Predicting Risk of Ammonia Exposure in Broiler Housing: Correlation with Incidence of Health Issues.

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.

Assessment of Preference Behavior of Layer Hens under Different Light Colors and Temperature Environments in Long-Time Footage Using a Computer Vision System.

As for all birds, the behavior of chickens is largely determined by environmental conditions. In many production systems, light intensity is low and red feather strains have low contrast with the background, making it impossible to use conventional image segmentation techniques. On the other hand, studies of chicken behavior, even when using video camera resources, depend on human vision to extract the information of interest; and in this case, reduced samples are observed, due to the high cost of time and energy. Our work combined the use of advanced object detection techniques using YOLO v4 architecture to locate chickens in low-quality videos, and we automatically extracted information on the location of birds in more than 648 h of footage. We develop an automated system that allows the chickens to transition among three environments with different illuminations equipped with video cameras to monitor the presence of birds in each compartment, and we automatically count the number of birds in each compartment and determine their preference. Our chicken detection algorithm shows a mean average precision of 99.9%, and a manual inspection of the results showed an accuracy of 98.8%. Behavioral analysis results based on bird unrest index and permanence time indicate that chickens tend to prefer white light and disfavor green light, except in the presence of heat stress when no clear preference can be observed. This study demonstrates the potential of using computer vision techniques with low-resolution, low-cost cameras to monitor chickens in low-light conditions.

Broiler behavior differs from males to females when under different light wavelengths.

It is well established that different light wavelengths affect broiler behavior. The present study aims to evaluate the effect of four light wavelengths on broiler behavior from 1 to 42 days of age. Birds were housed at a stocking density of 13 birds/m2, in 32 boxes of 1.56 m2. The experimental design was a completely randomized factorial of 4 × 2 (four colors × two sexes), with four replicates. Behavioral variables were accessed through cameras and observed in person thrice a week for 30 min per day in three different periods. Data were organized according to age groups and analyzed by a data mining approach with the different light wavelengths as the classes. Natural behavior defined by stretch, dust bath of male broilers reared in environments with green and blue light was more relevant to the classification of male broilers' behavior (96.9 and 96.9% accuracy and 0.8 and 1.0 of class precision of behavior classification, respectively). Blue and green lights affected the behavior of male broilers starting at 7 days of age, increasing the presence at the bird feeder, and reducing the idle period.

Computer-Vision-Based Indexes for Analyzing Broiler Response to Rearing Environment: A Proof of Concept.

Computer-vision systems for herd detection and monitoring are increasingly present in precision livestock. This technology provides insights into how environmental variations affect the group's movement pattern. We hypothesize that the cluster and unrest indexes based on computer vision (CV) can simultaneously assess the movement variation of reared broilers under different environmental conditions. The present study is a proof of principle and was carried out with twenty broilers (commercial strain Cobb®), housed in a controlled-environment chamber. The birds were divided into two groups, one housed in an enriched environment and the control. Both groups were subjected to thermal comfort conditions and heat stress. Image analysis of individual or group behavior is the basis for generating animal-monitoring indexes, capable of creating real-time alert systems, predicting welfare, health, environment, and production status. The results obtained in the experiment in a controlled environment allowed the validation of the simultaneous application of cluster and unrest indexes by monitoring the movement of the group of broilers under different environmental conditions. Observational results also suggest that research in more significant proportions should be carried out to evaluate the potential positive impact of environmental enrichment in poultry production. The complexity of the environment is a factor to be considered in creating alert systems for detecting heat stress in broiler production. In large groups, birds' movement and grouping patterns may differ; therefore, the CV system and indices will need to be recalibrated.

Behavioral responses of turkeys subjected to different climatic conditions.

I was evaluated the effect of seven different combinations of temperature, air velocity, and relative air humidity on the frequency and duration of eating, drinking, resting, cannibalism, dust bathing, scratching, ground pecking, shivering, and stretching behaviors of turkeys at three different ages. The combinations tested of temperature, relative air humidity, and air velocity were, respectively: 1 (22 °C, 50%, 1 m/s); 2 (26.2 °C, 73.2%, 0.45 m/s); 3 (26.6 °C, 71.2%, 1 m/s); 4 (28.9 °C, 72%, 1.4 m/s); 5 (31.1 °C, 85%, 0.45 m/s); 6 (34.1 °C, 82.1%, 1 m/s); and 7 (34.4 °C, 82.1%, 1.4 m/s) for three ages of birds (61, 96, and 131 days of age). Seven birds were housed per pen, at a density of 3 males/m2, totaling 147 birds in the entire experiment. Each combination was applied for 5 days. The data were analyzed considering the number of times the bird performed the behavior and the time it performed (in seconds). Each pen was considered a repetition. A comparison of the medians was used to compare the treatments by each age. The results showed that young birds were more likely to suffer from the combination of low temperature and high air velocity, reducing their frequency of normal behaviors. Increased humidity at a low temperature raised the frequency of scratching, shivering, and cannibalism behaviors leading to poorer bird welfare. It is recommended that the temperature, relative air humidity, and air velocity combination of 26.6 °C; 71.2%; and 1 m/s, respectively, for young birds, and 22 °C; 50%; and 1 m/s, respectively, for older birds should be used.