SavedAdd your journal

Journals / ADYUTAYAM Dergisi / 2024 / Cilt: 12 Sayı: 2

APPLICATIONS OF SMART AGRICULTURE TOOLS FOR ENVIRONMENTAL CONTROL IN ANIMAL HOUSES

HAYVAN BARINAKLARINDA ÇEVRESEL KONTROL SİSTEMLERİ İÇİN AKILLI TARIM UYGULAMALARI

Gürkan A. K. Gürdil(ONDOKUZ MAYIS UNIVERSITY)0000-0001-7764-3977
Fuat Lüle(Adıyaman Üniversitesi)0000-0002-9332-0761
Melih Cevdet Pehlivanlı(ONDOKUZ MAYIS ÜNİVERSİTESİ)0009-0000-8765-3060
Mustafa Çetin(ONDOKUZ MAYIS ÜNİVERSİTESİ)0009-0001-0245-0465
Pages
108–121
Publication Date
2024-12-27
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Smart agriculture tools are rapidly transforming traditional farming practices by enabling precise control over various environmental parameters.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:These advancements are improving animal health, enhancing productivity, reducing resource consumption, and lowering environmental impact.
🔬Design:Derleme
📍Scope:Comprehensive Sample
⚙️Variables:Akıllı tarım · Hayvan · Havalandırma

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.

Özet

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.

🔖

Cite This Article

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, Cilt: 12 Sayı: 2, 108–121. https://ajindex.com/articles/1441037

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (23)

  1. Ali, S. (2022) Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems, Journal of Engineering Research: Vol. 6: Iss. 3, Article 5.
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. Smith, N., (2024). Environmental Sustainability in Livestock Production. International Journal of Livestock Policy, 2(1), 26–38. https://doi.org/10.47941/ijlp.1701
  20. 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
  21. 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
  22. 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
  23. 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