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Start date: 2020Subject: search.filters.subject.Computer Vision

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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.