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Automatic Fish Classification Using Lanczos Resampling and Deep Learning
(2025-09-01)
Kuswantori, Ari
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Suesut, Taweepol
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Suthanupaphwut, Worapanya
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Tangsrirat, Worapong
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Nunak, Navaphattra
The development of automation in the fish industry, a vital sector of the food industry, is a highly relevant and essential topic. This development is essential for boosting output and mitigating the risk of future food shortages brought on by the world’s population expansion. Automatic fish classification using computer vision has been widely developed in fish industry automation, and a lot of research on that topic has been published. However, while some research has produced promising results using complex methods, others have applied simpler approaches with less satisfactory outcomes. This study suggests a straightforward but efficient technique for differentiating between fish species by concentrating on their main characteristics, such as body form and scale patterns. To effectively support these image capturing properties, the Lanczos re-sampling technique is used in this study. Additionally, our basic deep learning model can correctly learn and identify fish species thanks to a fish picture categorization engine created using Google Teachable Machine. Utilizing the Fish-Pak dataset, a popular fish picture dataset frequently used in studies on fish species classification, the suggested approach successfully overcomes the difficulty and attains a high accuracy rate of 97.16%.
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Monitoring of Food Fouling in A Plate Heat Exchanger Using Heat Flux Sensor
(2025-01-01)
Nunak, Navaphattra
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Suthanupaphwut, Worapanya
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Suesut, Taweepol
This article aims to describe the effect of fluid flow rate on soymilk fouling in a flat plate heat exchanger at a surface temperature of 95 °C using a foil heat flux sensor. The experiment was conducted in the pilot-scale fouling test rig for 6 hours each run. Five product flow rates of 1.5, 3.0, 4.5, 6.0, and 7.5 1/min were studied. The results showed that a heat flux sensor could be used to real-time monitor the changes of soymilk fouling deposits formed on the heated surface. Fouling curves of soymilk at flow rates of 3.0 and 4.5 1/min exhibited a falling rate pattern, while at flow rate of 1.5 showed an asymptotic pattern. It was found that the thickness of fouling decreased with increasing product flow rate in the range of 1.5 - 4.5 1/min. At higher flow rates, only a slightly thin film of fouling layer was observed.
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Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning
(2025-01-01)
Kuswantori, Ari
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Nunak, Navaphattra
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Suthanupaphwut, Worapanya
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Schleining, Gerhard
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Tangsrirat, Worapong
The advancement of automation in the fish industry, a critical segment of the food sector, has become increasingly relevant in light of the growing global population and the impacts of climate change and global warming. Enhancing productivity through automation is essential to mitigate the looming threat of food scarcity. In this context, automatic fish classification using computer vision has garnered significant attention, with various studies exploring both complex and simple approaches. While complex methods have shown promising results, simpler approaches often fall short in performance. This study proposes a simple yet effective method that highlights key distinguishing features of fish—namely, body shape and scale patterns—for species classification. The Lanczos resampling technique is employed to crop, resize, and focus on the features, enabling a lightweight deep learning model to effectively learn and classify fish species. With the right conceptual framework, appropriate feature extraction techniques, and an efficient deep learning architecture, the proposed method addresses the classification challenge in a straightforward yet effective manner. Experimental evaluations using the Fish-Pak dataset, comprising six aquaculture fish species, and the KMITL Fish dataset, containing eight species, demonstrate the effectiveness of the method, achieving accuracy rates of 97.16% and 98.59%, respectively.
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Effects of heat transfer surface temperature on liquid egg yolk fouling
(2024-01-01)
Sritham, Eakasit
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Nunak, Navaphattra
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Suthanupaphwut, Worapanya
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Somlitsopak, Badin
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Chaishome, Jedsada
This study was aimed at investigating the effects of different surface temperatures (60-80°C) on the formation of egg yolk deposits on heat transfer surface. Experimental data from the fouling period were fitted with zero- and first-order reaction models and the reaction kinetics of fouling was obtained using the Arrhenius equation. Egg yolk fouling curves exhibited an asymptotic pattern showing only fouling and post-fouling periods. The fouling resistance at transition point increased with the increasing surface temperature. The zero-order reaction model was well describing the reaction rate of egg yolk fouling. The obtained activation energy of 85.47 kJ/mol was less than that for thermal denaturation of egg yolk proteins. The fouling process of egg yolk was mainly controlled by the deposition reaction.

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