Hongwiangjan, Jeerayut

Preferred name

Alternative Name

Hongwiangjan, J.

Main Affiliation

School of Engineering

jeerayut.ho@kmitl.ac.th

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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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Design of a laboratory-scale sugarcane weighing system
(2019-09-09)
Veerasakulwat, S.
;
Udompetaikul, V.
;
Hongwiangjan, J.
Recently, sugarcane harvesters have been increasingly used in sugarcane harvesting. Loading trucks were traveling along the harvesters to collect the harvested cane billets. Since cane harvesters are expensive machines, there is an idea of collaborative farming by combining multiple fields from different owners to reduce operating costs and time. However, it is difficult to fairly classify yields from different fields. Site-specific yield monitoring system is not common in typical harvesters. Farmers only know the weight on each truck without its collecting location when selling the sugarcane to the factory. This research was the feasibility study to develop a hydraulic weighing system in laboratory scale for further applying to the side-tipping loading trucks. A low-cost hydraulic weighing system was fabricated. A microcontroller was used to read signals from pressure and gyroscopic sensors and then to calculate the applied load. Accuracy and precision of the system were examined. The coefficient of determination (R2) of the relationship between the actual and determined loads was 0.978. The standard error of prediction (SEP) of the system was 2.348 kg. The results show that there was feasibility to apply the system on farm scale; however, further study with a larger scale should be conducted.
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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
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Sharma, Sneha
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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.
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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.
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Quantitative detection of pepper powder adulterated with rice powder using Fourier-transform near infrared spectroscopy
(2019-09-09)
Lapcharoensuk, R.
;
Chalachai, S.
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Sinjaru, S.
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Singsriand, P.
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Hongwiangjan, J.
Near infrared (NIR) spectroscopy model was developed for detecting pepper powder adulterated with rice powder. The adulterated pepper powder samples were prepared by mixing rice powders with pure pepper powder to 19 levels of concentrations (w/w) from 5-95%w/w. Two hundred ten NIR spectra of pure and adulterant pepper powders were recorded using Fourier-transform near infrared spectrometer. The NIRs quantitative model for detecting adulterant pepper were established using partial least squares regression (PLS). The optimum model was established from NIR spectra treated by constant offset elimination with the of 0.99. These results show that the NIR spectroscopy could be a modern method for monitoring adulteration of pepper powder with rice powder.
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Modeling of hot air drying of coconut residue
(2018-08-14)
Wutthigarn, Pattawee
;
Hongwiangjan, Jeerayut
;
Sripinyowanich Jongyingcharoen, Jiraporn
In this study, the effect of drying temperature (50-110°C) on hot air drying characteristics of coconut residue was investigated. The drying time and drying rate (DR) were in the ranges of 540-100 min and 0.0048-0.0182 g water/g dry matter·min at the drying temperature of 50-110°C, respectively. Six drying models (Lewis, Page, Henderson and Pabis, Logarithmic, Midilli et al, and linear-plus-exponential model) were used to determine the change in moisture ratio (MR) with drying time. The linear-plus-exponential model provided best fitting of the predicted MR to the experimental MR with the highest average R2 of 0.9985 and the lowest RMSE of 0.01463. The variation of drying temperature with the constants and coefficient of the model was polynomial type. The generalized linear-plus-exponential model as a function of drying temperature gave best result of prediction of MR with the R2 of 0.9709.

Hongwiangjan, Jeerayut

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