Bernardo José Marques Ferreira

Accurate body weight monitoring is essential for efficient sheep production, yet conventional weighing methods are labor-intensive and require frequent animal handling. Computer vision provides a promising non-invasive alternative; however, many published studies rely on validation strategies that may overestimate predictive performance because repeated observations from the same animals are simultaneously included in training and testing datasets. This study developed and evaluated an integrated analytical framework for image-based body weight estimation in ewes that combines standardized image processing, robust frame-level quality control, longitudinal statistical modeling, and strict leakage-controlled validation. A longitudinal dataset comprising 20 Dorper ewes monitored over six sampling periods was acquired using top-view depth imaging synchronized with body weight measurements under semi-arid conditions. The analytical framework integrated three complementary modules: longitudinal mixed-effects modeling, early prediction of final body weight, and contemporaneous body weight estimation. Predictive analyses were evaluated using nested leave-one-animal-out cross-validation, in which all preprocessing, correlation filtering, feature selection, model optimization, and model selection were performed exclusively within the training animals of each outer fold. The longitudinal mixed-effects model accurately characterized individual growth trajectories (conditional R² = 0.98). Initial body weight remained the strongest predictor of final body weight (R² = 0.770), whereas image-derived morphometric descriptors alone showed limited predictive performance under strict animal-level validation (R² = −0.899 to −0.031). Combining baseline body weight with selected morphometric descriptors produced modest but biologically informative improvements, achieving a maximum R² of 0.839. Contemporaneous body weight estimation achieved moderate predictive performance (maximum R² = 0.332) and revealed temporal changes in the importance of morphometric descriptors throughout growth. Overall, the proposed framework provides a reproducible methodology for evaluating image-derived phenotypes under rigorous animal-level validation, contributing to the development of more robust, interpretable, and biologically grounded computer vision systems for Precision Livestock Farming.

The effect of incorporating of breadcrumbs, oatmeal and cassava starch on the physicochemical and sensorial parameters of low-fat lamb burgers were investigated. Seven treatments were prepared: control (CONT), 2% or 4% addition of breadcrumbs (BRE2 and BRE4), oatmeal flour (OAT2 and OAT4) and cassava flour (CAS2 and CAS4); and analyzed their physicochemical parameters (moisture, protein, fat, ash, carbohydrate, energy, water holding capacity and cooking loss). Furthermore, consumers evaluated the sensory acceptance (color, taste, texture, aroma and overall quality) and indicated their preference. The treatments affected the characteristics of the product (P<0.05), the highest values of fat (12.26%) carbohydrate (2.83%), energy (194.39Kcal/100g) and cooking loss (54.90%) were found in CAS2, CAS4, OAT4 and BRE2, respectively. CAS2 group improved the taste, while BRE4 improved the texture and along with CAS4 were the batches most preferred by consumers (18.8% and 21.5%, respectively). Breadcrumbs and cassava flour are most recommended for low-fat lamb burger production.