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Development of machine learning enhanced low-cost spectrophotometer for pesticide prediction
(2025-05-15)
Murathathunyaluk, S.
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Jinorose, M.
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Janpetch, K.
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Chanthapanya, N.
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Sombatsri, W.
Conventional analytical methods for measuring pesticide concentrations, such as chromatography, offer high accuracy but require expensive instrumentation, prompting the investigation of cost-effective alternatives like smartphone-based spectrophotometers. Despite their potential, these methods face challenges related to assembly and precision, often requiring human intervention to select appropriate images for analysis. This study presents a novel, affordable spectrophotometer designed for integration with machine learning algorithms. The device captures images of two spectral bands and employs a six-step image processing methodology to prepare images for analysis. A machine learning model trained on four algorithms with feature selection and cross-validation demonstrates high accuracy in predicting chemical concentrations of coloured solutions. The approach achieves 98.5 % accuracy for KMnO<inf>4</inf> and 96.7 % for Carbosulfan solutions, comparable to high-end spectrophotometry devices. The design eliminates the need for human intervention, reducing biased selection and result manipulation. However, concentration estimation of non-coloured compounds remains inaccurate, indicating areas for further refinement.
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Real Time Diagnosis of Neonatal Jaundice using Machine Learning
(2025-01-01)
Akarapanuvitaya, Napat
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Pintavirooj, Chuchart
Neonatal jaundice, a condition commonly found among newborns, usually requires invasive and timely methods for diagnosis to prevent further complications. This research presents a real-time diagnostic system for neonatal jaundice using machine learning and image processing techniques. The system utilizes a dataset of neonatal images, which undergo preprocessing to extract relevant features. Features, including color values from different color spaces, are analyzed using multiple machine learning models, such as XGBoost, CatBoost, Support Vector Machines (SVM), Random Forest (RF), and LightGBM. These models are trained and evaluated for their predictive performance. A user-friendly graphical user interface is developed to enable real-time diagnosis, implemented on a Raspberry Pi device equipped with a webcam to acquire real-time image capture and apply image processing. The system demonstrates the potential for accessible and reliable neonatal care solutions.
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Egg Defect Detection and Classification in Boiled Egg Industry with Surface Disturbance Removal on the Eggshell Based on Image Processing
(2025-01-01)
Chotchawalkul, Sasikan
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Chaipanya, Oraya
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Anuntachai, Anuntapat
In the boiled egg industry, quality inspection is typically conducted twice: before eggs are transported into the conveyor-based boiling system (before boiling), and after they exit the water-based cooling system prior to packaging (after cooling). These inspections are commonly carried out through human visual assessment, which demands substantial human resources and time. This paper presents an automated system for detecting and classifying defective eggs-such as those with cracks, dents, rough shells, and other surface anomalies-using image processing techniques. The system is designed to enhance the visibility of such defects while minimizing the impact of production-related surface disturbances, including water stains, reflections from the cooling process, and red stamps from imported eggs. The proposed system comprises two main approaches: (1) Defects Detection Method, which classifies eggs into two categories: intact and defective; and (2) Pixel Counting and Comparison Method, which classifies eggs into three categories: intact, cracked or dented, and exploded eggs. This system offers a practical and efficient solution for the egg processing industry, reducing reliance on human labor, minimizing inspection time, and lowering hardware requirements for industrial implementation.
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Skill Level Recognition in Writing for Elementary School Students in Thailand Using Image Processing Technology
(2024-01-01)
Jarusitratti, Nattwat
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Laohakul, Krittatee
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Anuntachai, Anuntapat
In the current context, the problem of developmental writing difficulties in children is of great importance for school-age children. Diagnosing whether a child has developmental writing difficulties requires the use of writing skills assessments. These assessments are used by professionals to evaluate and diagnose any abnormalities in a child's writing development. However, there are limitations in terms of format, as they often rely on expert physicians for diagnosis. This creates a significant need for human resources. To address this, we have designed a method for scoring based on writing skills assessments, utilizing image processing technology and criteria from existing standards. The scoring criteria include three aspects: article writing position, article format, and copying speed. For article writing position, we find the centroid of the text. Article format is assessed based on the aesthetics of the written article, which should form a parallelogram. Lastly, copying speed is determined by the number of lines visible on the paper, using pixel frequency analysis.
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System for Analysis and Verification of Exercise Postures with Equipment
(2024-01-01)
Kamcharoen, Chayanee
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Boriboon, Pragasit
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Anuntachai, Anuntapat
This paper is initiated in response to affectation from pandemic. The people demand to improve their heath by themselves, and the exercise equipment is easier to install at home. Exercising with equipment requires fundamental knowledge to avoid any injuries and reduce ineffective performance. The system analyzes exercise postures using equipment, encompassing 3 poses: Deadlift, Lat Pull Down, and Bench Press. Analysis is comparing the alignment of skeletal joints in the body from exercise videos through image processing and comparing correctness against expert's movement. Results and recommendations are displayed on a web application which is developed by the Django Framework. Test results indicate an improvement in users' exercise direction tendencies.
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Lung Cancer Prediction Model from Chest X-Ray Images
(2024-01-01)
Chaiyathed, Chayodom
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Thanesmaneekul, Ekawit
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Anuntachai, Anuntapat
Lung cancer is one of the leading causes of death globally. Early diagnosis of lung cancer is crucial for treatment and prognosis. Traditional medical techniques, such as chest x-rays, have limitations in the early diagnosis of lung cancer. This paper develops an image classification model for chest CT scans using deep learning with transfer learning techniques. The data is divided into three parts: a training set, a testing set, and a validation set. The development of this model can be applied to improve the efficiency of early lung cancer diagnosis, reduce the risk of human errors, and increase workflow efficiency in hospitals. In this paper, a model is developed to distinguish between normal images and images with lung cancer. This model can potentially assist physicians in accurately and rapidly diagnosing lung cancer.
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Weapon Detection in X-ray Image of Baggages
(2024-01-01)
Kundilokovit, Piyapat
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Thaweechoklertchaikul, Rimthaweep
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Anuntachai, Anuntapat
Due to the daily commutes of people by MRT trains, following the shooting incident at Paragon, the MRT system has implemented bag searches before entering the stations to look for concealed or hidden weapons. These searches are conducted manually, which sometimes may not be thorough enough and can take a significant amount of time. Especially during peak hours when many people are using the MRT, it is possible for some individuals to pass through the station without being searched. Such actions can render the security measures ineffective. Therefore, this paper proposes a study to find ways to address these issues. From the study and comparison of object detection processes for risky items, such as sharp objects or guns, in X-ray images of luggage, it was found that models such as CNN, RCNN, Detectron, RetinaNet, and Yolo achieved excellent results in object detection and recognition. The organizers plan to apply object detection techniques and improve the existing methods for detecting objects in X-ray images to be more efficient and accurate, capable of identifying a variety of risky items.
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Investigating the Influence of Rigden Void of Fillers on the Moisture Damage of Asphalt Mixtures
(2023-12-01)
Wuttisombatjaroen, Jirat
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Hemnithi, Nithinan
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Chaturabong, Preeda
Moisture damage and bond loss are major factors in pavement degradation, often stemming from excessive moisture accumulation due to weather events. Water infiltrates the gap between asphalt binder and aggregate, weakening the asphalt bond. Rigden Void (RV) has emerged as a crucial parameter in assessing the susceptibility of asphalt mastic-aggregate systems to moisture-induced damage. However, numerous waste natural fillers have been researched as potential aggregate filler replacements, yet their role in moisture damage remains unexplored. Therefore, this study aimed to understand how different fillers, including waste natural materials like coconut peat and bagasse, affect asphalt mixture performance and moisture damage. Results showed that Rigden Voids were positively correlated with pore size and negatively correlated with surface area. Larger pores contributed to higher Rigden Voids, while greater surface areas led to lower values. Limestone had the highest Rigden Void percentage due to its larger pore size and lower surface area. The research also explored contact parameters between fillers and asphalt, revealing varying interactions based on filler and asphalt types. Moisture damage testing demonstrated that all mixtures, both dense and porous, displayed good resistance to moisture damage. The correlation analysis between Rigden Voids and moisture damage revealed varying degrees of influence, dependent on asphalt type and aggregate gradation.
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Automated Bacterial Colony Counting on Agar Plate
(2023-01-01)
Bunkum, Manao
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Visitsattapongse, Sarinporn
In several fields, such as microbiology research, medical diagnostics, and food safety evaluation, bacterial colony counting is extremely important. However, the method of manual counting is time-consuming, labor-intensive, and prone to human error. This research approached these problems by using MATLAB's image processing feature to automatically count the number of bacterial colonies on agar plates. This technique effectively detects bacterial colonies from photos of agar plates by using image analysis algorithms. The images of agar plates were captured while controlling the lighting and adjusting the size to achieve the highest possible image quality. This study encompassed 10 bacterial species, achieving an accuracy of approximately 80%. This level of precision underscores the reliability and effectiveness of our automated system.
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Camera Pose Estimation using CNN
(2020-08-23)
Wattanacheep, Bhattarabhorn
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Chitsobhuk, Orachat
Estimating camera pose is a significant process, which assures the success of the 3D modeling performance. This research presents a camera pose estimation using convolutional neural network (CNN) to transfer learning from pre-trained deep learning VGG19 model in order to extract features from a single image using several datasets captured in indoor and outdoor environments with diverse perspectives and photographic styles. Due to the large dimensions of the extracted features, Latent Semantic Analysis (LSA) are introduced prior to the CNN input. Then, the CNN is trained to predict the camera views and translations. The prediction performance is measured in terms of average mean square errors and compared to the reference techniques. As a result, the regression estimation of the proposed CNN model outperforms the others with average 0.24 degrees rotation error and 0.26 m. translation errors.

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