Penaeus vannamei is one of the most cultured species. The global production of Penaeus (Litopenaeus) vannamei reached 5.8 million tonnes in 2020, contributing to 51.7% of total shrimp production. However, despite its high production, there are still many issues in this industry. One of those issues is shrimp growth monitoring. The growth monitoring is one of the crucial parts in shrimp production. The growth of shrimp not only determines how fast the shrimp grows but also the efficiency of feeding rate. Currently the growth rate of shrimp is monitored using conventional methods. The method not only introduces bias since it’s only calculating the average value from the samples but also time consuming since the shrimps need to be dried first before scaling. In this research we propose new methods that utilize image processing and machine learning algorithms to measure the shrimp weight. This research proposes a combination of YOLO V8 detection algorithm and logarithmic regression to measure the shrimp weight. YOLO V8 is used to detect shrimps from the images and its height. And then from the detected shrimp objects we predict the weight using logarithmic regression. Using this algorithm, we are able to measure the shrimp weight with Mean Average Error (MAE) 0.6 gram.