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DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY

Year 2023, Volume: 1 Issue: 1, 33 - 42, 27.12.2023

Eray Güney Enes Özdemir İsmail Kayadibi Nihan Kosku Perkgöz Ümmühan Başaran Filik

Abstract

Addressing the critical global need to combat agricultural scarcity, this research introduces artificial intelligence (AI) based smart greenhouse prototype as a holistic solution for enhanced productivity and sustainable practices. The study presents the design and implementation of an AI-powered intelligent greenhouse that integrates advanced technologies to optimize agricultural processes. Central to this prototype are integrated sensors that continuously capture real-time data on environmental parameters and crop conditions. This data is then used to develop predictive models, mitigating potential issues such as crop diseases. Complementing these capabilities, renewable energy sources, specifically solar power, are harnessed to meet the greenhouse's energy requirements, fostering eco-friendly operations. The research outlines a comprehensive system architecture, encompassing sensor data acquisition, serial communication, Python-based data analysis, and integration with the Thingspeak platform for data visualization and access. This cohesive setup allows stakeholders to gain insights into the greenhouse environment and crop well-being, facilitating informed decision-making. The proposed smart greenhouse prototype presents an innovative approach to precision agriculture, showcasing the potential of AI and renewable energy integration in revolutionizing conventional farming practices. By enhancing productivity, energy efficiency, and adaptability, this prototype offers a promising solution to address the challenges of modern agriculture while promoting sustainability.

Keywords

Artificial Intelligence Energy Efficiency Precision Agriculture Smart Greenhouse

Supporting Institution

Scientific and Technological Research Council of Turkey (TUBITAK)

Project Number

1919B012218243

Thanks

In memory of the 2023 Kahramanmaraş earthquake victims, we extend our gratitude to Ahmet Muhittin Özer, a driving force behind this project, who was tragically lost in the quake. We also acknowledge the support of the Scientific and Technological Research Council of Turkey (TUBITAK) under grant 1919B012218243. We would also like to thank Sarıcakaya Municipality for their contribution during the realisation of the project.

References

  • [1] World Bank Group. Population Estimates and Projections. 2020. Available online: https://databank.worldbank.org/source/ population-estimates-and-projections (accessed on 6 September 2021).
  • [2] The State of Food and Agriculture; Food and Agriculture Organization of the United Nations: Rome, Italy, 2017.
  • [3] L. Graamans, E. Baeza, A. Van Den Dobbelsteen, I. Tsafaras and C. Stanghellini, Plant factories versus greenhouses: Comparison of resource use efficiency. Agric. Sys. 160, 31-43, 2018.
  • [4] T. Chapagain and M. N. Raizada, Impacts of natural disasters on smallholder farmers: gaps and recommendations. Agriculture & Food Security, 6(1), 1-16, 2017.
  • [5] N. Ariesen-Verschuur, C. Verdouw and B. Tekinerdogan, Digital Twins in greenhouse horticulture: A review. Computers and Electronics in Agriculture, 199, 107183, 2022.
  • [6] A. Yahaya, Y. A. Abass and S.A Adeshina, Greenhouse monitoring and control system with an Arduino system. In 2019 15th International Conference on Electronics, Computer and Computation (ICECCO), 1-6, 2019.
  • [7] J. D. Chaux, D. Sanchez-Londono and G. Barbieri, A digital twin architecture to optimize productivity within controlled environment agriculture. Applied Sciences, 11(19), 8875, 2021.
  • [8] Clausen, K. Arendt, A. Johansen, F.C. Sangogboye, M.B. Kjærgaard, C. T. Veje and B. N. Jørgensen, A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings. Energy Informatics, 4, 1-19, 2021.
  • [9] R. Kumar, K. Pandey and P. Rai, Advanced Greenhouse Monitoring and Control System. In Biennial International Conference on Future Learning Aspects of Mechanical Engineering, 479-492, 2022.
  • [10] J. N. C. Bongulto, A. Y. L. O Cabato and R.B. Caldo, Design and implementation of smart farm data logging and monitoring system. Laguna J. Eng. Comput. Stud, 3(3), 42-54, 2016.
  • [11] J. A. Enokela and T. O. Othoigbe, An automated greenhouse control system using Arduino prototyping platform. Australian Journal of Engineering Research, 1(1), 64-73, 2015.
  • [12] J. J. Wong, Smart Green House using IOT and cloud computing (Doctoral dissertation, UTAR), 2021.
  • [13] A. Haridasan, J. Thomas and E. D. Raj, Deep learning system for paddy plant disease detection and classification. Environmental Monitoring and Assessment, 195(1), 120, 2023.
  • [14] Petrakis, T., Kavga, A., Thomopoulos, V., & Argiriou, A. A. Neural Network Model for Greenhouse Microclimate Predictions. Agriculture, 12(6), 780, 2022.

GELİŞMİŞ TARIMSAL SÜRDÜRÜLEBİLİRLİK İÇİN YAPAY ZEKA TABANLI AKILLI SERA PROTOTİPİNİN GELİŞTİRİLMESİ

Year 2023, Volume: 1 Issue: 1, 33 - 42, 27.12.2023

Eray Güney Enes Özdemir İsmail Kayadibi Nihan Kosku Perkgöz Ümmühan Başaran Filik

Abstract

Tarımsal kıtlıkla mücadele için kritik küresel ihtiyacı ele alan bu araştırma, gelişmiş verimlilik ve sürdürülebilir uygulamalar için bütünsel bir çözüm olarak yapay zekâ (AI) tabanlı bir akıllı sera prototipini tanıtmaktadır. Bu çalışma, tarımsal süreçleri optimize etmek için gelişmiş teknolojileri entegre eden yapay zeka destekli akıllı bir seranın tasarımını ve uygulamasını sunmaktadır. Bu prototipin merkezinde, çevresel parametreler ve mahsul koşulları hakkında gerçek zamanlı verileri sürekli olarak yakalayan entegre sensörler bulunmaktadır. Bu veriler daha sonra tahmine dayalı modeller geliştirmek için kullanılıyor ve mahsul hastalıkları gibi potansiyel sorunları hafifletebilmektedir. Bu yetenekleri tamamlayan yenilenebilir enerji kaynakları, özellikle de güneş enerjisi, seranın enerji gereksinimlerini karşılamak için kullanılıyor ve çevre dostu operasyonları teşvik etmektedir. Bu çalışmada sensör veri toplama, seri iletişim, Python tabanlı veri analizi ve veri görselleştirme ve erişim için Thingspeak platformuyla entegrasyonu içeren kapsamlı bir sistem mimarisinin ana hatları sunulmuştur. Bu uyumlu kurulum, paydaşların sera ortamı ve mahsulün refahı hakkında bilgi edinmesini sağlayarak bilinçli karar vermeyi kolaylaştırmaktadır. Önerilen akıllı sera prototipi, geleneksel tarım uygulamalarında devrim yaratmada yapay zekâ ve yenilenebilir enerji entegrasyonunun potansiyelini sergileyerek hassas tarıma yenilikçi bir yaklaşım sunmaktadır. Üretkenliği, enerji verimliliğini ve uyarlanabilirliği artıran bu prototip, sürdürülebilirliği teşvik ederken modern tarımın zorluklarını ele almak için umut verici bir çözüm sunmaktadır.

Keywords

Akıllı Sera Enerji Verimliliği Hassas Tarım Yapay Zeka

Project Number

1919B012218243

References

  • [1] World Bank Group. Population Estimates and Projections. 2020. Available online: https://databank.worldbank.org/source/ population-estimates-and-projections (accessed on 6 September 2021).
  • [2] The State of Food and Agriculture; Food and Agriculture Organization of the United Nations: Rome, Italy, 2017.
  • [3] L. Graamans, E. Baeza, A. Van Den Dobbelsteen, I. Tsafaras and C. Stanghellini, Plant factories versus greenhouses: Comparison of resource use efficiency. Agric. Sys. 160, 31-43, 2018.
  • [4] T. Chapagain and M. N. Raizada, Impacts of natural disasters on smallholder farmers: gaps and recommendations. Agriculture & Food Security, 6(1), 1-16, 2017.
  • [5] N. Ariesen-Verschuur, C. Verdouw and B. Tekinerdogan, Digital Twins in greenhouse horticulture: A review. Computers and Electronics in Agriculture, 199, 107183, 2022.
  • [6] A. Yahaya, Y. A. Abass and S.A Adeshina, Greenhouse monitoring and control system with an Arduino system. In 2019 15th International Conference on Electronics, Computer and Computation (ICECCO), 1-6, 2019.
  • [7] J. D. Chaux, D. Sanchez-Londono and G. Barbieri, A digital twin architecture to optimize productivity within controlled environment agriculture. Applied Sciences, 11(19), 8875, 2021.
  • [8] Clausen, K. Arendt, A. Johansen, F.C. Sangogboye, M.B. Kjærgaard, C. T. Veje and B. N. Jørgensen, A digital twin framework for improving energy efficiency and occupant comfort in public and commercial buildings. Energy Informatics, 4, 1-19, 2021.
  • [9] R. Kumar, K. Pandey and P. Rai, Advanced Greenhouse Monitoring and Control System. In Biennial International Conference on Future Learning Aspects of Mechanical Engineering, 479-492, 2022.
  • [10] J. N. C. Bongulto, A. Y. L. O Cabato and R.B. Caldo, Design and implementation of smart farm data logging and monitoring system. Laguna J. Eng. Comput. Stud, 3(3), 42-54, 2016.
  • [11] J. A. Enokela and T. O. Othoigbe, An automated greenhouse control system using Arduino prototyping platform. Australian Journal of Engineering Research, 1(1), 64-73, 2015.
  • [12] J. J. Wong, Smart Green House using IOT and cloud computing (Doctoral dissertation, UTAR), 2021.
  • [13] A. Haridasan, J. Thomas and E. D. Raj, Deep learning system for paddy plant disease detection and classification. Environmental Monitoring and Assessment, 195(1), 120, 2023.
  • [14] Petrakis, T., Kavga, A., Thomopoulos, V., & Argiriou, A. A. Neural Network Model for Greenhouse Microclimate Predictions. Agriculture, 12(6), 780, 2022.
There are 14 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other), Electronics, Sensors and Digital Hardware (Other)
Journal Section Research Articles
Authors

Eray Güney 0009-0007-5057-9443

Enes Özdemir 0009-0006-0119-6211

İsmail Kayadibi 0000-0002-1949-8211

Nihan Kosku Perkgöz 0000-0003-1331-0959

Ümmühan Başaran Filik 0000-0002-0715-821X

Project Number 1919B012218243
Publication Date December 27, 2023
Submission Date November 5, 2023
Acceptance Date December 15, 2023
Published in Issue Year 2023 Volume: 1 Issue: 1

Cite

APA Güney, E., Özdemir, E., Kayadibi, İ., Kosku Perkgöz, N., et al. (2023). DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY. Akdeniz Mühendislik Dergisi, 1(1), 33-42.
AMA Güney E, Özdemir E, Kayadibi İ, Kosku Perkgöz N, Başaran Filik Ü. DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY. AKUJE. December 2023;1(1):33-42.
Chicago Güney, Eray, Enes Özdemir, İsmail Kayadibi, Nihan Kosku Perkgöz, and Ümmühan Başaran Filik. “DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY”. Akdeniz Mühendislik Dergisi 1, no. 1 (December 2023): 33-42.
EndNote Güney E, Özdemir E, Kayadibi İ, Kosku Perkgöz N, Başaran Filik Ü (December 1, 2023) DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY. Akdeniz Mühendislik Dergisi 1 1 33–42.
IEEE E. Güney, E. Özdemir, İ. Kayadibi, N. Kosku Perkgöz, and Ü. Başaran Filik, “DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY”, AKUJE, vol. 1, no. 1, pp. 33–42, 2023.
ISNAD Güney, Eray et al. “DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY”. Akdeniz Mühendislik Dergisi 1/1 (December 2023), 33-42.
JAMA Güney E, Özdemir E, Kayadibi İ, Kosku Perkgöz N, Başaran Filik Ü. DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY. AKUJE. 2023;1:33–42.
MLA Güney, Eray et al. “DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY”. Akdeniz Mühendislik Dergisi, vol. 1, no. 1, 2023, pp. 33-42.
Vancouver Güney E, Özdemir E, Kayadibi İ, Kosku Perkgöz N, Başaran Filik Ü. DEVELOPMENT OF AN AI-BASED SMART GREENHOUSE PROTOTYPE FOR ENHANCED AGRICULTURAL SUSTAINABILITY. AKUJE. 2023;1(1):33-42.
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