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All Journal Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Zakiyabarsi, Furqan
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Computer Science & IT

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Zoea Crab Larva Counter (CLARCO) Based On Image Processing With Adaptive Gaussian Filter Algorithm And Blob Detection Technique Zakiyabarsi, Furqan; Arizal, Arizal; Nurul Puteri, Annisa; Zulfajri S, Achmad; Syafaat, Muhammad
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2023): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v13i1.48

Abstract

The size of crab larvae is very small, so there is no accurate and easy-to-use crab larvae counting tool at an affordable price. The low larval survival rate is due to the unknown larval stocking density, which causes cannibalism, a feed-to-larvae ratio that is out of proportion to the number of larvae needed to maintain water quality, a water supply that is out of proportion to stocking density, and is economically unfavorable in terms of cultivation, feed management, maintaining water quality, and the buying and selling process. Accurately estimating the amount of crab larvae is anticipated to boost their survival rate and make them more economically successful. This research is a continuation of previous research with the addition of different methods using adaptive gaussian filter algorithms and blob detection techniques to count crab larvae in the zoea-2, zoea-3, and zoea-4 phases where an increase in accuracy was obtained with an average of 97.67 %.
Towards The Future of Crab Farming: The Application Of AI with Yolox And Yolov9 To Detect Crab Larvae Zakiyabarsi, Furqan; Amalia Aras, Rezty; Dwi Nugroho H, Yabes; Muhaimin Nur, Muhammad; Aditya Alfarizi, Dimas; Ulil Amri, Muhammad
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 14 No. 2 (2024): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v14i2.93

Abstract

Crabs are a highly valued food source and a key export commodity for Indonesia, but farming them remains a challenge, particularly during the larval stage, where survival rates are critically low. This issue has contributed to the declining population of wild crabs. A crucial factor in improving survival rates is the accurate detection and counting of crab larvae. By determining the precise number of larvae, farmers can optimize feeding ratios, manage stocking densities to reduce cannibalism, maintain water quality, and improve cost efficiency through better resource management. Despite its importance, no affordable and precise tools currently exist for this purpose. This study aims to develop a cost-effective and accurate crab larvae detection and counting application using image processing powered by deep learning artificial intelligence (AI). Two models, You Only Look Once eXtreme (YOLOX) and YOLOv9, were evaluated for their performance. The YOLOX-S model struggled with accuracy in detecting larvae, whereas the YOLOv9 model demonstrated superior performance, achieving a mean Average Precision (mAP) of 0.85 at IoU=0.5 and successfully detecting 93% of crab larvae objects accurately. The findings of this research have significant implications for supporting Indonesia's blue economy and aligning with Sustainable Development Goals (SDGs), particularly in sustainable fisheries and aquaculture. By enabling a tech-driven approach to crab farming, this solution addresses the challenges of declining wild crab populations, improves food security, and promotes economic growth for fishers and farmers. These advancements contribute to the development of a sustainable crab farming ecosystem, ensuring long-term ecological and economic benefits.
Co-Authors Aditya Alfarizi, Dimas Amalia Aras, Rezty Annisa Nurul Puteri Arizal, Arizal Dwi Nugroho H, Yabes Muhaimin Nur, Muhammad Syafaat, Muhammad Ulil Amri, Muhammad Zulfajri S, Achmad