Ammonia emissions, impacts, and mitigation strategies for poultry production: A critical review.

Confined animal feeding operations (CAFOs) are the main sources of air pollutants such as ammonia (NH3) and greenhouse gases. Among air pollutants, NH3 is one of the most concerned gasses in terms of air quality, environmental impacts, and manure nutrient losses. It is recommended that NH3 concentrations in the poultry house should be controlled below 25 ppm. Otherwise, the poor air quality will impair the health and welfare of animals and their caretakers. After releasing from poultry houses, NH3 contributes to the form of fine particulate matters in the air and acidify soil and water bodies after deposition. Therefore, understanding the emission influential factors and impacts is critical for developing mitigation strategies to protect animals' welfare and health, environment, and ecosystems. This review paper summarized the primary NH3 emission influential factors, such as how poultry housing systems, seasonal changes, feed management, bedding materials, animal densities, and animals' activities can impact indoor air quality and emissions. A higher level of NH3 (e.g., >25 ppm) results in lower production efficiency and poor welfare and health, e.g., respiratory disorder, less feed intake, lower growth rates or egg production, poor feed use efficiency, increased susceptibility to infectious diseases, and mortality. In addition, the egg quality (e.g., albumen height, pH, and condensation) was reduced after laying hens chronically exposed to high NH3 levels. High NH3 levels have detrimental effects on farm workers' health as it is a corrosive substance to eyes, skin, and respiratory tract, and thus may cause blindness, irritation (throat, nose, eyes), and lung illness. For controlling poultry house NH3 levels and emissions, we analyzed various mitigation strategies such as litter additives, biofiltration, acid scrubber, dietary manipulation, and bedding materials. Litter additives were tested with 50% efficiency in broiler houses and 80-90% mitigation efficiency for cage-free hen litter at a higher application rate (0.9 kg m-2). Filtration systems such as multi-stage acid scrubbers have up to 95% efficiency on NH3 mitigation. However, cautions should be paid as mitigation strategies could be cost prohibitive for farmers, which needs assistances or subsidies from governments.

Precision Landing Test and Simulation of the Agricultural UAV on Apron.

Unmanned aerial vehicle (UAV) has been used to assist agricultural production. Precision landing control of UAV is critical for application of it in some specific areas such as greenhouses or livestock/poultry houses. For controlling UAV landing on a fixed or mobile apron/platform accurately, this study proposed an automatic method and tested it under three scenarios: (1) UAV landing at high operating altitude based on the GPS signal of the mobile apron; (2) UAV landing at low operating altitude based on the image recognition on the mobile apron; and (3) UAV landing progress control based on the fixed landing device and image detection to achieve a stable landing action. To verify the effectiveness of the proposed control method, apron at both stationary and mobile (e.g., 3 km/h moving speed) statuses were tested. Besides, a simulation was conducted for the UAV landing on a fixed apron by using a commercial poultry house as a model (135 L × 15 W × 3 H m). Results show that the average landing errors in high altitude and low altitude can be controlled within 6.78 cm and 13.29 cm, respectively. For the poultry house simulation, the landing errors were 6.22 ± 2.59 cm, 6.79 ± 3.26 cm, and 7.14 ± 2.41cm at the running speed of 2 km/h, 3 km/h, and 4 km/h, respectively. This study provides the basis for applying the UAV in agricultural facilities such as poultry or animal houses where requires a stricter landing control than open fields.

A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution.

The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken's floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities.

Tracking dustbathing behavior of cage-free laying hens with machine vision technologies.

Dustbathing (DB) is a functionally important maintenance behavior in birds that clean plumage, realigns feather structures, removes feather lipids, which helps to remove parasites and prevents feathers from becoming too oily. Among different natural behaviors birds perform in cage-free (CF) housing, DB is one of the important behavior related to bird welfare. Earlier studies have identified DB behavior using manual method such as counting number of DB bouts, and duration of DB bouts from video recordings. The manual detection of DB behavior is time-consuming, sometimes prone to errors, and have limitations. Therefore, an automated precision monitoring method is needed to detect DB behavior in laying hens from an early age in CF housing environment. The objectives of this study were to (1) develop and test a deep learning model for detecting DB behavior and find out the optimal model; and (2) assess the performance of the optimal model in detecting DB behavior at different growing phases. In this study, deep learning models, i.e., YOLOv7-DB, YOLOv7x-DB, YOLOv8s-DB and YOLOv8x-DB, networks, were developed, trained, and compared in tracking DB behavior in 4 CF rooms each with 200 hens (W-36 Hy-Line). Results indicate that the YOLOv8x-DB model outperform all other models on detecting DB behavior with a precision of 93.4%, recall of 91.20%, and mean average precision (mAP@0.50) of 93.70%. All models performed with over 90% detection precision; however, model performance was affected by equipment like drinking lines, perches, and feeders. Based on the optimal model (YOLOv8x-DB), DB detection precision was highest during grower phase (precision of 96.80%, recall of 97.10%, mAP@0.50 of 98.60%, and mAP@0.50-0.95 of 79.10% followed by prelay, layers, developer, and peaking phases. This study provides a reference for poultry and egg producers that DB behavior can be detected automatically with precision of at least 89% or more using optimal model at any growing phase of laying hens.

Tracking perching behavior of cage-free laying hens with deep learning technologies.

Providing perches in cage-free (CF) housing offers significant benefits for laying hens, such as improved leg muscle development, bone health, reduced abdominal fat, and decreased fear and aggression. A precise detection method is essential to ensure that hens engage in perching behavior from an early age, as manual observation is often labor-intensive and sometimes inaccurate. The objectives of this study were to (1) develop and test a deep learning model for detecting perching behavior; and (2) evaluate the optimal model's performance on detecting perching behavior of laying hens of different ages. In this study, recent deep learning models, that is, YOLOv8s-PB, YOLOv8x-PB, YOLOv7-PB, and YOLOv7x-PB, were developed, trained and compared in detecting perching behavior in 4 CF rooms (200 hens/room). Perch height was up to 1.8 m from the litter floor and situated 1.5 m below the cameras. A total of 3,000 images were used, with each image featuring at least 1 hen perching. The models' detection accuracies and their performance across different age groups of hens were compared using 1-way ANOVA at a 5% significance level. The results showed that the YOLOv8x-PB model outperform all other models used, achieving the precision of 94.80%, recall of 95.10%, and mean average precision (mAP@0.50) of 97.60%. While all models proved over 94% detection precision. With optimal model, PB detection precision was highest (97.40%) for peaking phase followed by prelay (95.20%), grower (94.80%), developer (94.70%) and layers (92.70%) phases while the lowest detection precision (88.80%) was for starter phase. Detection performance was somewhat reduced by the overlapping of birds during perching and occlusion. Overall, the YOLOv8x-PB model was the most optimal in detecting perching behavior, proposing a valuable tool for CF producers to monitor the perching activities of laying hens automatically.

Monitoring activity index and behaviors of cage-free hens with advanced deep learning technologies.

Chickens' behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNN) models were developed to monitor the chicken activity index. A dataset consisting of 1,500 top-view images was utilized to construct tracking models, with 900 images allocated for training, 300 for validation, and 300 for testing. Six different CNN models were developed, based on YOLOv5, YOLOv8, ByteTrack, DeepSORT, and StrongSORT. The final results demonstrated that the combination of YOLOv8 and DeepSORT exhibited the highest performance, achieving a multiobject tracking accuracy (MOTA) of 94%. Further application of the optimal model could facilitate the detection of abnormal behaviors such as smothering and piling, and enabled the quantification of flock activity into 3 levels (low, medium, and high) to evaluate footpad health states in the flock. This research underscores the application of deep learning in monitoring poultry activity index for assessing animal health and welfare.

Automatic detection of bumblefoot in cage-free hens using computer vision technologies.

Cage-free (CF) housing systems are expected to be the dominant egg production system in North America and European Union countries by 2030. Within these systems, bumblefoot (a common bacterial infection and chronic inflammatory reaction) is mostly observed in hens reared on litter floors. It causes pain and stress in hens and is detrimental to their welfare. For instance, hens with bumblefoot have difficulty moving freely, thus hindering access to feeders and drinkers. However, it is technically challenging to detect hens with bumblefoot, and no automatic methods have been applied for hens' bumblefoot detection (BFD), especially in its early stages. This study aimed to develop and test artificial intelligence methods (i.e., deep learning models) to detect hens' bumblefoot condition in a CF environment under various settings such as epochs (number of times the entire dataset passes through the network during training), batch size (number of data samples processed per iteration during training), and camera height. The performance of 3 newly developed deep learning models (i.e., YOLOv5s-BFD, YOLOv5m-BFD, & YOLOv5x-BFD) were compared in detecting hens with bumblefoot of hens in CF environments. The result shows that the YOLOv5m-BFD model had the highest precision (93.7%), recall (84.6%), mAP@0.50 (90.9%), mAP@0.50:0.95 (51.8%), and F1-score (89.0%) compared with other models. The observed YOLOv5m-BFD model trained at 400 epochs and batch size 16 is recommended for bumblefoot detection in laying hens. This study provides a basis for developing an automatic bumblefoot detection system in commercial CF houses. This model will be modified and trained to detect the occurrence of broilers with bumblefoot in the future.

Deep Learning Methods for Tracking the Locomotion of Individual Chickens.

Poultry locomotion is an important indicator of animal health, welfare, and productivity. Traditional methodologies such as manual observation or the use of wearable devices encounter significant challenges, including potential stress induction and behavioral alteration in animals. This research introduced an innovative approach that employs an enhanced track anything model (TAM) to track chickens in various experimental settings for locomotion analysis. Utilizing a dataset comprising both dyed and undyed broilers and layers, the TAM model was adapted and rigorously evaluated for its capability in non-intrusively tracking and analyzing poultry movement by intersection over union (mIoU) and the root mean square error (RMSE). The findings underscore TAM's superior segmentation and tracking capabilities, particularly its exemplary performance against other state-of-the-art models, such as YOLO (you only look once) models of YOLOv5 and YOLOv8, and its high mIoU values (93.12%) across diverse chicken categories. Moreover, the model demonstrated notable accuracy in speed detection, as evidenced by an RMSE value of 0.02 m/s, offering a technologically advanced, consistent, and non-intrusive method for tracking and estimating the locomotion speed of chickens. This research not only substantiates TAM as a potent tool for detailed poultry behavior analysis and monitoring but also illuminates its potential applicability in broader livestock monitoring scenarios, thereby contributing to the enhancement of animal welfare and management in poultry farming through automated, non-intrusive monitoring and analysis.

Electrostatic particle ionization for suppressing air pollutants in cage-free layer facilities.

The increasing demand for cage-free (CF) poultry farming raises concern regarding air pollutant emissions in these housing systems. Previous studies have indicated that air pollutants such as particulate matter (PM) and ammonia (NH3) pose substantial risks to the health of birds and workers. This study aimed to evaluate the efficacy of electrostatic particle ionization (EPI) technology with different lengths of ion precipitators in reducing air pollutants and investigate the relationship between PM reduction and electricity consumption. Four identical CF rooms were utilized, each accommodating 175 hens of 77 wk of age (WOA). A Latin Square Design method was employed, with 4 treatment lengths: T1 = control (0 m), T2 = 12 ft (3.7 m), T3 = 24 ft (7.3 m), and T4 = 36 ft (11.0 m), where room and WOA are considered as blocking factors. Daily PM concentrations, temperature, and humidity measurements were conducted over 24 h, while NH3 levels, litter moisture content (LMC), and ventilation were measured twice a week in each treatment room. Statistical analysis involved ANOVA, and mean comparisons were performed using the Tukey HSD method with a significance level of P ≤ 0.05. This study found that the EPI system with longer wires reduced PM2.5 concentrations (P ≤ 0.01). Treatment T2, T3, and T4 led to reductions in PM2.5 by 12.1%, 19.3%, and 31.7%, respectively, and in small particle concentrations (particle size >0.5 µm) by 18.0%, 21.1%, and 32.4%, respectively. However, no significant differences were observed for PM10 and large particles (particle size >2.5 µm) (P < 0.10), though the data suggests potential reductions in PM10 (32.7%) and large particles (33.3%) by the T4 treatment. Similarly, there was no significant impact of treatment on NH3 reduction (P = 0.712), possibly due to low NH3 concentration (<2 ppm) and low LMC (<13%) among treatment rooms. Electricity consumption was significantly related to the length of the EPI system (P ≤ 0.01), with longer lengths leading to higher consumption rates. Overall, a longer-length EPI corona pipe is recommended for better air pollutant reduction in CF housing. Further research should focus on enhancing EPI technology, assessing cost-effectiveness, and exploring combinations with other PM reduction strategies.

Sustainable poultry farming practices: a critical review of current strategies and future prospects.

As global demand for poultry products, environmental sustainability, and health consciousness rises with time, the poultry industry faces both substantial challenges and new opportunities. Therefore, this review paper provides a comprehensive overview of sustainable poultry farming, focusing on integrating genetic improvements, alternative feed, precision technologies, waste management, and biotechnological innovations. Together, these strategies aim to minimize ecological footprints, uphold ethical standards, improve economic feasibility, and enhance industry resilience. In addition, this review paper explores various sustainable strategies, including eco-conscious organic farming practices and innovative feed sources like insect-based proteins, single-cell proteins, algal supplements, and food waste utilization. It also addresses barriers to adoption, such as technical challenges, financial constraints, knowledge gaps, and policy frameworks, which are crucial for advancing the poultry industry. This paper examined organic poultry farming in detail, noting several benefits like reduced pesticide use and improved animal welfare. Additionally, it discusses optimizing feed efficiency, an alternate energy source (solar photovoltaic/thermal), effective waste management, and the importance of poultry welfare. Transformative strategies, such as holistic farming systems and integrated approaches, are proposed to improve resource use and nutrient cycling and promote climate-smart agricultural practices. The review underscores the need for a structured roadmap, education, and extension services through digital platforms and participatory learning to promote sustainable poultry farming for future generations. It emphasizes the need for collaboration and knowledge exchange among stakeholders and the crucial role of researchers, policymakers, and industry professionals in shaping a future where sustainable poultry practices lead the industry, committed to ethical and resilient poultry production.

A Computer Vision-Based Automatic System for Egg Grading and Defect Detection.

Defective eggs diminish the value of laying hen production, particularly in cage-free systems with a higher incidence of floor eggs. To enhance quality, machine vision and image processing have facilitated the development of automated grading and defect detection systems. Additionally, egg measurement systems utilize weight-sorting for optimal market value. However, few studies have integrated deep learning and machine vision techniques for combined egg classification and weighting. To address this gap, a two-stage model was developed based on real-time multitask detection (RTMDet) and random forest networks to predict egg category and weight. The model uses convolutional neural network (CNN) and regression techniques were used to perform joint egg classification and weighing. RTMDet was used to sort and extract egg features for classification, and a Random Forest algorithm was used to predict egg weight based on the extracted features (major axis and minor axis). The results of the study showed that the best achieved accuracy was 94.8% and best R2 was 96.0%. In addition, the model can be used to automatically exclude non-standard-size eggs and eggs with exterior issues (e.g., calcium deposit, stains, and cracks). This detector is among the first models that perform the joint function of egg-sorting and weighing eggs, and is capable of classifying them into five categories (intact, crack, bloody, floor, and non-standard) and measuring them up to jumbo size. By implementing the findings of this study, the poultry industry can reduce costs and increase productivity, ultimately leading to better-quality products for consumers.

Mislaying behavior detection in cage-free hens with deep learning technologies.

Floor egg-laying behavior (FELB) is one of the most concerning issues in commercial cage-free (CF) houses because floor eggs (i.e., mislaid eggs on the floor) result in high labor costs and food safety concerns. Farms with poor management may have up to 10% of daily floor eggs. Therefore, it is critical to improving floor eggs management. Detecting FELB timely and identifying the reason behind its cause may address the issue. The primary objectives of this research were to develop and test a new deep-learning model to detect FELB and evaluate the model's performance in 4 identical research CF houses (200 Hy-Line W-36 hens per house), where perches and litter floor were provided to mimic commercial tiered aviary system. Five different YOLOv5 models (i.e., YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) were trained and compared. According to a dataset of 5400 images (i.e., 3780 for training, 1080 for validation, and 540 for testing), YOLOv5m-FELB and YOLOv5x-FELB models were tested with higher precision (99.9%), recall (99.2%), mAP@0.50 (99.6%), and F1-score (99.6%) than others. However, the YOLOv5m-NFELB model has lower recall than other YOLOv5-NFELB models, although it was tested with higher precision. Similarly, the speed of data processing (4%-45% FPS), and training time (3%-148%) were higher in the YOLOv5s model while requiring less GPU (1.8-4.8 times) than in other models. Furthermore, the camera height of 0.5 m and clean camera outperform compared to 3 m height and dusty camera. Thus, the newly developed and trained YOLOv5s model will be further innovated. Future studies will be conducted to verify the performance of the model in commercial CF houses to detect FELB.

Tracking floor eggs with machine vision in cage-free hen houses.

Some of the major restaurants and grocery chains in the United States have pledged to buy cage-free (CF) eggs only by 2025 or 2030. While CF house allows hens to perform more natural behaviors (e.g., dust bathing, perching, and foraging on the litter floor), a particular challenge is floor eggs (i.e., mislaid eggs on litter floor). Floor eggs have high chances of contamination. The manual collection of eggs is laborious and time-consuming. Therefore, precision poultry farming technology is necessary to detect floor eggs. In this study, 3 new deep learning models, that is, YOLOv5s-egg, YOLOv5x-egg, and YOLOv7-egg networks, were developed, trained, and compared in tracking floor eggs in 4 research cage-free laying hen facilities. Models were verified to detect eggs by using images collected in 2 different commercial houses. Results indicate that the YOLOv5s-egg model detected floor eggs with a precision of 87.9%, recall of 86.8%, and mean average precision (mAP) of 90.9%; the YOLOv5x-egg model detected the floor eggs with a precision of 90%, recall of 87.9%, and mAP of 92.1%; and the YOLOv7-egg model detected the eggs with a precision of 89.5%, recall of 85.4%, and mAP of 88%. All models performed with over 85% detection precision; however, model performance is affected by the stocking density, varying light intensity, and images occluded by equipment like drinking lines, perches, and feeders. The YOLOv5x-egg model detected floor eggs with higher accuracy, precision, mAP, and recall than YOLOv5s-egg and YOLOv7-egg. This study provides a reference for cage-free producers that floor eggs can be monitored automatically. Future studies are guaranteed to test the system in commercial houses.

Osteoimmunology: A Link between Gastrointestinal Diseases and Skeletal Health in Chickens.

Bone serves as a multifunctional organ in avian species, giving structural integrity to the body, aiding locomotion and flight, regulating mineral homeostasis, and supplementing calcium for eggshell formation. Furthermore, immune cells originate and reside in the bone marrow, sharing a milieu with bone cells, indicating a potential interaction in functions. In avian species, the prevalence of gastrointestinal diseases can alter the growth and the immune response, which costs a great fortune to the poultry industry. Previous studies have shown that coccidiosis and necrotic enteritis can dramatically reduce bone quality as well. However, possible mechanisms on how bone quality is influenced by these disease conditions have not yet been completely understood, other than the reduced feed intake. On the other hand, several mediators of the immune response, such as chemokines and cytokines, play a vital role in the differentiation and activation of osteoclasts responsible for bone resorption and osteoblasts for bone formation. In the case of Eimeria spp./Clostridium perfringens coinfection, these mediators are upregulated. One possible mechanism for accelerated bone loss after gastrointestinal illnesses might be immune-mediated osteoclastogenesis via cytokines-RANKL-mediated pathways. This review article thus focuses on osteoimmunological pathways and the interaction between host immune responses and bone biology in gastrointestinal diseases like coccidiosis and necrotic enteritis affecting skeletal health.

Automatic detection of brown hens in cage-free houses with deep learning methods.

Computer vision technologies have been tested to monitor animals' behaviors and performance. High stocking density and small body size of chickens such as broiler and cage-free layers make effective automated monitoring quite challenging. Therefore, it is critical to improve the accuracy and robustness of laying hens clustering detection. In this study, we established a laying hens detection model YOLOv5-C3CBAM-BiFPN, and tested its performance in detecting birds on open litter. The model consists of 3 parts: 1) the basic YOLOv5 model for feature extraction and target detection of laying hens; 2) the convolution block attention module integrated with C3 module (C3CBAM) to improve the detection effect of targets and occluded targets; and 3) bidirectional feature pyramid network (BiFPN), which is used to enhance the transmission of feature information between different network layers and improve the accuracy of the algorithm. In order to better evaluate the effectiveness of the new model, a total of 720 images containing different numbers of laying hens were selected to construct complex datasets with different occlusion degrees and densities. In addition, this paper also compared the proposed model with a YOLOv5 model that combined other attention mechanisms. The test results show that the improved model YOLOv5-C3CBAM-BiFPN achieved a precision of 98.2%, a recall of 92.9%, a mAP (IoU = 0.5) of 96.7%, a classification rate 156.3 f/s (frames per second), and a F1 (F1 score) of 95.4%. In other words, the laying hen detection method based on deep learning proposed in the present study has excellent performance, can identify the target accurately and quickly, and can be applied to real-time detection of laying hens in real-world production environment.

Interindividual distances and orientations of laying hens under 8 stocking densities measured by integrative deep learning techniques.

Interindividual distances and orientations of laying hens provide quantitative measures to calculate and optimize space allocations for bird flocks. However, these metrics were often measured manually and have not been examined for different stocking densities of laying hens. The objectives of this study were to 1) integrate and develop several deep learning techniques to detect interindividual distances and orientations of laying hens; and 2) examine the 2 metrics under 8 stocking densities via the developed techniques. Laying hens (Jingfen breed, a popular hen breed in China) at 35 wk of age were raised in experimental compartments at 8 different stocking densities of 3,840, 2,880, 2,304, 1,920, 1,646, 1,440, 1,280, and 1,152 cm2•bird-1 (3-10 hens per compartment, respectively), and cameras on the top of the compartments recorded videos for further analysis. The designed deep learning image classifier achieved over 99% accuracy to classify bird's perching status and excluded frames with bird perching to ensure that all birds analyzed were on the same horizontal plane, reducing calculation errors. The YOLOv5m oriented object detection model achieved over 90% precision, recall, and F1 score in detecting birds in compartments and can output bird centroid coordinates and angles, from which interindividual distances and orientations were calculated based on pairs of birds. Laying hens maintained smaller minimum interindividual distances in higher stocking densities. They were in an intersecting relationship with conspecifics for over 90% of the time. The developed integrative deep learning techniques and behavior metrics provide animal-based measurement of space requirement for laying hens.

A Deep Learning Model for Detecting Cage-Free Hens on the Litter Floor.

Real-time and automatic detection of chickens (e.g., laying hens and broilers) is the cornerstone of precision poultry farming based on image recognition. However, such identification becomes more challenging under cage-free conditions comparing to caged hens. In this study, we developed a deep learning model (YOLOv5x-hens) based on YOLOv5, an advanced convolutional neural network (CNN), to monitor hens' behaviors in cage-free facilities. More than 1000 images were used to train the model and an additional 200 images were adopted to test it. One-way ANOVA and Tukey HSD analyses were conducted using JMP software (JMP Pro 16 for Mac, SAS Institute, Cary, North Caronia) to determine whether there are significant differences between the predicted number of hens and the actual number of hens under various situations (i.e., age, light intensity, and observational angles). The difference was considered significant at p < 0.05. Our results show that the evaluation metrics (Precision, Recall, F1 and mAP@0.5) of the YOLOv5x-hens model were 0.96, 0.96, 0.96 and 0.95, respectively, in detecting hens on the litter floor. The newly developed YOLOv5x-hens was tested with stable performances in detecting birds under different lighting intensities, angles, and ages over 8 weeks (i.e., birds were 8−16 weeks old). For instance, the model was tested with 95% accuracy after the birds were 8 weeks old. However, younger chicks such as one-week old birds were harder to be tracked (e.g., only 25% accuracy) due to interferences of equipment such as feeders, drink lines, and perches. According to further data analysis, the model performed efficiently in real-time detection with an overall accuracy more than 95%, which is the key step for the tracking of individual birds for evaluation of production and welfare. However, there are some limitations of the current version of the model. Error detections came from highly overlapped stock, uneven light intensity, and images occluded by equipment (i.e., drinking line and feeder). Future research is needed to address those issues for a higher detection. The current study established a novel CNN deep learning model in research cage-free facilities for the detection of hens, which provides a technical basis for developing a machine vision system for tracking individual birds for evaluation of the animals' behaviors and welfare status in commercial cage-free houses.

Monitoring Behaviors of Broiler Chickens at Different Ages with Deep Learning.

Animal behavior monitoring allows the gathering of animal health information and living habits and is an important technical means in precision animal farming. To quickly and accurately identify the behavior of broilers at different days, we adopted different deep learning behavior recognition models. Firstly, the top-view images of broilers at 2, 9, 16 and 23 days were obtained. In each stage, 300 images of each of the four broilers behaviors (i.e., feeding, drinking, standing, and resting) were segmented, totaling 4800 images. After image augmentation processing, 10,200 images were generated for each day including 8000 training sets, 2000 validation sets, and 200 testing sets. Finally, the performance of different convolutional neural network models (CNN) in broiler behavior recognition at different days was analyzed. The results show that the overall performance of the DenseNet-264 network was the best, with the accuracy rates of 88.5%, 97%, 94.5%, and 90% when birds were 2, 9, 16 and 23 days old, respectively. In addition, the efficient channel attention was introduced into the DenseNet-264 network (ECA-DenseNet-264), and the results (accuracy rates: 85%, 95%, 92%, 89.5%) confirmed that the DenseNet-264 network was still the best overall. The research results demonstrate that it is feasible to apply deep learning technology to monitor the behavior of broilers at different days.

COVID-19 in Farm Animals: Host Susceptibility and Prevention Strategies.

COVID-19 is caused by the virus SARS-CoV-2 that belongings to the family of Coronaviridae, which has affected multiple species and demonstrated zoonotic potential. The COVID-19 infections have been reported on farm animals (e.g., minks) and pets, which were discussed and summarized in this study. Although the damage of COVID-19 has not been reported as serious as highly pathogenic avian influenza (HPAI) for poultry and African Swine Fever (ASF) for pigs on commercial farms so far, the transmission mechanism of COVID-19 among group animals/farms and its long-term impacts are still not clear. Prior to the marketing of efficient vaccines for livestock and animals, on-farm biosecurity measures (e.g., conventional disinfection strategies and innovated technologies) need to be considered or innovated in preventing the direct contact spread or the airborne transmission of COVID-19.

A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment.

The presence equipment (e.g., water pipes, feed buckets, and other presence equipment, etc.) in the poultry house can occlude the areas of broiler chickens taken via top view. This can affect the analysis of chicken behaviors through a vision-based machine learning imaging method. In our previous study, we developed a machine vision-based method for monitoring the broiler chicken floor distribution, and here we processed and restored the areas of broiler chickens which were occluded by presence equipment. To verify the performance of the developed restoration method, a top-view video of broiler chickens was recorded in two research broiler houses (240 birds equally raised in 12 pens per house). First, a target detection algorithm was used to initially detect the target areas in each image, and then Hough transform and color features were used to remove the occlusion equipment in the detection result further. In poultry images, the broiler chicken occluded by equipment has either two areas (TA) or one area (OA). To reconstruct the occluded area of broiler chickens, the linear restoration method and the elliptical fitting restoration method were developed and tested. Three evaluation indices of the overlap rate (OR), false-positive rate (FPR), and false-negative rate (FNR) were used to evaluate the restoration method. From images collected on d2, d9, d16, and d23, about 100-sample images were selected for testing the proposed method. And then, around 80 high-quality broiler areas detected were further evaluated for occlusion restoration. According to the results, the average value of OR, FPR, and FNR for TA was 0.8150, 0.0032, and 0.1850, respectively. For OA, the average values of OR, FPR, and FNR were 0.8788, 0.2227, and 0.1212, respectively. The study provides a new method for restoring occluded chicken areas that can hamper the success of vision-based machine predictions.