A Low-Cost Computer Vision Approach for Counting Juvenile Shrimp Using OpenCV and Smartphone

One of the most important tasks in growing and trading shrimp is postlarvae and juvenile shrimp counting. In Vietnam, most small-scale shrimp farms/companies today rely on manual and volumetric counting to estimate the number of postlarvae and juveniles for sale to customers. This traditional counting method is time-consuming, labor-intensive, and prone to error. Inaccurate shrimp counting not only causes economic loss to sellers and buyers but also affects production efficiency. This paper presents the design and implementation of a computer vision-based device for automatic counting of juvenile shrimp. The designed shrimp counting device were portable and low cost by taking advantages of the open-source computer vision library OpenCV and the power of now-ubiquitous smartphones. The experimental results demonstrate that the proposed counting approach can provide an average accuracy of over 96% compared to the true values. The average processing time for one counting is a few seconds. This could provide famers with a shrimp counting approach that offers acceptable accuracy, requires less time and labor cost compared to the traditional manual counting methods.