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Precision Poultry Farming
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Precision Poultry Farming

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 44138

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Yang Zhao

E-Mail Website
Guest Editor
Department of Animal Science, The University of Tennessee, Knoxville, TN, USA
Interests: animal smart sensoring; robotics; behavior monitoring; welfare assessment; airborne transmission of pathogens; and environment management
Special Issues, Collections and Topics in MDPI journals
Dr. María Cambra-López

E-Mail Website
Guest Editor
Institute for Animal Science and Technology, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: animal production; feeding animals; animal nutrition; environment; precision poultry farming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Daniella Jorge De Moura

E-Mail Website
Guest Editor
Agricultural Engineering College, State University of Campinas, Campinas, Brazil
Interests: precision livestock farming; animal welfare; smart housing
Special Issues, Collections and Topics in MDPI journals
Dr. Weichao Zheng

E-Mail Website
Guest Editor
Department of Agricultural Structure and Bioenvironmental Engineering, China Agricultural University, Beijing, China
Interests: poultry environment control; biosecurity; precision poultry farming

Special Issue Information

The increase of global population comes along with growing demands on protein resources. To meet such demands, world poultry meat and egg production soared in the past few decades and is projected to keep growing in decades to come. While the poultry production makes crucial contributions to food and nutrition security, it uses substantial natural and human resources and has significant impacts on society, economy, public health, and environment. Although the extent of these impacts may vary among continents and countries due to differences in production practices and social structures and preferences, the world poultry industry, as a whole, should strive to keep improving sustainability and efficiency in the resource usage. Precision Poultry Farming (PPF) features applications of continuous, objective, and automated sensing technologies and computer tools for sustainable and efficient poultry production; and it offers solutions to poultry industry to address challenges in terms of poultry management, environment, nutrition, automation and robotics, health, welfare assessment, behavior monitoring, waste management, etc.

We invite original research papers, on a global scale, that address sustainability and efficiency of poultry industry and explore above mentioned areas through applications of PPF solutions in poultry meat and egg production for this special issue of “Precision Poultry Farming”. 

Dr. Yang Zhao
Dr. María Cambra-López
Dr. Daniella Jorge de Moura
Dr. Weichao Zheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Precision Poultry Farming
  • Precision Livestock Farming
  • Precision nutrition
  • Poultry environment
  • Poultry production system
  • Poultry robotics
  • Poultry health
  • Poultry welfare and behavior
  • Poultry biosecurity
  • Sensors
  • IoT
  • Machine learning and deep learning
  • Deep learning
  • Big data
  • Computational fluid dynamics (CFD)

Published Papers (12 papers)

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Research

11 pages, 630 KiB  
Article
Assessment of Husbandry Practices That Can Reduce the Negative Effects of Exposure to Low Ammonia Concentrations in Broiler Houses
by Leonardo V. S. Barbosa, Daniella J. De Moura, Fernando Estellés, Adrian Ramón-Moragues, Salvador Calvet and Arantxa Villagrá
Animals 2022, 12(9), 1096; https://doi.org/10.3390/ani12091096 - 23 Apr 2022
Cited by 2 | Viewed by 1509
Abstract
Ammonia is an important pollutant emitted by broiler litter that can accumulate inside farms, impairing animal health and welfare productivity. An experiment was designed to evaluate of precision husbandry practices such as the effect of ventilation, animal density and growth rate as management [...] Read more.
Ammonia is an important pollutant emitted by broiler litter that can accumulate inside farms, impairing animal health and welfare productivity. An experiment was designed to evaluate of precision husbandry practices such as the effect of ventilation, animal density and growth rate as management options to reduce the adverse effects of ammonia exposure on productive parameters in broiler houses. Two identical experimental rooms were used in this study. They were programmed to differ in ammonia concentration from day 32 of the growing period (10 and 20 ppm in Room 1 and Room 2, respectively). Three treatments were tested in each room: slow growth in high stocking density (SHD), fast growth in low density (FLD) and fast growth in high density (FHD). Animal weight, feed intake and feed conversion ratio were determined weekly. In addition, the immune status of animals was assessed by weighing the organs related to immune response as stress indicators. Increasing ventilation was effective to control ammonia concentrations. Exposure to ammonia caused no significant effect on productive parameters. However, lowering stocking density improved response to higher ammonia concentrations by lowering the feed conversion ratio. No other relevant effects of differential exposure to ammonia were found in fast-growing animals, either at high or low stocking density. The use of slow-growing breeds had no effect on production parameters. Despite having a slower growth rate, their feed conversion ratio was not different from that of fast-growing breeds. The productive performance of slow-growing animals was not affected by the differential exposure to ammonia, but the reduced spleen size would suggest an impairment of the immune system. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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16 pages, 14011 KiB  
Article
Biomarkers and De Novo Protein Design Can Improve Precise Amino Acid Nutrition in Broilers
by María Cambra-López, Pablo Jesús Marín-García, Clara Lledó, Alba Cerisuelo and Juan José Pascual
Animals 2022, 12(7), 935; https://doi.org/10.3390/ani12070935 - 06 Apr 2022
Cited by 1 | Viewed by 2625
Abstract
Precision nutrition in broilers requires tools capable of identifying amino acid imbalances individually or in groups, as well as knowledge on how more digestible proteins can be designed for innovative feeding programs adjusted to animals’ dynamic requirements. This work proposes two potential tools, [...] Read more.
Precision nutrition in broilers requires tools capable of identifying amino acid imbalances individually or in groups, as well as knowledge on how more digestible proteins can be designed for innovative feeding programs adjusted to animals’ dynamic requirements. This work proposes two potential tools, combining traditional nutrition with biotechnological, metabolomic, computational and protein engineering knowledge, which can contribute to improving the precise amino acid nutrition of broilers in the future: (i) the use of serum uric nitrogen content as a rapid biomarker of amino acid imbalances, and (ii) the design and modeling of de novo proteins that are fully digestible and fit exactly to the animal’s requirements. Each application is illustrated with a case study. Case study 1 demonstrates that serum uric nitrogen can be a useful rapid indicator of individual or group amino acid deficiencies or imbalances when reducing dietary protein and adjusting the valine and arginine to lysine ratios in broilers. Case study 2 describes a stepwise approach to design an ideal protein, resulting in a potential amino acid sequence and structure prototype that is ideally adjusted to the requirements of the targeted animal, and is theoretically completely digestible. Both tools can open up new opportunities to form an integrated framework for precise amino acid nutrition in broilers, helping us to achieve more efficient, resilient, and sustainable production. This information can help to determine the exact ratio of amino acids that will improve the efficiency of the use of nitrogen by poultry. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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12 pages, 2570 KiB  
Article
Computer-Vision-Based Indexes for Analyzing Broiler Response to Rearing Environment: A Proof of Concept
by Juliana Maria Massari, Daniella Jorge de Moura, Irenilza de Alencar Nääs, Danilo Florentino Pereira and Tatiane Branco
Animals 2022, 12(7), 846; https://doi.org/10.3390/ani12070846 - 28 Mar 2022
Cited by 2 | Viewed by 2279
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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15 pages, 1333 KiB  
Article
Implementation of Inertia Sensor and Machine Learning Technologies for Analyzing the Behavior of Individual Laying Hens
by Sayed M. Derakhshani, Matthias Overduin, Thea G. C. M. van Niekerk and Peter W. G. Groot Koerkamp
Animals 2022, 12(5), 536; https://doi.org/10.3390/ani12050536 - 22 Feb 2022
Cited by 8 | Viewed by 1873
Abstract
Welfare-oriented regulations cause farmers worldwide to shift towards more welfare-friendly, e.g., loose housing systems such as aviaries with litter. In contrast to the traditional cage housing systems, good technical results can only be obtained if the behavior of hens is considered. With increasing [...] Read more.
Welfare-oriented regulations cause farmers worldwide to shift towards more welfare-friendly, e.g., loose housing systems such as aviaries with litter. In contrast to the traditional cage housing systems, good technical results can only be obtained if the behavior of hens is considered. With increasing flock sizes, the automation of behavioural assessment can be beneficial. This research aims to show a proof of principle of tools for analyzing laying-hen behaviors by using wearable inertia sensor technology and a machine learning model (ML). For this aim, the behaviors of hens were classified into three classes: static, semi-dynamic, and highly dynamic behavior. The activities of hens were continuously recorded on video and synchronized with the sensor signals. Two hens were equipped with sensors, one marked green and one blue, for five days to collect the data. The training data set indicated that the ML model can accurately classify the highly dynamic behaviors with a one-second time window; a four-second time window is accurate for static and semi-dynamic behaviors. The Bagged Trees model, with an overall accuracy of 89% was the best ML model with the F1-scores of 89%, 91%, and 87% for static, semi-dynamic, and highly dynamic behaviors. The Bagged Trees model also performed well in classifying the behaviors of the hen in the validation data set with an overall F1-score of 0.92 (uniform either % or decimals). This research illustrates that the combination of wearable inertia sensors and machine learning is a viable technique for analyzing the laying-hen behaviors and supporting farmers in the management of hens in loose housing systems. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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14 pages, 4708 KiB  
Article
New Insights into the Hourly Manure Coverage Proportion on the Manure Belt in a Typical Layer House for Accurate Ammonia Emission Modeling
by Li Yang, Chaowu Yang, Chenming Hu, Chunlin Yu, Siyang Liu, Shiliang Zhu, Mohan Qiu, Hongqiang Zhu, Lingzhi Xie and Longhuan Du
Animals 2021, 11(8), 2433; https://doi.org/10.3390/ani11082433 - 18 Aug 2021
Cited by 1 | Viewed by 2253
Abstract
The main advantage of having livestock, for example, the laying hens, in a controlled environment is that the optimum growth conditions can be achieved with accuracy. The indoor air temperature, humidity, gases concentration, etc., would significantly affect the animal performance, thus should be [...] Read more.
The main advantage of having livestock, for example, the laying hens, in a controlled environment is that the optimum growth conditions can be achieved with accuracy. The indoor air temperature, humidity, gases concentration, etc., would significantly affect the animal performance, thus should be maintained within an acceptable range. In order to achieve the goals of precision poultry farming, various models have been developed by researchers all over the world to estimate the hourly indoor environmental parameters so as to provide decision suggestions. However, a key parameter of hourly manure area in the poultry house was missing in the literature to predict the ammonia emission using the recently developed mechanistic model. Therefore, in order to fill the gap of the understanding of hourly manure coverage proportion and area on the manure belt, experimental measurements were performed in the present study using laying hens from 10 weeks age to 30 weeks age. For each test, six polypropylene (pp) plates were applied to collect the manure dropped by the birds every hour, and photographs of the plates were taken at the same time using a pre-fixed camera. Binary images were then produced based on the color pictures to determine the object coverage proportion. It was demonstrated that for laying hens of stocking density around 14 birds/m2, the manure coverage proportion at the 24th hour after the most recent manure removal was about 60%, while the value was approximately 82% at the 48th hour. Meanwhile, for laying hens at different ages, the hourly increment of manure coverage proportion showed a similar pattern with four distinct stages within 48 h. The statistical analyses demonstrated no significant correlation between the hourly increment of manure weight and the hourly increment of manure coverage proportion. Finally, prediction models for estimating the hourly manure coverage proportion on the manure belt in typical laying hen houses were provided. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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14 pages, 2447 KiB  
Article
Analysis of Cluster and Unrest Behaviors of Laying Hens Housed under Different Thermal Conditions and Light Wave Length
by Aline Mirella Fernandes, Diogo de Lucca Sartori, Flávio José de Oliveira Morais, Douglas D’Alessandro Salgado and Danilo Florentino Pereira
Animals 2021, 11(7), 2017; https://doi.org/10.3390/ani11072017 - 06 Jul 2021
Cited by 9 | Viewed by 3757
Abstract
Laying hens are affected by the intensity, wavelength, and duration of light, and the behavioral patterns of these animals are important indicators of stress. The objective of the present study was to evaluate cluster and unrest behaviors of lying hens submitted to three [...] Read more.
Laying hens are affected by the intensity, wavelength, and duration of light, and the behavioral patterns of these animals are important indicators of stress. The objective of the present study was to evaluate cluster and unrest behaviors of lying hens submitted to three environments with different treatments of monochromatic lighting (blue, green, and red). For 29 weeks, 60 laying hens from the Lohmann variety were divided into three groups and monitored by surveillance cameras installed on each shed ceiling and directed to the floor. Each group was housed in a small-scale shed and maintained under a monochromatic lighting treatment. The recordings were made at two times of the day, 15 min in the morning and 15 min in the afternoon, and the videos were processed, segmented, and analyzed computationally. From the analysis of the images, the cluster and unrest indexes were calculated. The results showed the influence of lighting on these behaviors, displaying that the birds were more agitated in the treatments with shorter wavelengths. Cluster behavior was higher in birds housed under red light. There was an interaction between the lighting treatments and the thermal environment, indicating that more studies should be carried out in this area to better understand these behavioral changes. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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11 pages, 4942 KiB  
Article
Characterizing Sounds of Different Sources in a Commercial Broiler House
by Xiao Yang, Yang Zhao, Hairong Qi and George T. Tabler
Animals 2021, 11(3), 916; https://doi.org/10.3390/ani11030916 - 23 Mar 2021
Cited by 2 | Viewed by 2571
Abstract
Audio data collected in commercial broiler houses are mixed sounds of different sources that contain useful information regarding bird health condition, bird behavior, and equipment operation. However, characterizations of the sounds of different sources in commercial broiler houses have not been well established. [...] Read more.
Audio data collected in commercial broiler houses are mixed sounds of different sources that contain useful information regarding bird health condition, bird behavior, and equipment operation. However, characterizations of the sounds of different sources in commercial broiler houses have not been well established. The objective of this study was, therefore, to determine the frequency ranges of six common sounds, including bird vocalization, fan, feed system, heater, wing flapping, and dustbathing, at bird ages of week 1 to 8 in a commercial Ross 708 broiler house. In addition, the frequencies of flapping (in wing flapping events, flaps/s) and scratching (during dustbathing, scratches/s) behaviors were examined through sound analysis. A microphone was installed in the middle of broiler house at the height of 40 cm above the back of birds to record audio data at a sampling frequency of 44,100 Hz. A top-view camera was installed to continuously monitor bird activities. Total of 85 min audio data were manually labeled and fed to MATLAB for analysis. The audio data were decomposed using Maximum Overlap Discrete Wavelet Transform (MODWT). Decompositions of the six concerned sound sources were then transformed with the Fast Fourier Transform (FFT) method to generate the single-sided amplitude spectrums. By fitting the amplitude spectrum of each sound source into a Gaussian regression model, its frequency range was determined as the span of the three standard deviations (99% CI) away from the mean. The behavioral frequencies were determined by examining the spectrograms of wing flapping and dustbathing sounds. They were calculated by dividing the number of movements by the time duration of complete behavioral events. The frequency ranges of bird vocalization changed from 2481 ± 191–4409 ± 136 Hz to 1058 ± 123–2501 ± 88 Hz as birds grew. For the sound of fan, the frequency range increased from 129 ± 36–1141 ± 50 Hz to 454 ± 86–1449 ± 75 Hz over the flock. The sound frequencies of feed system, heater, wing flapping and dustbathing varied from 0 Hz to over 18,000 Hz. The behavioral frequencies of wing flapping were continuously decreased from week 3 (17 ± 4 flaps/s) to week 8 (10 ± 1 flaps/s). For dustbathing, the behavioral frequencies decreased from 16 ± 2 scratches/s in week 3 to 11 ± 1 scratches/s in week 6. In conclusion, characterizing sounds of different sound sources in commercial broiler houses provides useful information for further advanced acoustic analysis that may assist farm management in continuous monitoring of animal health and behavior. It should be noted that this study was conducted with one flock in a commercial house. The generalization of the results remains to be explored. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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15 pages, 6509 KiB  
Article
Design and Implementation of Poultry Farming Information Management System Based on Cloud Database
by Haikun Zheng, Tiemin Zhang, Cheng Fang, Jiayuan Zeng and Xiuli Yang
Animals 2021, 11(3), 900; https://doi.org/10.3390/ani11030900 - 22 Mar 2021
Cited by 20 | Viewed by 11729
Abstract
Aiming at breaking down the bottleneck problems of different scale of poultry farms, the low profitability of poultry farming, and backward information management in China, a safe and efficient information management system for poultry farming was designed. This system consists of (1) a [...] Read more.
Aiming at breaking down the bottleneck problems of different scale of poultry farms, the low profitability of poultry farming, and backward information management in China, a safe and efficient information management system for poultry farming was designed. This system consists of (1) a management system application layer, (2) a data service layer, and (3) an information sensing layer. The information sensing layer obtains and uploads production and farming information through the wireless sensor network built in the poultry house. The use of a cloud database as an information storage carrier in the data service layer eliminates the complex status of deploying local server clusters, and it improves the flexibility and scalability of the system. The management system application layer contains many sub-function modules including poultry disease detection functions to realize the visual management of farming information and health farming; each module operates independently and cooperates with each other to form a set of information management system for poultry farming with wide functional coverage, high service efficiency, safety, and convenience. The system prototype has been tested for the performance of wireless sensor network and cloud database, and the results show that the prototype is capable of acquiring and managing poultry farming information. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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11 pages, 1587 KiB  
Article
Smart Feeding Unit for Measuring the Pecking Force in Farmed Broilers
by Rogério Torres Seber, Daniella Jorge de Moura, Nilsa Duarte da Silva Lima and Irenilza de Alencar Nääs
Animals 2021, 11(3), 864; https://doi.org/10.3390/ani11030864 - 18 Mar 2021
Cited by 1 | Viewed by 2776
Abstract
Feeding is one of the most critical processes in the broiler production cycle. A feeder can collect data of force signals and continuously transform it into information about birds’ feed intake and quickly permit more agile and more precise decision-making concerning the broiler [...] Read more.
Feeding is one of the most critical processes in the broiler production cycle. A feeder can collect data of force signals and continuously transform it into information about birds’ feed intake and quickly permit more agile and more precise decision-making concerning the broiler farm’s production process. A smart feeding unit (SFU) prototype was developed to evaluate the broiler pecking force and average feed intake per pecking (g/min). The prototype consisted of a power supply unit with a data acquisition module, management software connected to a computer for data storage, and a video camera to verify the pecking force during signal processing. In the present study, seven male Cobb-500 broilers were raised in an experimental chamber to test and commission the prototype. The prototype consisted of a feeding unit (feeder) with a data acquisition module (amplifier), with real-time integration for testing and intuitive operation with Catman Easy software connected to a computer to obtain and store data from signals. The sampling of average feed intake per pecking per broiler (g) was conducted during the first minute of feeding, subtracting the amount of feed provided per the amount of feed consumed, including the count of pecking in the first minute of feeding. An equation was used for estimating the average feed intake per pecking per broiler (g). The results showed that the average broiler pecking force was 1.39 N, with a minimum value of 0.04 N and a maximum value of 7.29 N. The average feed intake per pecking (FIP) was 0.13 g, with an average of 173 peckings per minute. The acquisition, processing, and classification of signals in the pecking force information were valuable during broilers’ feeding. The smart feeding unit prototype for broilers was efficient in the continuous assessment of feed intake and can generate information for estimating broiler performance. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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11 pages, 4364 KiB  
Article
A Machine Vision-Based Method Optimized for Restoring Broiler Chicken Images Occluded by Feeding and Drinking Equipment
by Yangyang Guo, Samuel E. Aggrey, Adelumola Oladeinde, Jasmine Johnson, Gregory Zock and Lilong Chai
Animals 2021, 11(1), 123; https://doi.org/10.3390/ani11010123 - 08 Jan 2021
Cited by 17 | Viewed by 2989
Abstract
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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18 pages, 3132 KiB  
Article
Developing and Evaluating Poultry Preening Behavior Detectors via Mask Region-Based Convolutional Neural Network
by Guoming Li, Xue Hui, Fei Lin and Yang Zhao
Animals 2020, 10(10), 1762; https://doi.org/10.3390/ani10101762 - 28 Sep 2020
Cited by 18 | Viewed by 2789
Abstract
There is a lack of precision tools for automated poultry preening monitoring. The objective of this study was to develop poultry preening behavior detectors using mask R-CNN. Thirty 38-week brown hens were kept in an experimental pen. A surveillance system was installed above [...] Read more.
There is a lack of precision tools for automated poultry preening monitoring. The objective of this study was to develop poultry preening behavior detectors using mask R-CNN. Thirty 38-week brown hens were kept in an experimental pen. A surveillance system was installed above the pen to record images for developing the behavior detectors. The results show that the mask R-CNN had 87.2 ± 1.0% MIOU, 85.1 ± 2.8% precision, 88.1 ± 3.1% recall, 95.8 ± 1.0% specificity, 94.2 ± 0.6% accuracy, 86.5 ± 1.3% F1 score, 84.3 ± 2.8% average precision and 380.1 ± 13.6 ms·image−1 processing speed. The six ResNets (ResNet18-ResNet1000) had disadvantages and advantages in different aspects of detection performance. Training parts of the complex network and transferring some pre-trained weights from the detectors pre-trained in other datasets can save training time but did not compromise detection performance and various datasets can result in different transfer learning efficiencies. Resizing and padding input images to different sizes did not affect detection performance of the detectors. The detectors performed similarly within 100–500 region proposals. Temporal and spatial preening behaviors of individual hens were characterized using the trained detector. In sum, the mask R-CNN preening behavior detector could be a useful tool to automatically identify preening behaviors of individual hens in group settings. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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11 pages, 1342 KiB  
Article
Accuracy of Broiler Activity Index as Affected by Sampling Time Interval
by Xiao Yang, Yang Zhao and George T. Tabler
Animals 2020, 10(6), 1102; https://doi.org/10.3390/ani10061102 - 26 Jun 2020
Cited by 6 | Viewed by 2486
Abstract
Different time intervals between consecutive images have been used to determine broiler activity index (AI). However, the accuracy of broiler AI as affected by sampling time interval remains to be explored. The objective of this study was to investigate the effect of the [...] Read more.
Different time intervals between consecutive images have been used to determine broiler activity index (AI). However, the accuracy of broiler AI as affected by sampling time interval remains to be explored. The objective of this study was to investigate the effect of the sampling time interval (0.04, 0.2, 1, 10, 60, and 300 s) on the accuracy of broiler AI at different bird ages (1–7 weeks), locations (feeder, drinker, and open areas) and times of day (06:00–07:00 h, 12:00–13:00 h, and 18:00–19:00 h). A ceiling-mounted camera was used to capture top-view videos for broiler AI calculations. The results show that the sampling time interval of 0.04 s yielded the highest broiler AI because more bird motion details were captured at this short time interval. The broiler AIs at longer time intervals were 1–99% of that determined at the 0.04-s interval. The broiler AI at 0.2-s interval showed an acceptable accuracy with 80% less computational resources. Broiler AI decreased as birds aged but increased after week 4 at the drinker area. Broiler AI was the highest at the open area for weeks 1–4 and at the feeder and drinker areas for weeks 5–7. It is concluded that the accuracy of broiler AI was significantly affected by sampling time intervals. Broiler AI in commercial housing showed both temporal and spatial variations. Full article
(This article belongs to the Special Issue Precision Poultry Farming)
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