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    Low–cost IoT–enabled organic pH control system for smart aquaponic production in urban agriculture
    (2026-02-15)
    Sinchai, Ananta
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    Ardkaew, Chitsanucha
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    Jaiyen, Tontrakan
    This study presents a smart aquaponic system integrating low–cost IoT monitoring, automated organic pH control using food–grade orange juice, and computer vision–based plant growth assessment to enhance sustainable urban lettuce production. Two experimental rounds compared uncontrolled pH conditions with active regulation across 15 plants per group, with sample size adequacy confirmed through power analysis for large observed effects. The system maintained optimal pH (5.8–7.0) through automated feedback control of orange juice within a defined range, producing 13–30 % higher growth rates, supported by t-tests, effect sizes, confidence intervals, and regression analysis. Performance and cost efficiency were assessed against established control strategies, showing competitive outcomes under resource–limited conditions. Economic evaluation indicated a payback period of 9–59 months depending on local market factors. Overall, results demonstrate the feasibility of combining natural pH buffers with IoT feedback to reduce chemical inputs, sustain growth improvements, and enable scalable adoption in small– to medium–scale aquaponics.
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    BMA for the BMA: Boosting Mobility Analysis for the Bangkok Metropolitan Administration via Automated Pedestrian Counting from CCTV
    (2024-01-01)
    Kujareanpaisal, Poonnaphop
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    Mayhasap, Rujira
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    Tea-Makorn, Pin Pin
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    Jindahra, Pavitra
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    Starita, Stefano
    The objective of detecting and counting people using the CCTV camera on the footpath is to facilitate and reduce the time required to count the number of people traveling in pedestrian areas without having to actually visit the area. This paper uses the head detection technique to solve the problem of overlapping objects, YOLOv8n for detection and BoT-SORT for object tracking. A program was developed to assist the Bangkok Metropolitan Administration in counting the number of people within the region of interest and visualizing the statistics. Users can view statistics in the form of visual charts to compare the maximum number of people in each period by importing the video into the program. Users can also view historical statistics from previously imported videos. This program enables users to monitor pedestrian traffic in each area, providing valuable insights for urban planning decisions.
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    Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning
    (2021-06-29)
    Kittiworapanya, Phongsathorn
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    Pasupa, Kitsuchart
    ;
    Auer, Peter
    We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.
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    An Image Segment-based Classification for Chest X-Ray Image
    (2020-11-19)
    Kittiworapanya, Phongsathorn
    ;
    Pasupa, Kitsuchart
    In late 2019, the first case of COVID-19 was confirmed in Wuhan, China. The number of cases has been rapidly growing since then. Molecular and antigen testing methods are very accurate for the diagnosis of COVID-19. However, with sudden increases of infected cases, laboratory-based molecular test and COVID-19 test kits are in short supply. Because the virus affects an infected patient's lung, interpreting images obtained from Computed Tomography Scanners and Chest X-ray Radiography (CXR) machines can be an alternative for diagnosis. However CXR interpretation requires experts and the number of experts is limited. Therefore, automatic detection of COVID-19 from CXR images is required. We describe a system for automatic detection of COVID-19 from CXR images. It first segmented images to select only the lung. The segmented part was then fed into a multiclass classification module, which worked well with samples obtained from various sources, which had different aspect ratios, contrast and viewpoints. The system also handled the unbalanced dataset-only a small fraction of images showed COVID-19. Our system achieved 92% of F1-score and 88.1% Marco F1-score on the 3rd Deep Learning and AI Summer/Winter School Hackathon Phase 3-Multi-class COVID-19 Chest X-ray challenge public leaderboard.
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    An implementation of automatic container number recognition system
    (2018-05-30)
    Roeksukrungrueang, Chanathip
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    Kusonthammrat, Theeradet
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    Kunapronsujarit, Napasin
    ;
    Aruwong, Thiwat Nunj
    ;
    Chivapreecha, Sorawat
    This paper presents a design and implementation of automatic container number recognition (ACNR) system for an oversea logistic purpose. Usually in import/export industry in Thailand, the container number which is used for logistic management and also customs duties is manually inspected and recorded by human at each gate of port. However, the manual operation by human causes of error and slow speed of operation from bottle neck, especially customs clearance verification process which customs officers and terminal operators must deal with containers individually as they enter and leave terminals via a gate. By these reasons, an automatic system is required. The cameras capture containers and by using image processing and optical characteristic recognition (OCR) techniques, a container number will be achieved and then customs clearance verification can be automatically checked via database system including other logistic management. The proposed ACNR system can reduce the bottle neck issue, enhance time utilization, accuracy rate, and data management efficiency. From an experiment, total samples of 218 containers are tested, the proposed system can give an accuracy rate of 95.41% with time utilization < 10 seconds per/container.
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    Influence of facial expression and viewpoint variations on face recognition accuracy by different face recognition algorithms
    (2017-08-29)
    Phankokkruad, Manop
    ;
    Jaturawat, Phichaya
    Face recognition is a personal identification method using biometrics that is gaining the attention in this research field. The face recognition process can be done without the human and devices interaction, so it can be applied in several applications. In additions, the face recognition systems are typically implemented at different places in unconstrained environments. Hence, the study of the factors that impact the face recognition accuracy is an interesting and challenging topic. In the class attendance checking system using face recognition, there are variations of three factors that possibly affect the accuracy of the system; facial expressions, and face viewpoints. This study intends to compare facial recognition accuracy of three well-known algorithms namely Eigenfaces, Fisherfaces, and LBPH. The experiments conducted in the respects of the variation of facial expressions, and face viewpoints in the actual classroom. The results of the experiment demonstrated that LBPH is the most precise algorithm which achieves 81.67% of accuracy in still-image-based testing. The facial expression that has the most impact on accuracy is the grin, and face viewpoints that affect accuracy are looking down and tilting left, and right respectively. Therefore, LBPH is the most suitable algorithm to apply in a class attendance checking system after considering the accuracy.