-3dc97f3d182b6b0ed3d-13a2 20221119023321866 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 11 30 2022 11 12 Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India. Sagar Yeruva Annaldas Pushkara Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India. Athina Bhavana Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India. Markapuram Krishna Priya Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India. S Haripriya Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India Saka Pranuthi Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India. Nuthalapati Parthav Department of CSE (AIML and IoT), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad (Telangana), India. 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. 11 30 2022 62 66 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.L9332.11111222 https://www.ijitee.org/portfolio-item/l933211111222/ Department of Computer Science Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India Geerisha Jain In the last couple of decades, the number of vehicles has increased drastically, consequently, it is becoming difficult to keep track of each vehicle for purpose of law enforcement and traffic management. License Plate Detection is used increasingly nowadays for the same. The system performing the task of License Plate detection is known as the LPR system which generally consists of three steps: Detection of the License plate, Segmentation of License plate characters, and Recognition of the characters of the License Plate (LP). But in real-world scenarios, the various lighting conditions, camera angle, and rotation degrades the accuracy of License Plate region detection, which in turn causes inaccurate segmentation and recognition of the license plate characters hence leading to low accuracy of the LPR systems. Therefore, it is vital to consider the most promising algorithm or technique for LP detection. In this paper, we will be analyzing and comparing five different methods for license plate detection: Morphological reconstruction, Sobel Operator, Top Hat Transform, Histogram processing, and Canny Edge detection. We will be experimentally applying these techniques on real-time captured vehicle images, using the Bounding Box algorithm for character segmentation, performing license plate character recognition using Template matching, and subsequentially evaluating and demonstrating the LPR system that promises the most accurate and efficient results. 11 30 2022 67 75 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.L9342.11111222 https://www.ijitee.org/portfolio-item/l934211111222/