HAYVANSAL ÜRETİM VE HAYVANSAL GIDA SANAYİNDE YAPAY ZEKÂ: UYGULAMALAR, SINIRLILIKLAR VE GELECEK PERSPEKTİFİ
Özet
Dünya nüfusundaki hızlı artış, iklim değişikliği, doğal kaynakların giderek azalması ve tüketici beklentilerindeki değişim, küresel gıda üretim sistemleri üzerinde ciddi baskılar oluşturmaktadır. Bu durum, özellikle hayvansal üretim sistemlerinde verimlilik, sürdürülebilirlik, hayvan refahı ve gıda güvenliğinin eş zamanlı olarak sağlanmasını zorunlu hâle getirmektedir. Bu bağlamda hayvansal üretim ve hayvansal üretime dayalı gıda sanayi, dijitalleşme ve veri temelli üretim modellerine yönelen önemli bir dönüşüm süreci içerisine girmiştir. Bu dönüşümün merkezinde yer alan yapay zekâ (YZ) teknolojileri; hayvan davranışlarının gerçek zamanlı olarak izlenmesi ve analiz edilmesi, hastalıkların erken teşhisi, yem tüketimi ve besleme stratejilerinin yönetilmesi, üreme süreçlerinin takip edilmesi, süt ve et verimi gibi üretim parametrelerinin tahmin edilmesi, çevresel stres faktörlerinin değerlendirilmesi, hayvan refahının izlenmesi, üretim süreçlerinin dijital olarak takip edilmesi, hayvansal gıda üretim süreçlerinin yönetimi ve hayvansal ürünlerin kalite kontrolü gibi birçok alanda önemli uygulama olanakları sunmaktadır. Bu özellikleri sayesinde YZ, günümüzde hassas hayvancılık sistemlerinin (Precision Livestock Farming) en önemli ve stratejik bileşenlerinden biri olarak kabul edilmektedir. Makine öğrenmesi, derin öğrenme, yapay sinir ağları ve sensör tabanlı veri analizine dayanan YZ uygulamaları; hayvansal üretimde verimliliğin artırılması, hayvan sağlığı ve refahının izlenmesi, rasyon yönetimi, üretim planlaması ve öngörüsel karar destek mekanizmalarının geliştirilmesi gibi alanlarda önemli olanaklar sunmaktadır. Ayrıca bireysel hayvan davranışlarının sürekli izlenmesi sayesinde sağlık, beslenme ve üreme ile ilişkili problemlerin erken dönemde tespit ve performans parametrelerinin eş zamanlı olarak takip edilmesi mümkün hale gelmiştir. Hayvansal gıda üretiminde ise YZ tabanlı sistemler; kalite sınıflandırması, ürün özelliklerinin hızlı değerlendirilmesi, raf ömrünün tahmin edilmesi, kontaminasyon risklerinin belirlenmesi ve tedarik zinciri boyunca izlenebilirliğin artırılması gibi uygulamalar aracılığıyla gıda güvenliği ve kalite güvencesinin geliştirilmesine katkı sağlamaktadır. Literatür bulguları; makine öğrenmesi, derin öğrenme, sensör teknolojileri ve büyük veri analitiğine dayanan uygulamaların birim hayvan başına elde edilen verimi artırdığını, işletme ekonomisini güçlendirdiğini, hayvan refahını iyileştirdiğini ve gıda güvenliğinin güçlendirilmesine önemli katkılar sağladığını ortaya koymaktadır. Bununla birlikte mevcut çalışmaların önemli bir bölümü kontrollü koşullarda yürütülmüş olup; model genellenebilirliği, veri standardizasyonu, sensör güvenilirliği, ekonomik uygulanabilirlik ve etik boyutlar önemli sınırlılıklar arasında yer almaktadır. Gelecekte multimodal veri entegrasyonu, açıklanabilir yapay zekâ, dijital ikiz teknolojileri ve blok zincir tabanlı izlenebilirlik sistemlerinin daha yaygın hale gelmesi beklenmektedir. Ancak YZ’nin hayvansal üretim ve gıda sistemlerinde sürdürülebilir şekilde kullanılabilmesi; düşük maliyetli, ölçeklenebilir ve farklı üretim koşullarına uyum sağlayabilen sistemlerin geliştirilmesine bağlıdır.
Anahtar Kelimeler: Yapay zekâ, hayvansal üretim, süt teknolojisi, et endüstrisi, gıda güvenliği, makine öğrenmesi
Konu Alanı: Tarım Bilimleri -> Tarım (diğer) -> Ziraat, Orman ve Su Ürünleri
Van Dijk, M., Morley, T., Rau, M. L., & Saghai, Y. (2021). A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nature Food, 2(7), 494–501. https://doi.org/10.1038/s43016-021-00322-9
FAO. (2022). The future of food and agriculture – Drivers and triggers for transformation. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cc0959en
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming: A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
García, R., Aguilar, J., Toro, M., Pinto, A., & Rodríguez, J. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture, 179, 105826. https://doi.org/10.1016/j.compag.2020.105826
Mahmud, M. S., Zahid, A., Das, A. K., Muzammil, M., & Khan, M. U. (2021). A systematic literature review on deep learning applications for precision cattle farming. Computers and Electronics in Agriculture, 187, 106313. https://doi.org/10.1016/j.compag.2021.106313
Kang, Z., Zhao, Y., Chen, L., Guo, Y., Mu, Q., & Wang, S. (2022). Advances in machine learning and hyperspectral imaging in the food supply chain. Food Engineering Reviews, 14, 596–616. https://doi.org/10.1007/s12393-022-09322-2
Liakos, K. G., Athanasiadis, V., Bozinou, E., & Lalas, S. I. (2025). Machine learning for quality control in the food industry: A review. Foods, 14(19), 3424. https://doi.org/10.3390/foods14193424
Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic Markets, 31(3), 685–695. https://doi.org/10.1007/s12525-021-00475-2
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539
Computer Trends. (2024). SZTE mezőgazdasági kar: A teheneknek nincs magánéletük. https://www.computertrends.hu/technologia/szte-mezogazdasagi-kar-a-teheneknek-nincs-maganeletuk-382061.html
Duckett, T., Pearson, S., Blackmore, S., Grieve, B., Chen, W. H., Cielniak, G., et al. (2018). Agricultural robotics: The future of robotic agriculture. arXiv. https://doi.org/10.48550/arXiv.1806.06762
Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014
Distante, D., Ali, M. M., Chessa, S., Giannetto, C., & Tolone, M. (2025). Artificial intelligence applied to precision livestock farming. Smart Agricultural Technology, 11, 100889. https://doi.org/10.1016/j.atech.2025.100889
Morrone, S., Dimauro, C., Gambella, F., & Cappai, M. G. (2022). Industry 4.0 and Precision Livestock Farming (PLF): An up-to-date overview across animal productions. Sensors, 22(12), 4319. https://doi.org/10.3390/s22124319
Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367. https://doi.org/10.1016/j.sbsr.2020.100367
Rutten, C. J., Velthuis, A. G. J., Steeneveld, W., & Hogeveen, H. (2013). Sensors to support health management on dairy farms. Journal of Dairy Science, 96(4), 1928–1952. https://doi.org/10.3168/jds.2012-6107
Kumar, S., Tiwari, S., & Singh, S. K. (2018). Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement, 116, 1–17. https://doi.org/10.1016/j.measurement.2017.10.064
Andrew, W., Greatwood, C., & Burghardt, T. (2021). Visual identification of individual Holstein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture, 185, 106133. https://doi.org/10.1016/j.compag.2021.106133
Nasirahmadi, A., Edwards, S. A., & Sturm, B. (2017). Implementation of machine vision for detecting behaviour of cattle and pigs. Livestock Science, 202, 25–38. https://doi.org/10.1016/j.livsci.2017.05.014
Achour, B., Belkadi, M., Filali, I., Laghrouche, M., & Lahdir, M. (2020). Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on convolutional neural networks (CNN). Biosystems Engineering, 198, 31–49. https://doi.org/10.1016/j.biosystemseng.2020.07.019
Borchers, M. R., Chang, Y. M., Proudfoot, K. L., Wadsworth, B. A., Stone, A. E., & Bewley, J. M. (2017). Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. Journal of Dairy Science, 100(7), 5664–5674. https://doi.org/10.3168/jds.2016-11526
Benaissa, S., Tuyttens, F. A. M., Plets, D., De Pessemier, T., Trogh, J., Tanghe, E., et al. (2019). On the use of on-cow accelerometers for the classification of behaviours in dairy barns. Research in Veterinary Science, 125, 425–433. https://doi.org/10.1016/j.rvsc.2017.10.005
Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6–11. https://doi.org/10.2527/af.2017.0102
Li, G., Zhao, Y., Purswell, J. L., Du, Q., Chesser, G. D., Jr., & Lowe, J. W. (2020). Analysis of feeding and drinking behaviors of group-reared broilers via image processing. Computers and Electronics in Agriculture, 175, 105596. https://doi.org/10.1016/j.compag.2020.105596
Xu, B., Wang, W., Falzon, G., Kwan, P., Guo, L., Gao, Y., et al. (2020). Livestock classification and counting in quadcopter aerial images using Mask R-CNN. International Journal of Remote Sensing, 41(21), 8121–8142. https://doi.org/10.1080/01431161.2020.1754491
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427–437. https://doi.org/10.1016/j.ipm.2009.03.002
Kamphuis, C., Mollenhorst, H., Heesterbeek, J. A. P., & Hogeveen, H. (2010). Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction. Journal of Dairy Science, 96(3), 1620–1631. https://doi.org/10.3168/jds.2010-3228
Tatai, A. (2026, May 21). The impact of digitalization and artificial intelligence on the efficiency of livestock production sectors. In 23rd Wellmann International Scientific Conference, Book of Abstracts. University of Szeged Faculty of Agriculture.
Haile-Mariam, M., & Pryce, J. E. (2015). Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian dairy cattle. Journal of Dairy Science, 98(10), 7364–7379. https://doi.org/10.3168/jds.2015-9537
Viazzi, S., Bahr, C., Schlageter-Tello, A., Van Hertem, T., Romanini, C. E. B., Halachmi, I., Lokhorst, C., & Berckmans, D. (2013). Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle. Journal of Dairy Science, 96(1), 257–266. https://doi.org/10.3168/jds.2012-5806
Jukan, A., Masip-Bruin, X., & Amla, N. (2017). Smart computing and sensing technologies for animal welfare: A systematic review. ACM Computing Surveys, 50(5), 1–27. https://doi.org/10.1145/3075930
Oliveira, D. A. B., Pereira, L. G. R., Bresolin, T., Ferreira, R. E. P., & Dorea, J. R. R. (2021). A review of deep learning algorithms for computer vision systems in livestock. Livestock Science, 253, 104700. https://doi.org/10.1016/j.livsci.2021.104700
Machuve, D., Nwankwo, E., Mduma, N., & Mbelwa, J. (2022). Poultry diseases diagnostics models using deep learning. Frontiers in Artificial Intelligence, 5, 733345. https://doi.org/10.3389/frai.2022.733345
Schlageter-Tello, A., Bokkers, E. A. M., Groot Koerkamp, P. W. G., Van Hertem, T., Viazzi, S., Romanini, C. E. B., et al. (2014). Manual and automatic locomotion scoring systems in dairy cows: A review. Preventive Veterinary Medicine, 116(1–2), 12–25. https://doi.org/10.1016/j.prevetmed.2014.06.006
Zgank, A. (2021). IoT-based bee swarm activity acoustic classification using deep neural networks. Sensors, 21(6), 2033. https://doi.org/10.3390/s21030676
Tullo, E., Finzi, A., & Guarino, M. (2019). Environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy. Science of the Total Environment, 650, 2751–2760. https://doi.org/10.1016/j.scitotenv.2018.10.018
Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4), 1540–1550. https://doi.org/10.1093/jas/sky014
Angeles-Hernandez, J. C., Pollott, G. E., Albarran-Portillo, B., Ramirez-Valverde, R., & Castillo-Maldonado, P. P. (2022). Prediction of milk yield from udder morphology in dairy sheep using artificial neural networks. Scientific Reports, 12, 8967. https://doi.org/10.1038/s41598-022-12868-0
Kutyauripo, I., Rushambwa, M., & Chiwazi, L. (2023). Artificial intelligence applications in the agrifood sectors. Journal of Agriculture and Food Research, 11, 100502. https://doi.org/10.1016/j.jafr.2023.100502
Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of artificial intelligence in agriculture sector. Advanced Agrochem, 2(1), 15–30. https://doi.org/10.1016/j.aac.2022.10.001
Bovo, M., Agrusti, M., Ozella, L., Forte, C., Torreggiani, D., & Tassinari, P. (2025). A viable data-driven method for the assessment of the productivity level of dairy cows in future lactations. Computers and Electronics in Agriculture, 230, 109860. https://doi.org/10.1016/j.compag.2024.109860
Ramírez-Morales, I., Rivero-Cebrián, D., Fernández-Blanco, E., & Pazos-Sierra, A. (2016). Early warning in egg production curves from commercial hens: A SVM approach. Computers and Electronics in Agriculture, 161, 219–227. https://doi.org/10.1016/j.compag.2015.12.009
Saar, M., Bezen, R., Edan, Y., & Halachmi, I. (2022). A machine vision system to predict individual cow feed intake using RGB-D camera and deep learning models. Animal, 16(2), 100443. https://doi.org/10.1016/j.animal.2021.100432
Zuidhof, M. J. (2020). Precision livestock feeding: Matching nutrient supply with nutrient requirements of individual animals. Journal of Applied Poultry Research, 29(1), 11–14. https://doi.org/10.1016/j.japr.2019.12.009
Akintan, O., Gebremedhin, K. G., & Uyeh, D. D. (2024). Animal feed formulation—Connecting technologies to build a resilient and sustainable system. Animals, 14(10), 1497. https://doi.org/10.3390/ani14101497
Salleh, S. M., Danielsson, R., & Kronqvist, C. (2023). Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle. Journal of Dairy Research, 90(1), 5–8. https://doi.org/10.1017/S0022029923000171
Chhetri, K. B. (2024). Applications of artificial intelligence and machine learning in food quality control and safety assessment. Food Engineering Reviews, 16, 1–21. https://doi.org/10.1007/s12393-023-09363-1
Devi, P., Subburamu, K., Giridhari, V. A., Dananjeyan, B., & Maruthamuthu, T. (2025). Integration of AI-based tools in dairy quality control: Enhancing pathogen detection efficiency. Journal of Food Measurement and Characterization, 19(7), 4427–4438. https://doi.org/10.1007/s11694-025-03269-8
Bereczki-Tisza, J., & Mikó, E. (2026). AI-based visual monitoring of dairy cattle: Individual tracking and early detection of lameness through movement analysis. In 23rd Wellmann International Scientific Conference, Book of Abstracts. University of Szeged Faculty of Agriculture.
Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033
Zhao, X., Lin, C.-W., Wang, J., & Oh, D. H. (2014). Advances in rapid detection methods for foodborne pathogens. Journal of Microbiology and Biotechnology, 24(3), 297–312. https://doi.org/10.4014/jmb.1310.10013
Law, J. W.-F., Ab Mutalib, N.-S., Chan, K.-G., & Lee, L.-H. (2015). Rapid methods for the detection of foodborne bacterial pathogens: Principles, applications, advantages and limitations. Frontiers in Microbiology, 5, 770. https://doi.org/10.3389/fmicb.2014.00770
Gao, R., Liu, X., Xiong, Z., Wang, G., & Ai, L. (2024). Research progress on detection of foodborne pathogens: The more rapid and accurate answer to food safety. Food Research International, 193, 114767. https://doi.org/10.1016/j.foodres.2024.114767
Banicod, R. J. S., Tabassum, N., Jo, D.-M., et al. (2025). Integration of artificial intelligence in biosensors for enhanced detection of foodborne pathogens. Biosensors, 15(6), 690. https://doi.org/10.3390/bios15060690
Jiang, W., Liu, C., Liu, W., & Zheng, L. (2025). Advancements in intelligent sensing technologies for food safety detection. Research, 8, 0713. https://doi.org/10.34133/research.0713
Wang, Y., & Salazar, J. K. (2016). Culture-independent rapid detection methods for bacterial pathogens and toxins in food matrices. Comprehensive Reviews in Food Science and Food Safety, 15, 183–205. https://doi.org/10.1111/1541-4337.12175
Rejeb, A., Rejeb, K., Keogh, J. G., & Treiblmaier, H. (2022). Blockchain technology in the food industry: A review of potentials, challenges and future research directions. Logistics, 6, 27. https://doi.org/10.3390/logistics4040027
Pérez Núñez, I., Quiñones, J., Sepúlveda Truan, G., Cancino-Baier, D., Agregán, R., Lorenzo, J. M., et al. (2026). Strategies for advanced production: A review of the use of AI in the dairy industry. Animals, 16(9), 1363. https://doi.org/10.3390/ani16091363
Serrano-Torres, G. J., López-Naranjo, A. L., Larrea-Cuadrado, P. L., & Mazón-Fierro, G. (2025). Transformation of the dairy supply chain through artificial intelligence: A systematic review. Sustainability, 17, 982. https://doi.org/10.3390/su17030982
Taner, O., & Çolak, A. B. (2024). Dairy factory milk product processing and sustainable shelf-life extension with artificial intelligence: A model study. Frontiers in Sustainable Food Systems, 8, 1344370. https://doi.org/10.3389/fsufs.2024.1344370
Shi, Y., Wang, X., Borhan, M. S., Young, J., Newman, D., Berg, E., & Sun, X. (2021). A review on meat quality evaluation methods based on non-destructive computer vision and artificial intelligence technologies. Food Science of Animal Resources, 41(4), 563–588. https://doi.org/10.5851/kosfa.2021.e25
Driessen, C., & Heutinck, L. F. M. (2015). Cows desiring to be milked? Milking robots and the co-evolution of ethics and technology on Dutch dairy farms. Agriculture and Human Values, 32(1), 3–20. https://doi.org/10.1007/s10460-014-9515-5
Sanchez, C. N., Orvananos-Guerrero, M. T., Domínguez-Soberanes, J., & Alvarez-Cisneros, Y. M. (2023). Analysis of beef quality according to color changes using computer vision and white-box machine learning techniques. Heliyon, 9, e17976. https://doi.org/10.1016/j.heliyon.2023.e17976
Barbin, D. F., ElMasry, G., Sun, D. W., & Allen, P. (2013). Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry, 138(2–3), 1162–1171. https://doi.org/10.1016/j.foodchem.2012.11.120
Alvarez-García, W. Y., Mendoza, L., Muñoz-Vílchez, Y., Nuñez-Melgar, D. C., & Quilcate, C. (2024). Implementing artificial intelligence to measure meat quality parameters in local market traceability processes. International Journal of Food Science & Technology, 59(11), 8058–8068. https://doi.org/10.1111/ijfs.17546
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F. J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–113). Springer. https://doi.org/10.1007/978-3-319-38756-7
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415. https://doi.org/10.1109/TII.2018.2873186
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358
Wang, S., Wan, J., Li, D., & Zhang, C. (2015). Implementing smart factory of Industrie 4.0: An outlook. International Journal of Distributed Sensor Networks, 14(1), 1–10. https://doi.org/10.1155/2016/3159805
Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806
Samek, W., Wiegand, T., & Müller, K. R. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. ITU Journal: ICT Discoveries, Special Issue, 1(1), 1–10.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Monteiro, A., & Gökdal, Ö. (2026). Artificial intelligence applications for sustainable livestock production and environmental management: A review. Sustainable Production and Consumption, 45, 112–126.
Rotz, S., Duncan, E., Small, M., Botschner, J., Dara, R., Mosby, I., Reed, M., & Fraser, E. D. G. (2019). The politics of digital agricultural technologies: A preliminary review. Sociologia Ruralis, 59(2), 203–229. https://doi.org/10.1111/soru.12233
ATAY, O., Özuğur, A. K., Gökdal, Ö., Sarı, A., & Eren, V. (2026). HAYVANSAL ÜRETİM VE HAYVANSAL GIDA SANAYİNDE YAPAY ZEKÂ: UYGULAMALAR, SINIRLILIKLAR VE GELECEK PERSPEKTİFİ. In KÖPRÜCÜ, K. & KÖPRÜCÜ, S. (Eds.), Ekosistem Temelli Üretim: Ziraat, Ormancılık ve Su Ürünleri | Ecosystem-Based Production: Agriculture, Forestry and Fisheries (pp. 31-62). Vizetek Yayıncılık.
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