Abstract: The AI vision based weight estimation of crab is an innovative, contactless solution designed to estimate the weight of crabs without the use of conventional load cell sensors. This system integrates a Raspberry Pi 4 (4GB RAM) and an ESP32 microcontroller to create a smart, automated, and hygienic seafood grading platform.The system captures top-view images of crabs using a camera module connected to the Raspberry Pi. These images are processed using Python-based image processing techniques with the OpenCV library, where key features such as area, length, and width are extracted. A machine learning regression algorithm is applied to estimate the crab's weight based on these extracted features. The predicted weight is then displayed on a 16×2 LCD with I2C interface, providing real-time feedback to the user. Additionally, a push button interface is included to initiate image capture and processing, while a buzzer system provides audio alerts indicating successful measurement or error conditions. The ESP32 supports auxiliary control functions and enables future IoT integration for remote monitoring and data logging.Experimental analysis shows that the system achieves an accuracy of approximately 97–98%, with minimal error margins, making it a reliable alternative to traditional weighing methods. The contactless nature of the system ensures improved hygiene, making it particularly suitable for seafood processing industries.This project demonstrates the effective integration of embedded systems, computer vision, and machine learning, offering a low-cost, scalable, and automated solution for real-time crab weight estimation and grading.

Keywords: Crab weight detection, computer vision, machine learning, image processing, OpenCV, Raspberry Pi 4, ESP32, regression algorithm, feature extraction, contactless measurement, smart aquaculture, IoT integration, automation system, real-time monitoring, seafood grading system.

Downloads: | DOI: 10.17148/IARJSET.2026.13440