MATSYASTRA – An Automated Fish Species Identification using Teachable Machine Services
Sagar Yeruva1, Annaldas Pushkara2, Athina Bhavana3, Markapuram Krishna Priya4, S Haripriya5, Saka Pranuthi6, Nuthalapati Parthav7
1Sagar Yeruva, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
2Annaldas Pushkara, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
3Athina Bhavana, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
4Markapuram Krishna Priya, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
5S Haripriya, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
6Saka Pranuthi, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
7Nuthalapati Parthav, Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India.
Manuscript received on 22 October 2022 | Revised Manuscript received on 15 November 2022 | Manuscript Accepted on 15 November 2022 | Manuscript published on 30 November 2022 | PP: 62-66 | Volume-11 Issue-12, November 2022 | Retrieval Number: 100.1/ijitee.L933211111222 | DOI: 10.35940/ijitee.L9332.11111222
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Abstract: Generally, only feature values obtained from photos are used to identify fish species. But, it is challenging to identify fish species based on an image alone because fish of the same species can have varying hues or seem quite similar to other species. Additionally, it can be a tedious task that might lead to wrong predictions. Since various fish species exist, it is difficult to determine a fish without a proper model. Fast-growing computing and sensing technologies have improved most embedded systems, which help us solve more complicated algorithms. The main challenge is to perceive and analyze corresponding information for better judgment. An advanced system with better computing power can facilitate identifying fish species. Using the Teachable machine, a web-based tool for creating machine learning models, we can ensure that this application gives accurate results in classifying various fish species. An application that uses machine learning to identify fish categories is developed in this study by capturing images of fish and identifying their categories. In addition to providing fish information, this app also connects users with other fishermen, gives feedback on the fish, display catch logs, supports multilingual display of data, fish focused advisory chatbot, and market value information. User dashboards allow users to sign up, create profiles, scan, and identify their catches. This mobile application ensures the data integrity and confidentiality of the user’s data. The overall performance of the application is responsive and user friendly.
Keywords: Fish Species, Identification, Teachable Machine, MATSYASTRA.
Scope of the Article: Automated Software Specification