{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:33:12Z","timestamp":1782984792481,"version":"3.54.5"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T00:00:00Z","timestamp":1732924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007061","name":"National Secretariat for Science, Technology, and Innovation (SENACYT), Panama","doi-asserted-by":"publisher","award":["IDDS22-28"],"award-info":[{"award-number":["IDDS22-28"]}],"id":[{"id":"10.13039\/501100007061","id-type":"DOI","asserted-by":"publisher"}]},{"name":"SENACYT\u2019s National Research System (SNI)","award":["IDDS22-28"],"award-info":[{"award-number":["IDDS22-28"]}]},{"name":"SENACYT\u2019s Program for the Strengthening of National Graduate Programs for a Master\u2019s Degree in Mechanical Engineering Sciences","award":["IDDS22-28"],"award-info":[{"award-number":["IDDS22-28"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agriculture"],"abstract":"<jats:p>The exponential growth of global poultry production highlights the critical need for efficient flock management, particularly in accurately counting chickens to optimize operations and minimize economic losses. This study advances the application of artificial intelligence (AI) in agriculture by developing and validating an AI-driven automated poultry flock management system using the YOLOv8 object detection model. The scientific objective was to address challenges such as occlusions, lighting variability, and high-density flock conditions, thereby contributing to the broader understanding of computer vision applications in agricultural environments. The practical objective was to create a scalable and reliable system for automated monitoring and decision-making, optimizing resource utilization and improving poultry management efficiency. The prototype achieved high precision (93.1%) and recall (93.0%), demonstrating its reliability across diverse conditions. Comparative analysis with prior models, including YOLOv5, highlights YOLOv8\u2019s superior accuracy and robustness, underscoring its potential for real-world applications. This research successfully achieves its objectives by delivering a system that enhances poultry management practices and lays a strong foundation for future innovations in agricultural automation.<\/jats:p>","DOI":"10.3390\/agriculture14122187","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T09:00:10Z","timestamp":1733130010000},"page":"2187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["AI-Based Monitoring for Enhanced Poultry Flock Management"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7988-3293","authenticated-orcid":false,"given":"Edmanuel","family":"Cruz","sequence":"first","affiliation":[{"name":"Centro Regional Veraguas, Universidad Tecnol\u00f3gica de Panam\u00e1, Atalaya 0901, Panama"},{"name":"Sistema Nacional de Investigaci\u00f3n (SNI), SENACYT, Panama City 0816-02852, Panama"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4976-6612","authenticated-orcid":false,"given":"Miguel","family":"Hidalgo-Rodriguez","sequence":"additional","affiliation":[{"name":"Centro Regional Veraguas, Universidad Tecnol\u00f3gica de Panam\u00e1, Atalaya 0901, Panama"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6079-9668","authenticated-orcid":false,"given":"Adiz Mariel","family":"Acosta-Reyes","sequence":"additional","affiliation":[{"name":"Centro Regional Veraguas, Universidad Tecnol\u00f3gica de Panam\u00e1, Atalaya 0901, Panama"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1456-2253","authenticated-orcid":false,"given":"Jos\u00e9 Carlos","family":"Rangel","sequence":"additional","affiliation":[{"name":"Sistema Nacional de Investigaci\u00f3n (SNI), SENACYT, Panama City 0816-02852, Panama"},{"name":"Facultad de Ingenier\u00eda de Sistemas Computacionales, Universidad Tecnol\u00f3gica de Panam\u00e1, Panama City 0819-07289, Panama"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3008-3025","authenticated-orcid":false,"given":"Keyla","family":"Boniche","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Mec\u00e1nica, Universidad Tecnol\u00f3gica de Panam\u00e1, Panama City 0819-07289, Panama"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1093\/advances\/nmac074","article-title":"Poultry consumption and human health: How much is really known? A systematically searched scoping review and research perspective","volume":"13","author":"Connolly","year":"2022","journal-title":"Adv. Nutr."},{"key":"ref_2","unstructured":"Day, J. (2023). Global Chicken Market Report 2023: Rising Consumption of Poultry Worldwide to Boost Growth, Poultry Producer."},{"key":"ref_3","unstructured":"Shahbandeh, M. (2024). Global Production of Meat 2016\u20132024, Statista."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Taleb, H.M., Mahrose, K., Abdel-Halim, A.A., Kasem, H., Ramadan, G.S., Fouad, A.M., Khafaga, A.F., Khalifa, N.E., Kamal, M., and Salem, H.M. (2024). Using artificial intelligence to improve poultry productivity\u2014A review. Ann. Anim. Sci.","DOI":"10.2478\/aoas-2024-0039"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khanal, R., Choi, Y., and Lee, J. (2024). Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments. Sensors, 24.","DOI":"10.3390\/s24102977"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Neethirajan, S. (2022). Automated tracking systems for the assessment of farmed poultry. Animals, 12.","DOI":"10.3390\/ani12030232"},{"key":"ref_7","unstructured":"Shukla, P.P., Bhattachayya, A., and Sharma, A. (2024). Artificial Intelligence in Poultry Production, SR Publications."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5455\/JRAFS.20240404014009","article-title":"Precision Agriculture using Artificial Intelligence and Robotics","volume":"1","author":"Eissa","year":"2024","journal-title":"J. Res. Agric. Food Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, S.O., Peres, R.S., Ramalho, J.C., Lidon, F., and Barata, J. (2023). Machine Learning Applications in Agriculture: Current Trends, Challenges, and Future Perspectives. Agronomy, 13.","DOI":"10.3390\/agronomy13122976"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s12524-020-01265-7","article-title":"Spatial\u2014Spectral image classification with edge preserving method","volume":"49","author":"Merugu","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_11","unstructured":"Sharma, R. (2021, January 6\u20138). Artificial Intelligence in Agriculture: A Review. Proceedings of the International Conference on Intelligent Computing and Control Systems, Madurai, India."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100308","DOI":"10.1016\/j.atech.2023.100308","article-title":"Uncertainty Estimation for Deep Neural Networks to Improve the Assessment of Plumage Conditions of Chickens","volume":"5","author":"Lamping","year":"2023","journal-title":"Smart Agric. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104193","DOI":"10.1016\/j.psj.2024.104193","article-title":"Monitoring Activity Index and Behaviors of Cage-Free Hens with Advanced Deep Learning Technologies","volume":"103","author":"Yang","year":"2024","journal-title":"Poult. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1007\/s00146-021-01377-9","article-title":"The Social and Ethical Impacts of Artificial Intelligence in Agriculture: Mapping the Agricultural AI Literature","volume":"38","author":"Ryan","year":"2023","journal-title":"AI Soc."},{"key":"ref_15","unstructured":"Dalal, N., and Triggs, B. (2005, January 21\u201323). Histograms of Oriented Gradients for Human Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst. (CVIU)"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 21\u201326). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 24\u201327). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_23","unstructured":"Ren, S., He, K., Girshick, R.B., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NIPS), Proceedings of the Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada, 7\u201312 December 2015, Curran Associates, Inc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R.B., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-End Object Detection with Transformers. Computer Vision\u2014ECCV 2020, Proceedings of the 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"149","DOI":"10.3991\/ijim.v17i12.38095","article-title":"Poultry-Edge-AI-IoT System for Real-Time Monitoring and Predicting by Using Artificial Intelligence","volume":"17","author":"Jebari","year":"2023","journal-title":"Int. J. Interact. Mob. Technol."},{"key":"ref_28","first-page":"314","article-title":"Chicken Diseases Detection and Classification Based on Fecal Images Using EfficientNetB7 Model","volume":"11","author":"Vandana","year":"2024","journal-title":"Evergr. Jt. J. Nov. Carbon Resour. Sci. Green Asia Strategy"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fang, C., Wu, Z., Zheng, H., Yang, J., Ma, C., and Zhang, T. (2024). MCP: Multi-Chicken Pose Estimation Based on Transfer Learning. Animals, 14.","DOI":"10.3390\/ani14121774"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Gool, L.V. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the International Conference on Pattern Recognition (ICPR), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft COCO: Common Objects in Context. Computer Vision\u2014ECCV 2014, Proceedings of the 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1016\/j.comnet.2010.05.010","article-title":"The Internet of Things: A survey","volume":"54","author":"Atzori","year":"2010","journal-title":"Comput. Netw."},{"key":"ref_35","unstructured":"Ministerio de la Presidencia (2010). Real Decreto 692\/2010, de 20 de Mayo, Por el que se Establecen las Normas M\u00ednimas Para la Protecci\u00f3n de los Pollos Destinados a la Producci\u00f3n de Carne y se Modifica el Real Decreto 1047\/1994, de 20 de mayo, Relativo a las Normas M\u00ednimas para la Protecci\u00f3n de Terneros, BOE, N\u00fam. 135, Secci\u00f3n I."},{"key":"ref_36","unstructured":"Tzutalin (2023, April 16). LabelImg. Git Code. Available online: https:\/\/github.com\/tzutalin\/labelImg."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kaliappan, V.K., V, M.S., Shanmugasundaram, K., Ravikumar, L., and Hiremath, G.B. (2023, January 24\u201326). Performance Analysis of YOLOv8, RCNN, and SSD Object Detection Models for Precision Poultry Farming Management. Proceedings of the 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), Bhubaneswar, India.","DOI":"10.1109\/AESPC59761.2023.10389906"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, Z., Lei, L., Wang, X., and Guo, X. (2020). Agricultural Greenhouses Detection in High-Resolution Satellite Images Based on Convolutional Neural Networks: Comparison of Faster R-CNN, YOLO v3 and SSD. Sensors, 20.","DOI":"10.3390\/s20174938"},{"key":"ref_39","unstructured":"Kim, J.A., Sung, J.Y., and Park, S.H. (2024, January 5\u20137). Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition. Proceedings of the 2024 IEEE International Conference on Deep Learning for Autonomous Vehicles (DLAV), Vienna, Austria."},{"key":"ref_40","first-page":"84","article-title":"Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments","volume":"13","author":"Sapkota","year":"2024","journal-title":"Artif. Intell. Agric."},{"key":"ref_41","unstructured":"Ultralytics (2024). Train Settings\u2014Ultralytics Documentation, Ultralytics."},{"key":"ref_42","unstructured":"Lempitsky, V., and Zisserman, A. (2010, January 5\u201311). Learning To Count Objects in Images. Proceedings of the European Conference on Computer Vision (ECCV), Crete, Greece."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"13052","DOI":"10.1109\/JSEN.2023.3267101","article-title":"A Real-Time Object Counting and Collecting Device for Industrial Automation Process Using Machine Vision","volume":"23","author":"Kumar","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., and Ma, Y. (2016, January 27\u201330). Single-Image Crowd Counting via Multi-Column Convolutional Neural Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref_45","unstructured":"Grinberg, M. (2018). Flask Web Development: Developing Web Applications with Python, O\u2019Reilly Media, Inc."}],"container-title":["Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0472\/14\/12\/2187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:43:18Z","timestamp":1760114598000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0472\/14\/12\/2187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,30]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["agriculture14122187"],"URL":"https:\/\/doi.org\/10.3390\/agriculture14122187","relation":{},"ISSN":["2077-0472"],"issn-type":[{"value":"2077-0472","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,30]]}}}