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Kunz Cechinel A, Soares CE, Pfleger SG, De Oliveira LLGA, Américo de Andrade E, Damo Bertoli C, De Rolt CR, De Pieri ER, Plentz PDM, Röning J. Mobile Robot + IoT: Project of Sustainable Technology for Sanitizing Broiler Poultry Litter. SENSORS (BASEL, SWITZERLAND) 2024; 24:3049. [PMID: 38793903 PMCID: PMC11125414 DOI: 10.3390/s24103049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 04/25/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024]
Abstract
The traditional aviary decontamination process involves farmers applying pesticides to the aviary's ground. These agricultural defenses are easily dispersed in the air, making the farmers susceptible to chronic diseases related to recurrent exposure. Industry 5.0 raises new pillars of research and innovation in transitioning to more sustainable, human-centric, and resilient companies. Based on these concepts, this paper presents a new aviary decontamination process that uses IoT and a robotic platform coupled with ozonizer (O3) and ultraviolet light (UVL). These clean technologies can successfully decontaminate poultry farms against pathogenic microorganisms, insects, and mites. Also, they can degrade toxic compounds used to control living organisms. This new decontamination process uses physicochemical information from the poultry litter through sensors installed in the environment, which allows accurate and safe disinfection. Different experimental tests were conducted to construct the system. First, tests related to measuring soil moisture, temperature, and pH were carried out, establishing the range of use and the confidence interval of the measurements. The robot's navigation uses a back-and-forth motion that parallels the aviary's longest side because it reduces the number of turns, reducing energy consumption. This task becomes more accessible because of the aviaries' standardized geometry. Furthermore, the prototype was tested in a real aviary to confirm the innovation, safety, and effectiveness of the proposal. Tests have shown that the UV + ozone combination is sufficient to disinfect this environment.
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Affiliation(s)
- Alan Kunz Cechinel
- Graduate Program in Automation and System Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil;
| | - Carlos Eduardo Soares
- Graduate Program in Food Sciences, Federal University of Santa Catarina, Florianópolis 88034-001, SC, Brazil;
| | - Sergio Genilson Pfleger
- Graduate Program in Computer Science, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
| | | | | | - Claudia Damo Bertoli
- Graduate Program in Plant and Animal Science, Catarinense Federal Institute, Camboriú 88340-055, SC, Brazil;
| | - Carlos Roberto De Rolt
- Graduate Program in Business Management and Socioeconomic Science—ESAG, State University of Santa Catarina—UDESC, Florianópolis 88035-001, SC, Brazil;
| | - Edson Roberto De Pieri
- Graduate Program in Automation and System Engineering, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil;
| | - Patricia Della Méa Plentz
- Graduate Program in Computer Science, Federal University of Santa Catarina, Florianópolis 88040-900, SC, Brazil
| | - Juha Röning
- Biomimetics and Intelligent Systems Group, University of Oulu, P.O. Box 4500, 90014 Oulu, Finland;
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Jie D, Wang J, Lv H, Wang H. Research on duck egg recognition algorithm based on improved YOLOv4. Br Poult Sci 2024; 65:223-232. [PMID: 38465873 DOI: 10.1080/00071668.2024.2308282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/03/2024] [Indexed: 03/12/2024]
Abstract
1. The following study addressed the problem of small duck eggs as challenging to detect and identify for pick up in complex free-range duck farm environments. It introduces improvements to the YOLOv4 convolutional neural network target detection algorithm, based on the working conditions of egg-picking robots.2. Specifically, one scale of anchor boxes was removed from the prediction network, and a duck egg labelling dataset was established to make the improved algorithm YOLOv4-ours better match the working state of egg-picking robots and enhance detection performance.3. Through multiple comparative experiments, the YOLOv4-ours object detection algorithm exhibited superior overall performance, achieving a precision of 98.85%, recall of 96.67%, and an average precision of 98.60% and F1 score increased to 97%. Compared to the original YOLOv4 model, these improvements represented increases of 1.89%, 3.41%, 1.32%, and 1.04%, respectively. Furthermore, detection time was reduced from 0.26 seconds per image to 0.20 seconds.4. The enhanced model accurately detected duck eggs in free-range duck housing, effectively meeting the real-time egg identification and picking requirements.
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Affiliation(s)
- D Jie
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - J Wang
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - H Lv
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
| | - H Wang
- College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
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da Rocha Balthazar G, Silveira RMF, da Silva IJO. How Do Escape Distance Behavior of Broiler Chickens Change in Response to a Mobile Robot Moving at Two Different Speeds? Animals (Basel) 2024; 14:1014. [PMID: 38612253 PMCID: PMC11011048 DOI: 10.3390/ani14071014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 04/14/2024] Open
Abstract
In poultry farming, robots are considered by birds as intruder elements to their environment, because animals escape due to their movement. Their escape is measured using the escape distance (ED) technique. This study analyzes the behavior of animals in relation to their ED through the use of a robot with two speeds: 12 rpm and 26 rpm. The objective is to understand whether the speeds cause variations in ED and their implications for animal stress. A broiler breeding cycle was analyzed (six weeks) through the introduction of the robot weekly. ED analyses were carried out on static images generated from footage of the robot running. The results indicate higher escape distance rates (p < 0.05) peaking midway through the production cycle, notably in the third week. Conversely, the final weeks saw the lowest ED, with the most significant reduction occurring in the last week. This pattern indicates a gradual escalation of ED up to the fourth week, followed by a subsequent decline. Despite RPM12 having shown low ED results, it did not show enough ED to move the animals away from their path of travel, causing bumps and collisions. RPM26 showed higher ED in all breeding phases, but showed ED with no bumps and collisions.
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Affiliation(s)
- Glauber da Rocha Balthazar
- Ambience Research Center, Department of Biosystems Engineering, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba 13418-900, Brazil; (R.M.F.S.); (I.J.O.d.S.)
- Analysis and Development Department, Federal Institute of Education, Science and Technology of São Paulo (IFSP), R. Heitor Lacerda Guedes, 1000-Cidade Satélite Íris, Campinas 13059-581, Brazil
| | - Robson Mateus Freitas Silveira
- Ambience Research Center, Department of Biosystems Engineering, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba 13418-900, Brazil; (R.M.F.S.); (I.J.O.d.S.)
| | - Iran José Oliveira da Silva
- Ambience Research Center, Department of Biosystems Engineering, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba 13418-900, Brazil; (R.M.F.S.); (I.J.O.d.S.)
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Liu HW, Chen CH, Tsai YC, Hsieh KW, Lin HT. Identifying Images of Dead Chickens with a Chicken Removal System Integrated with a Deep Learning Algorithm. SENSORS 2021; 21:s21113579. [PMID: 34063974 PMCID: PMC8196783 DOI: 10.3390/s21113579] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/16/2021] [Accepted: 05/17/2021] [Indexed: 01/02/2023]
Abstract
The chicken industry, in which broiler chickens are bred, is the largest poultry industry in Taiwan. In a traditional poultry house, breeders must usually observe the health of the broilers in person on the basis of their breeding experience at regular times every day. When a breeder finds unhealthy broilers, they are removed manually from the poultry house to prevent viruses from spreading in the poultry house. Therefore, in this study, we designed and constructed a novel small removal system for dead chickens for Taiwanese poultry houses. In the mechanical design, this system mainly contains walking, removal, and storage parts. It comprises robotic arms with a fixed end and sweep-in devices for sweeping dead chickens, a conveyor belt for transporting chickens, a storage cache for storing chickens, and a tracked vehicle. The designed system has dimensions of approximately 1.038 × 0.36 × 0.5 m3, and two dead chickens can be removed in a single operation. The walking speed of the chicken removal system is 3.3 cm/s. In order to enhance the automation and artificial intelligence in the poultry industry, the identification system was used in a novel small removal system. The conditions of the chickens in a poultry house can be monitored remotely by using a camera, and dead chickens can be identified through deep learning based on the YOLO v4 algorithm. The precision of the designed system reached 95.24% in this study, and dead chickens were successfully moved to the storage cache. Finally, the designed system can reduce the contact between humans and poultry to effectively improve the overall biological safety.
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