[OLATUNDE EMMANUEL]

Deep learning can analyze poultry behavior related to feeding and lighting changes by <mark>using computer vision and machine learning algorithms to identify and track poultry movements, behaviors, and spatial distributions</mark>, enabling early detection of issues and optimization of farm management.

[Amir]

Deep learning can analyze poultry behavior related to feeding and lighting changes by using computer vision techniques like CNNs to analyze videos and images, enabling automated detection of behaviors like feeding, drinking and responses to lighting changes, which can improve poultry welfare and production.

[Olayiwola]

Behavior Analysis: Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), can be trained to recognize and classify various poultry behaviors from video or image data.

Examples of Behaviors:

Feeding: Identifying when and how often birds are feeding, where they are feeding, and the duration of feeding bouts.

Drinking: Monitoring water consumption patterns and identifying potential issues with water access or quality.

Floor Distribution: Tracking the movement and spatial distribution of birds within the pen, which can indicate stress, overcrowding, or other welfare issues.

Other behaviors: Deep learning can also be used to detect and classify other behaviors like preening, resting, and social interactions.

Lighting analysis:

Light intensity and color: Deep learning can be used to analyze the effects of different lighting conditions (intensity, color, and duration) on poultry behavior.

Behavioral changes: Researchers have found that changes in lighting can affect pecking activity, aggression, and resting behavior.

Data Collection:

Video Cameras: Video cameras are used to capture images and videos of poultry behavior in real-time.

Sensors: Other sensors, such as RFID tags, can be used to track individual birds and their movements.

Deep Learning Models:

YOLO (You Look Once): YOLO models are known for their speed and accuracy in object detection and tracking, making them suitable for real-time poultry monitoring.

CNNs (Convolutional Neutral Networks): CNNs are widely used for image and video analysis, allowing for the identification of complex patterns in poultry behavior.

LSTM (Long Short-Term Memory): LSTM networks can be used to analyze sequential data, such as video sequences, to identify temporal patterns in poultry behavior.

Applications:

Early Disease Detection: Identifying subtle behavioral changes that could indicate early signs of disease or stress.

Welfare Monitoring: Assessing the overall welfare of the flock by monitoring their behaviors and activity levels.

Optimizing Management Practices: Using data on poultry behavior to optimize feeding strategies, lighting programs, and other management practices.

[Md. Osman Sheikh]

Poultry behaviors reflect the health status of poultry. For four behaviors of laying hens, such as standing, lying, feeding, and grooming, four deep learning methods.

[Muhammad Zeeshan Asghar]

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