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HAYVAN BARINAKLARINDA ÇEVRESEL KONTROL SİSTEMLERİ İÇİN AKILLI TARIM UYGULAMALARI

Year 2024, Volume: 12 Issue: 2, 108 - 121, 27.12.2024

Abstract

Akıllı tarım uygulamaları, çeşitli çevresel parametreler üzerinde hassas kontrol sağlayarak geleneksel tarım uygulamalarını hızla dönüştürmektedir. Hayvancılıkta, üretkenlik ve refah büyük ölçüde çevreye bağlı olduğundan, bu araçlar sıcaklık, nem, hava kalitesi ve genel barınma koşullarını optimize etmek için umut verici çözümler sunmaktadır. Bu makale, hayvan barınaklarında çevresel kontrol sistemleri için akıllı tarım sistemlerinin uygulamalarını, IoT tabanlı sensörler, otomatik havalandırma sistemleri, akıllı besleme mekanizmaları ve veri odaklı karar destek platformları gibi teknolojiler üzerinden incelemektedir. Bu ilerlemeler, hayvan sağlığını iyileştirerek, üretkenliği artırır, kaynak tüketimini azaltır ve çevresel etkiyi düşürür. Tarımın geleceği akıllı tarım sistemleridir.

References

  • Ali, S. (2022) Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems, Journal of Engineering Research: Vol. 6: Iss. 3, Article 5.
  • Alyahyan, E., Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17 (1), 1-21. https://doi.org/10.1186/s41239-020-0177-7
  • Arpaci, I. (2020). What drives students' online self-disclosure behaviour on social media? A hybrid SEM and artificial intelligence approach. International Journal of Mobile Communications, 18 (2), 229-241. https://doi.org/10.1504/IJMC.2020.105847
  • Attia, Y.A., Aldhalmi, A.K., Youssef, I.M., (2024). Climate change and its effects on poultry industry and sustainability. Discovery Sustain 5, 397. https://doi.org/10.1007/s43621-024-00627-2
  • Bao, J., Xie, Q. (2022). Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production, 331, 129956. https://doi.org/10.1016/j.jclepro.2021.129956
  • Bhardwaj R., A.R. Nambiar, D. Dutta., (2017). A study of machine learning in healthcare. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 236-241, July 2017. https://doi.org/10.1109/COMPSAC.2017.164
  • Gil, P.D., Susana, D.C.M., Moro, S., Costa, J.M., (2021). A data-driven approach to predict first-year students' academic success in higher education institutions. Education and Information Technologies, 26 (2), 2165-2190 https://doi.org/10.1007/s10639-020-10346-6
  • Halachmi, I., Guarino, M., Bewley, J., Pastell, M. (2019). Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual review of animal biosciences, 7(1), 403-425. https://doi.org/10.1146/annurev-animal-020518-114851
  • Kumar, V., Sharma, K.V., Kedam, N., Patel, A., Kate, T.R., Rathnayake, U., (2024). A Comprehensive Review on Smart and Sustainable Agriculture Using IoT Technologies. Smart Agricultural Technology, 100487. https://doi.org/10.1016/j.atech.2024.100487
  • Lee, M., Kim, H., Hwang, H.J., Yoe, H. (2020). IoT Based Management System for Livestock Farming. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_33
  • Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S.J.H., Ogata, H., Baltes, J., Guerra, R., Li, P., Tsai, C.C., (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 1 (11), 2748. https://doi.org/10.3389/fpsyg.2020.580820
  • Markov N., Svetoslava S., Hristov M., Kaneva T., (2022) Smart Dairy Farm - Digitalization. 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE). https://doi.org/10.1109/EEAE53789.2022.9831220
  • Mohsen S., Elkaseer, A., Scholz,S. G., (2021). Industry 4.0-oriented deep learning models for human activity recognition. IEEE Access, vol. 9,pp, 150508-150521, November. https://doi.org/10.1109/ACCESS.2021.3125733
  • Monteiro, A., Santos, S., Gonçalves, P. (2021). Precision agriculture for crop and livestock farming-Brief review. Animals. mdpi.com https://doi.org/10.3390/ani11082345
  • Morgado, J.N., Lamonaca, E., Santeramo, F.G., Caroprese, M., Albenzio, M., Ciliberti, M.G. (2023). Effects of management strategies on animal welfare and productivity under heat stress: A synthesis. Frontiers in Veterinary Science, 10, 1145610. https://doi.org/10.3389/fvets.2023.1145610
  • Nandanwar, H., Chauhan, A., Pahl, D., Meena, H., (2020). A survey of application of ML and data mining techniques for smart irrigation system, In 2020 Second International Conference on Inventive Research in Computing Applications, pp. 205-212, July 2020. https://doi.org/10.1109/ICIRCA48905.2020.9183088
  • Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Muzirafuti, A. (2022). IoT-enabled smart agriculture: architecture, applications, and challenges. Applied Sciences, 12(7), 3396. mdpi.com https://doi.org/10.3390/app12073396
  • Schukat, S. Heise, H. (2021). Smart Products in Livestock Farming-An Empirical Study on the Attitudes of German Farmers. Animals. mdpi.com https://doi.org/10.3390/ani11041055
  • Smith, N., (2024). Environmental Sustainability in Livestock Production. International Journal of Livestock Policy, 2(1), 26–38. https://doi.org/10.47941/ijlp.1701
  • Symeonaki, E., Arvanitis, K.G., Piromalis, D., Tseles, D., Balafoutis, A.T. (2022). Ontology-based IoT middleware approach for smart livestock farming toward agriculture 4.0: A case study for controlling thermal environment in a pig facility. Agronomy, 12(3), 750. mdpi.com https://doi.org/10.3390/agronomy12030750
  • Tanveer, M., Hassan, S., Bhaumik, A. (2020). Academic Policy Regarding Sustainability and Artificial Intelligence (AI). Sustainability, 12(22), 9435. https://doi.org/10.3390/su12229435
  • Teng, Y., Zhang, J., Sun, T. (2023). Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems, 40(4), e12820. https://doi.org/10.1111/exsy.12820
  • Yu D., Deng, L., (2014). Automatic speech recognition: A deep learning approach. Springer Publishing Company, Incorporated. https://doi.org/10.1007/978-1-4471-5779-3

APPLICATIONS OF SMART AGRICULTURE TOOLS FOR ENVIRONMENTAL CONTROL IN ANIMAL HOUSES

Year 2024, Volume: 12 Issue: 2, 108 - 121, 27.12.2024

Abstract

Smart agriculture tools are rapidly transforming traditional farming practices by enabling precise control over various environmental parameters. In animal husbandry, where productivity and animal welfare depend heavily on the environment, such tools offer promising solutions to optimize temperature, humidity, air quality, and overall housing conditions. This article explores the applications of smart agriculture tools in the environmental control of animal houses, focusing on technologies such as IoT-based sensors, automated ventilation systems, smart feeding mechanisms, and data-driven decision-making platforms. These advancements are improving animal health, enhancing productivity, reducing resource consumption, and lowering environmental impact.

References

  • Ali, S. (2022) Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems, Journal of Engineering Research: Vol. 6: Iss. 3, Article 5.
  • Alyahyan, E., Düştegör, D. (2020). Predicting academic success in higher education: Literature review and best practices. International Journal of Educational Technology in Higher Education, 17 (1), 1-21. https://doi.org/10.1186/s41239-020-0177-7
  • Arpaci, I. (2020). What drives students' online self-disclosure behaviour on social media? A hybrid SEM and artificial intelligence approach. International Journal of Mobile Communications, 18 (2), 229-241. https://doi.org/10.1504/IJMC.2020.105847
  • Attia, Y.A., Aldhalmi, A.K., Youssef, I.M., (2024). Climate change and its effects on poultry industry and sustainability. Discovery Sustain 5, 397. https://doi.org/10.1007/s43621-024-00627-2
  • Bao, J., Xie, Q. (2022). Artificial intelligence in animal farming: A systematic literature review. Journal of Cleaner Production, 331, 129956. https://doi.org/10.1016/j.jclepro.2021.129956
  • Bhardwaj R., A.R. Nambiar, D. Dutta., (2017). A study of machine learning in healthcare. In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 236-241, July 2017. https://doi.org/10.1109/COMPSAC.2017.164
  • Gil, P.D., Susana, D.C.M., Moro, S., Costa, J.M., (2021). A data-driven approach to predict first-year students' academic success in higher education institutions. Education and Information Technologies, 26 (2), 2165-2190 https://doi.org/10.1007/s10639-020-10346-6
  • Halachmi, I., Guarino, M., Bewley, J., Pastell, M. (2019). Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual review of animal biosciences, 7(1), 403-425. https://doi.org/10.1146/annurev-animal-020518-114851
  • Kumar, V., Sharma, K.V., Kedam, N., Patel, A., Kate, T.R., Rathnayake, U., (2024). A Comprehensive Review on Smart and Sustainable Agriculture Using IoT Technologies. Smart Agricultural Technology, 100487. https://doi.org/10.1016/j.atech.2024.100487
  • Lee, M., Kim, H., Hwang, H.J., Yoe, H. (2020). IoT Based Management System for Livestock Farming. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_33
  • Luan, H., Geczy, P., Lai, H., Gobert, J., Yang, S.J.H., Ogata, H., Baltes, J., Guerra, R., Li, P., Tsai, C.C., (2020). Challenges and future directions of big data and artificial intelligence in education. Frontiers in Psychology, 1 (11), 2748. https://doi.org/10.3389/fpsyg.2020.580820
  • Markov N., Svetoslava S., Hristov M., Kaneva T., (2022) Smart Dairy Farm - Digitalization. 8th International Conference on Energy Efficiency and Agricultural Engineering (EE&AE). https://doi.org/10.1109/EEAE53789.2022.9831220
  • Mohsen S., Elkaseer, A., Scholz,S. G., (2021). Industry 4.0-oriented deep learning models for human activity recognition. IEEE Access, vol. 9,pp, 150508-150521, November. https://doi.org/10.1109/ACCESS.2021.3125733
  • Monteiro, A., Santos, S., Gonçalves, P. (2021). Precision agriculture for crop and livestock farming-Brief review. Animals. mdpi.com https://doi.org/10.3390/ani11082345
  • Morgado, J.N., Lamonaca, E., Santeramo, F.G., Caroprese, M., Albenzio, M., Ciliberti, M.G. (2023). Effects of management strategies on animal welfare and productivity under heat stress: A synthesis. Frontiers in Veterinary Science, 10, 1145610. https://doi.org/10.3389/fvets.2023.1145610
  • Nandanwar, H., Chauhan, A., Pahl, D., Meena, H., (2020). A survey of application of ML and data mining techniques for smart irrigation system, In 2020 Second International Conference on Inventive Research in Computing Applications, pp. 205-212, July 2020. https://doi.org/10.1109/ICIRCA48905.2020.9183088
  • Quy, V.K., Hau, N.V., Anh, D.V., Quy, N.M., Ban, N.T., Lanza, S., Muzirafuti, A. (2022). IoT-enabled smart agriculture: architecture, applications, and challenges. Applied Sciences, 12(7), 3396. mdpi.com https://doi.org/10.3390/app12073396
  • Schukat, S. Heise, H. (2021). Smart Products in Livestock Farming-An Empirical Study on the Attitudes of German Farmers. Animals. mdpi.com https://doi.org/10.3390/ani11041055
  • Smith, N., (2024). Environmental Sustainability in Livestock Production. International Journal of Livestock Policy, 2(1), 26–38. https://doi.org/10.47941/ijlp.1701
  • Symeonaki, E., Arvanitis, K.G., Piromalis, D., Tseles, D., Balafoutis, A.T. (2022). Ontology-based IoT middleware approach for smart livestock farming toward agriculture 4.0: A case study for controlling thermal environment in a pig facility. Agronomy, 12(3), 750. mdpi.com https://doi.org/10.3390/agronomy12030750
  • Tanveer, M., Hassan, S., Bhaumik, A. (2020). Academic Policy Regarding Sustainability and Artificial Intelligence (AI). Sustainability, 12(22), 9435. https://doi.org/10.3390/su12229435
  • Teng, Y., Zhang, J., Sun, T. (2023). Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems, 40(4), e12820. https://doi.org/10.1111/exsy.12820
  • Yu D., Deng, L., (2014). Automatic speech recognition: A deep learning approach. Springer Publishing Company, Incorporated. https://doi.org/10.1007/978-1-4471-5779-3
There are 23 citations in total.

Details

Primary Language English
Subjects Agricultural Machines, Agricultural Energy Systems, Agricultural Automatization
Journal Section Review
Authors

Gürkan A. K. Gürdil 0000-0001-7764-3977

Fuat Lüle 0000-0002-9332-0761

Melih Cevdet Pehlivanlı 0009-0000-8765-3060

Mustafa Çetin 0009-0001-0245-0465

Publication Date December 27, 2024
Submission Date November 15, 2024
Acceptance Date December 14, 2024
Published in Issue Year 2024 Volume: 12 Issue: 2

Cite

APA Gürdil, G. A. K., Lüle, F., Pehlivanlı, M. C., Çetin, M. (2024). APPLICATIONS OF SMART AGRICULTURE TOOLS FOR ENVIRONMENTAL CONTROL IN ANIMAL HOUSES. ADYUTAYAM Dergisi, 12(2), 108-121.