Machine learning in estimating the weight of Tilapia Oreochromis niloticus juveniles - Lessandro do Carmo Lima; Adriano Carvalho Costa; Heyde Francielle do Carmo França; Alene Santos Souza; Gidélia Araújo Ferreira de Melo; Brenno Muller Vitorino; Vitória de Vasconcelos Kretschmer; Isabel Rodrigues de Rezende; Rafael Vilhena Reis Neto (Aquaculture, 2026)

Predicting the Body Weight of Tilapia Fingerlings from Images Using Computer Vision - Lessandro do Carmo Lima; Adriano Carvalho Costa; Heyde Francielle do Carmo França; Alene Santos Souza; Gidélia Araújo Ferreira de Melo; Brenno Muller Vitorino; Vitória de Vasconcelos Kretschmer; Suzana Maria Loures de Oliveira Marcionilio; Rafael Vilhena Reis Neto; Pedro Henrique Viadanna; Gabriel Rinaldi Lattanzi; Luciana Maria da Silva; et al. (Fishes, 2025)
The aim of this study was to develop a mathematical model to predict the body weight of tilapia fingerlings using variables obtained through computer vision. A total of 2092 tilapia fingerlings and juveniles, weighing between 10 and 100 g, were fasted for 12 h, anesthetized, weighed, and photographed using an iPhone 12 Pro Max at 33 cm height in a closed container with different bottom colors. Images were segmented using Roboflow’s instance segmentation model, achieving 99.5% mean average precision, 99.9% precision, and 100% recall. From the segmented images, area, perimeter, major axis (MA), minor axis (SA), X and Y centroids, compactness, eccentricity, and the MA/SA ratio were extracted. Seventy percent of the data was used to build the model, and 30% for validation. Stepwise multiple regression (backward selection) was performed, using body weight as the dependent variable. The prediction model was: −17.7677 + 0.0007539(area) – 0.0848303 (MA) – 0.108338(SA) + 0.0034496(CX). The validation model showed similar coefficients and R2 = 0.99. The second validation, using observed versus predicted values, also yielded an R2 of 0.99 and a mean absolute error of 1.57 g. Correlation and principal component analyses revealed strong positive associations among body weight, area, axes, and predicted values. Computer vision proved effective for predicting tilapia fingerlings’ weight.