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Author: search.filters.author.Kuswantori, Ari

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    Automatic Fish Classification Using Lanczos Resampling and Deep Learning
    (2025-09-01)
    Kuswantori, Ari
    ;
    Suesut, Taweepol
    ;
    Suthanupaphwut, Worapanya
    ;
    Tangsrirat, Worapong
    ;
    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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    Simple and Effective Techniques for Automatic Fish Species Classification Using Image Processing and Deep Learning
    (2025-01-01)
    Kuswantori, Ari
    ;
    Nunak, Navaphattra
    ;
    Suthanupaphwut, Worapanya
    ;
    Schleining, Gerhard
    ;
    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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    Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm
    (2023-03-01)
    Kuswantori, Ari
    ;
    Suesut, Taweepol
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    Tangsrirat, Worapong
    ;
    Schleining, Gerhard
    ;
    Nunak, Navaphattra
    Featured Application: In the future, the application of this study is very feasible and very close to being implemented for the auto-sorting system for various fish or other objects, in the fish industry or other industries, with deep learning and machine vision technology. Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.
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    DEVELOPMENT OF OBJECT DETECTION AND CLASSIFICATION WITH YOLOV4 FOR SIMILAR AND STRUCTURAL DEFORMED FISH
    (2022-03-31)
    Kuswantori, Ari
    ;
    Suesut, Taweepol
    ;
    Tangsrirat, Worapong
    ;
    Nunak, Navaphattra
    Food scarcity is an issue of concern due to the continued growth of the human population and the threat of global warming and climate change. Increasing food production is expected to meet the challenges of food needs that will continue to increase in the future. Automation is one of the solutions to increase food productivity, including in the aquaculture industry, where fish recognition is essential to support it. This paper presents fish recognition using YOLO version 4 (YOLOv4) on the «Fish-Pak» dataset, which contains six species of identical and structurally damaged fish, both of which are characteristics of fish processed in the aquaculture industry. Data augmentation was generated to meet the validation criteria and improve the data balance between classes. For fish images on a conveyor, flip, rotation, and translation augmentation techniques are appropriate. YOLOv4 was applied to the whole fish body and then combined with several techniques to determine the impact on the accuracy of the results. These techniques include landmarking, subclassing, adding scale data, adding head data, and class elimination. Performance for each model was evaluated with a confusion matrix, and analysis of the impact of the combination of these techniques was also reviewed. From the experimental test results, the accuracy of YOLOv4 for the whole fish body is only 43.01 %. The result rose to 72.65 % with the landmarking technique, then rose to 76.64 % with the subclassing technique, and finally rose to 77.42 % by adding scale data. The accuracy did not improve to 76.47 % by adding head data, and the accuracy rose to 98.75 % with the class elimination technique. The final result was excellent and acceptable.
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    Fish Recognition Optimization in Various Backgrounds Using Landmarking Technique and YOLOv4
    (2022-01-01)
    Kuswantori, Ari
    ;
    Suesut, Taweepol
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    Tangsrirat, Worapong
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    Satthamsakul, Sutham
    The identification and categorization of fish is a popular and fascinating research topic. Many researchers have developed expertise in fish detection, both underwater and outside the water, which is particularly beneficial for population management and aquaculture. This paper proposes a fish recognition approach using the landmarking methodology with YOLO version 4 to identify and categorize fish with different backdrop circumstances. The approach can be used both underwater and on land. The proposed approach was evaluated using four distinct types of fish from the BYU dataset. The final test result determined that the accuracy reached 96.60%, with an average classification score of 99.67% at the 60% threshold. The result is 4.94 % better than the most frequent traditional labelling approach.