Abstract
Pond biomass estimation and non-invasive biometrics are necessary problems to solve in shrimp farming to achieve the optimization of currently outdated manual processes which are slow, inaccurate, imprecise, and prone to errors. This dataset was collected to develop and test computer vision and artificial intelligence models to accurately detect shrimps and estimate their biomass. The dataset was collected in three ponds, two in an industrial farm and the other in a university pond cultivated for academic purposes. 170 shrimps were sampled by taking pictures and manual measurements of their total length, cephalothorax length, and weight. A total of 5507 shrimp' images were taken which were put in containers equipped with cameras and with a water level of 10 cm. The dataset is organized into five sub-datasets folders and excel files containing the manual measurements taken with a scale and vernier from each sample. This dataset could be used to compare and develop different detection and biomass estimation computer vision models since it presents a good amount of images and samples of shrimps cultivated in different conditions which can allow models to relate image features of shrimp samples with their corresponding weight and also compare these models against the machine learning models that can be applied solely to the manually extracted features stored in the excel files for biomass estimation.