Broiler productivity is dependent on a range of variables; among them, the rearing environment is a significant factor for proper well-being and productivity. Behavior indicates the bird’s initial response to an adverse environment and is capable of providing an indicator of well-being in real-time. The present study aims to identify and characterize the sequential pattern of broilers’ behavior when exposed to thermoneutral conditions (TNZ) and thermal stress (HS) by constant heat. The research was carried out in a climatic chamber with 18 broilers under thermoneutral conditions and heat stress for three consecutive days (at three different ages). The behavior database was first analyzed using one-way ANOVA, Tukey test by age, and Boxplot graphs, and then the sequence of the behaviors was evaluated using the generalized sequential pattern (GSP) algorithm. We were able to predict behavioral patterns at the different temperatures assessed from the behavioral sequences. Birds in HS were prostrate, identified by the shorter behavioral sequence, such as the {Lying down, Eating} pattern, unlike TNZ ({Lying down, Walking, Drinking, Walking, Lying down}), which indicates a tendency to increase behaviors (feeding and locomotor activities) that guarantee the better welfare of the birds. The sequence of behaviors ‘Lying down’ followed by ‘Lying laterally’ occurred only in HS, which represents a stressful thermal environment for the bird. Using the pattern mining sequences approach, we were able to identify temporal relationships between thermal stress and broiler behavior, confirming the need for further studies on the use of temporal behavior sequences in environmental controllers.

O estudo foi realizado com objetivo de avaliar os parâmetros fisiológicos de poedeiras alojadas em diferentes densidades de alojamento e períodos. Foram utilizadas 150 poedeiras Dekalb White com 20 semanas de idade alojadas em um galpão convencional com gaiolas metálicas com duas subdivisões de 50x45x40 cm2 e distribuídas em três tratamentos: 8 aves por gaiola (562,5 cm2/ave); 10 aves por gaiola (450 cm2/ave) e 12 aves por gaiola (375 cm2/ave), com cinco repetições cada. Avaliaram-se parâmetros fisiológicos (frequência respiratória, temperatura cloacal e temperatura superficial) e condições ambientais (temperatura ambiente e umidade relativa) durante três ciclos de 28 dias. Adotou-se um delineamento inteiramente casualizado com arranjo fatorial 3x3 (três densidades de alojamento e três períodos). Os resultados demonstraram que a densidade de alojamento não prejudicou a frequência respiratória e a temperatura superficial, porém, a temperatura cloacal diferiu significativamente, sendo superior às 16 horas e a densidade de alojamento com 10 aves/gaiola permaneceu intermediário às outras densidades. Os parâmetros voltados à busca do conforto térmico evidenciaram que nos horários mais quentes do dia (até as 16:00 horas), independente da densidade, as aves apresentaram temperaturas maiores que na parte da manhã.

The genetic development of the commercial broiler has led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broilers that were 40 d old (male and female) were placed to walk on a specially built runway, and their locomotion was recorded. An image segmentation algorithm was developed, and the coordinates of the bird’s center of mass were extracted from the segmented images for each frame analyzed, and the unrest index (UI) was applied. We calculated the center of mass’s movement of the broiler walking lateral images capturing the bird’s displacement speed in the onward direction. Results indicated that broiler walking speed on the runway tends to decrease with the increase of the gait score. The locomotion did not differ between males or females. The proposed algorithm was efficient in predicting the broiler gait score based on their displacement speed.

The study aimed to forecast ammonia exposure risk in broiler chicken production, correlating it with health injuries using machine learning. Two chicken breeds, fast-growing (Ross^®) and slow-growing (Hubbard^®), were compared at different densities. Slow-growing birds had a constant density of 32 kg m⁻², while fast-growing birds had low (16 kg m⁻²) and high (32 kg m⁻²) densities. Initial feeding was uniform, but nutritional demands led to varied diets later. Environmental data underwent selection, pre-processing, transformation, mining, analysis, and interpretation. Classification algorithms (decision tree, SMO, Naive Bayes, and Multilayer Perceptron) were employed for predicting ammonia risk (10–14 pmm, Moderate risk). Cross-validation was used for model parameterization. The Spearman correlation coefficient assessed the link between predicted ammonia risk and health injuries, such as pododermatitis, vision/affected, and mucosal injuries. These injuries encompassed trachea, bronchi, lungs, eyes, paws, and other issues. The Multilayer Perceptron model emerged as the best predictor, exceeding 98% accuracy in forecasting injuries caused by ammonia. The correlation coefficient demonstrated a strong association between elevated ammonia risks and chicken injuries. Birds exposed to higher ammonia concentrations exhibited a more robust correlation. In conclusion, the study effectively used machine learning to predict ammonia exposure risk and correlated it with health injuries in broiler chickens. The Multilayer Perceptron model demonstrated superior accuracy in forecasting injuries related to ammonia (10–14 pmm, Moderate risk). The findings underscored the significant association between increased ammonia exposure risks and the incidence of health injuries in broiler chicken production, shedding light on the importance of managing ammonia levels for bird welfare.