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Given that common egg counting methods in conventional layer farms are inefficient and costly, there is a growing demand for cost-effective solutions with high counting accuracy, expandable functionality, and flexibility that can be easily shared between different coops. However, accurate real-time egg counting faces challenges due to small size, density variation, and egg similarity, exacerbated by dynamic poses. Moreover, current animal industry methods emphasize single-image counting, limiting suitability for video-based counting due to a lack of frame-to-frame target association. The you only look once version 5-DeepSORT-spatial encoding (YOLO v5-DSE) algorithm is proposed as a solution for efficient and reliable egg counting to tackle these issues. The algorithm contains the following three main modules: 1) the egg detector utilizes the improved YOLOv5 to locate eggs in video frames automatically, 2) the DeepSORT-based tracking module is employed to continuously track each egg's position between frames, preventing the detector from losing egg localization, and 3) the spatial encoding (SE) module is designed to count eggs. Extensive experiments are conducted on 4808 eggs on a commercial farm. Our proposed egg-counting approach achieves a counting accuracy of 99.52% and a speed of 22.57 fps, surpassing not only the DeepSORT-SE and ByteTrack-SE versions of eight advanced YOLO-series object detectors (YOLOX, and YOLOv6-v9) but also other egg-counting methods. The proposed YOLOv5-DSE provides real-time and reliable egg counting for commercial layer farms. This approach could be further expanded to the egg conveyor to locate cages for low-lying hens and help companies cull more efficiently.

Light is an important factor affecting the feather pecking behavior in poultry. To evaluate the quality of lighting in production of a local broiler breed, this study was designed to investigate the effects of light color on the growth performance (body weight, feed conversion ratio and upper beak length), welfare parameters (walking step, plumage damage and hormone level) relating to feather pecking and their correlations. One hundred and twenty 49-day-old Youhuang broilers were randomly distributed into 3 lighting treatment groups (warm white, cold white and red) and reared for 4 weeks. The results showed that the lighting had no significant effect on the growth performance (P >0.05). The daily walking steps of birds in cold white group were greater than birds in warm white and red group at 8 (P <0.05) rather than 9 to 11 (P >0.05) weeks of age. Compared with birds in red group, the birds in cold white group had lower plumage scores (P <0.05) and the birds in warm white group had similar plumage scores (P >0.05). Lower scores meant severer plumage damage. Moreover, the plumage scores of backs and wings were both lower than those on tails (P <0.05). The serotonin levels in blood of birds in warm white group were greater than those in cold white group (P <0.05) but lower than those in red group (P <0.05). However, during the entire study, there were no correlations among body weight gain ratios, upper beak length gain ratios, average daily walking steps, total plumage scores of different body parts, and serotonin levels of 24 selected birds in three light treatment groups (P >0.05). Considering the friendliness of white light to human eye, warm white light should be recommended to reduce pecking behavior in production.

Poultry behavior indicates their health, welfare, and production performance. Timely access to broilers’ behavioral information can improve their welfare and reduce disease spread. Most behaviors require a period of observation before they can be accurately judged. However, the existing approaches for multi-object behavior recognition were mostly developed based on a single-frame image and ignored the temporal features in videos, which led to misrecognition. This study proposed an end-to-end method for recognizing multiple simultaneous behavioral events of cage-free broilers in videos by Broiler Behavior Recognition System (BBRS) based on spatiotemporal feature learning. The BBRS consisted of 3 main components: the improved YOLOv8s detector, the Bytetrack tracker, and the 3D-ResNet50-TSAM model. The basic network YOLOv8s was improved with MPDIoU to identify multiple broilers in the same frame of videos. The Bytetrack tracker was used to track each identified broiler and acquire its image sequence of 32 continuous frames as input for the 3D-ResNet50-TSAM model. To accurately recognize behavior of each tracked broiler, the 3D-ResNet50-TSAM model integrated a temporal-spatial attention module for learning the spatiotemporal features from its image sequence and enhancing inference ability in the case of its image sequence less than 32 continuous frames due to its tracker ID switching. Each component of BBRS was trained and tested with the rearing density of 7 to 8 birds/m2. The results demonstrated that the mAP@0.5 of the improved YOLOv8s detector was 99.50%. The Bytetrack tracker achieved a mean MOTA of 93.89% at different levels of occlusion. The Accuracy, Precision, Recall, and F1scoreof the 3D-ResNet50-TSAM model were 97.84, 97.72, 97.65, and 97.68%, respectively. The BBRS showed satisfactory inference ability with an Accuracyof 93.98% when 26 continuous frames of the tracked broiler were received by the 3D-ResNet50-TSAM model. This study provides an efficient tool for automatically and accurately recognizing behaviors of cage-free multi-broilers in videos. The code will be released on GitHub (https://github.com/CoderYLH/BBRS) as soon as the study is published.

The characteristics of chicken droppings are closely linked to their health status. In prior studies, chicken droppings recognition is treated as an object detection task, leading to challenges in labeling and missed detection due to the diverse shapes, overlapping boundaries, and dense distribution of chicken droppings. Additionally, the use of intelligent monitoring equipment equipped with edge devices in farms can significantly reduce manual labor. However, the limited computational power of edge devices presents challenges in deploying real-time segmentation algorithms for field applications. Therefore, this study redefines the task as a segmentation task, with the main objective being the development of a lightweight segmentation model for the automated monitoring of abnormal chicken droppings. A total of 60 Arbor Acres broilers were housed in 5 specific pathogen-free cages for over 3 wk, and 1650 RGB images of chicken droppings were randomly divided into training and testing sets in an 8:2 ratio to develop and test the model. Firstly, by incorporating the attention mechanism, multi-loss function, and auxiliary segmentation head, the segmentation accuracy of the DDRNet was enhanced. Then, by employing the group convolution and an advanced knowledge-distillation algorithm, a lightweight segmentation model named DDRNet-s-KD was obtained, which achieved a mean Dice coefficient (mDice) of 79.43% and an inference speed of 86.10 frames per second (FPS), showing a 2.91% and 61.2% increase in mDice and FPS compared to the benchmark model. Furthermore, the DDRNet-s-KD model was quantized from 32-bit floating-point values to 8-bit integers and then converted to TensorRT format. Impressively, the weight size of the quantized model was only 13.7 MB, representing an 82.96% reduction compared to the benchmark model. This makes it well-suited for deployment on the edge device, achieving an inference speed of 137.51 FPS on Jetson Xavier NX. In conclusion, the methods proposed in this study show significant potential in monitoring abnormal chicken droppings and can provide an effective reference for the implementation of other agricultural embedded systems.1.A high-performance and lightweight chicken droppings segmentation model is proposed, which has the significant application potential in the early warning of chicken diseases.2.The process of model lightweight and deployment is proposed, contributing to the development of edge smart equipment serving agricultural tasks.The characteristics of chicken droppings are closely related to their health. In this study, we developed a lightweight segmentation model for chicken droppings and evaluated its inference speed on the edge device with limited computational power. The results showed that the proposed model exhibits significant potential in the early warning of abnormal chicken droppings, which can help producers implement interventions before disease outbreaks, thereby avoiding great economic losses. Additionally, the model optimization and compression processes proposed in this study can provide an effective reference for the implementation of other embedded systems.

Light intensity is an important aspect for broiler production. However, previous results do not provide a solid scientific basis for quantifying the response of broilers to light intensity. This study performed a meta-analysis to model the response of broilers to 0.1−200 lux of light intensity. Meta-analysis was used to integrate smaller studies and increase the statistical power over that of any single study and explore new hypotheses. The results indicated that light intensity <5 lux caused welfare concern (P< 0.05) and light intensity <1 lux induced productivity loss of broiler (P< 0.05), whereas greater level of light intensity >10 lux led to increased mortality (P< 0.01) and decreased uniformity (P< 0.05). Meta-regression showed that 30−200 lux light intensity was negatively related to BW (P= 0.047) and feed intake change (P= 0.054), whereas a quadratic relationship was observed between feed conversion ratio change and 50−180 lux light intensity (R2= 0.95). In addition, the majority of carcass characteristics (abdominal fat weight and wing weight) and metabolic indicators (K+, Ca2+, and T3) were affected by light intensity >5 lux. To conclude, this meta-analysis based on published data quantitatively identified that 5 lux of light intensity during grow-out period should be the minimum level to maintain a well productivity and welfare of broiler chickens.

Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. Light is an important factor that affects the pecking behavior of poultry. A wooden box was built to investigate the effects of Light Emitting Diode (LED) light color (warm white and cold white) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used to simulate a red object and paired with another piece of paper (Paper 2 to 8) with a different color. Bigger number of the paired paper indicated greater color difference. The experiment consisted of three phases: adaptation, training, and test. In the adaptation phase, birds were selected for the adaptation to reduce the stress from the box. In the training phase, birds were trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Twenty-three birds were tested when the paired paper was changed from Paper 7 to 2. Each pair of paper included 12 trials for every bird, and response time to peck and proportion of choices of Paper 1 in the last 10 trials were collected. The results showed that broilers tested under 5 lux light had longer response times than broilers tested under 50 lux light (P< 0.05). When Paper 1 was paired with paper 7, broilers tested under warm white light had lower proportion of choices of Paper 1 than those tested under cold white light (P< 0.05). Color difference had a significant effect on response time of broilers (P< 0.05). Moreover, the proportion of choices of Paper 1 decreased to 50% (chance-level performance) when color of the paired paper was gradually similar to Paper 1. Conclusively, rearing broilers in warm white rather than cold white light with appropriate light intensity should be recommended to reduce damaging pecking behavior in broiler production.Light color and intensity will affect the discrimination of red objects in broiler.Poultry are sensitive to red objects, such as comb and blood on the body surface, likely inducing injurious pecking in flocks. We built a wooden box to investigate the effects of light color (reddish and bluish) and intensity (5 and 50 lux) of background light on the discrimination of red objects in broilers. A piece of red photographic paper (Paper 1) was used and paired with another piece of paper (Paper 2 to 8) with a different color. Every bird was trained to discriminate and peck at Paper 1 when paired with Paper 8 under one type of background light. Then, Paper 8 was changed from Paper 7 to 2. Response time to peck and proportion of choices of Paper 1 were collected. We found that broilers under 5 lux light had longer response times than under 50 lux light. Broilers under reddish light had lower proportion of choices than under bluish light. Moreover, color difference had a significant effect on the response time and the proportion of choices. Conclusively, rearing broilers under reddish rather than bluish light with appropriate intensity should be recommended to reduce damaging pecking behavior in broiler production.

Individual egg identification technology has potential applications in breeding, egg production, and anti-counterfeiting of high-end brand eggs. This study developed a novel method for its individual identification based on eggshell images. A convolutional neural network-based model, named Eggshell Biometric Identification (EBI) model, was proposed and evaluated. The main workflow included eggshell biometric feature extraction, egg information registration, and egg identification. The image dataset of individual eggshell was first collected from the blunt end region of 770 chicken eggs using an image acquisition platform. The ResNeXt network was then trained as a texture feature extraction module to obtain sufficient eggshell texture features. The EBI model was applied to a test set of 1540 images. The results showed that when an appropriate Euclidean distance threshold for classification was set (17.18), the correct recognition rate (CRR) and the equal error rate (EER) reached 99.96% and 0.02%. This new method provides an efficient and accurate solution for individual egg identification for product tracing and anti-counterfeiting.