-MSI) system employing three discrete bandpass filters (880, 905, and 940 nm) integrated with machine learning algorithms for non-destructive evaluation of fresh-cut durian pulp. Compared with conventional point-based NIR spectroscopy and complex hyperspectral imaging systems, the proposed configuration markedly reduces system complexity and cost while maintaining sensitivity to key compositional variations. It accurately predicted dry matter content (DMC) and starch, demonstrating that limited spectral information within the 860–1100 nm range can effectively capture moisture- and carbohydrate-related features. These findings confirm the feasibility of implementing filter-based SWIR imaging as a practical and scalable alternative to hyperspectral systems for on-line fruit quality assessment. Practically, this approach enables rapid, non-destructive, and spatially adaptable evaluation of durian pulp quality, offering significant potential for on-site grading, ripeness classification, and process control in fresh-cut durian production and packaging operations, particularly for small- and medium-scale agro-processors.