IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i7p1044-d865304.html
   My bibliography  Save this article

Development of an Energy Efficient and Fully Autonomous Low-Cost IoT System for Irrigation Scheduling in Water-Scarce Areas Using Different Water Sources

Author

Listed:
  • Zisis Tsiropoulos

    (Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 38446 Volos, Greece
    Agricultural and Environmental Solutions (AGENSO), Markou Mpotsari 47, 11742 Athens, Greece)

  • Evangelos Skoubris

    (Agricultural and Environmental Solutions (AGENSO), Markou Mpotsari 47, 11742 Athens, Greece
    Department of Surveying and Geoinformatics Engineering, School of Engineering, Agiou Spyridonos, University of West Attica, 12243 Egaleo, Greece)

  • Spyros Fountas

    (Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece)

  • Ioannis Gravalos

    (Department of Agrotechnology, School of Agricultural Sciences, University of Thessaly, Periferiaki odos Larissas—Trikalon, 41500 Larissa, Greece)

  • Theofanis Gemtos

    (Department of Agriculture Crop Production and Rural Environment, University of Thessaly, 38446 Volos, Greece)

Abstract

Politicians and the general public are concerned about climate change, water scarcity, and the constant reduction in agricultural land. Water reserves are scarce in many regions in the world, negatively affecting agricultural productivity, which makes it a necessity to introduce sustainable water resource management. Nowadays, there is a number of commercial IoT systems for irrigation scheduling, helping farmers to manage and save water. However, these systems focus on using the available fresh water sources, without being able to manage alternative water sources. In this study, an Arduino-based low-cost IoT system for automated irrigation scheduling is developed and implemented, which can provide measurements of water parameters with high precision using low-cost sensors. The system used weather station data combined with the FAO56 model for computing the water requirements for various crops, and it was capable of handling and monitoring different water streams by supervising their quality and quantity. The developed IoT system was tested in several field trials, to evaluate its capabilities and functionalities, including the sensors’ accuracy, its autonomous controlling and operation, and its power consumption. The results of this study show that the system worked efficiently on the management and monitoring of different types of water sources (rainwater, groundwater, seawater, and wastewater) and on automating the irrigation scheduling. In addition, it was proved that the system is can be used for long periods of time without any power source, making it ideal for using it on annual crops.

Suggested Citation

  • Zisis Tsiropoulos & Evangelos Skoubris & Spyros Fountas & Ioannis Gravalos & Theofanis Gemtos, 2022. "Development of an Energy Efficient and Fully Autonomous Low-Cost IoT System for Irrigation Scheduling in Water-Scarce Areas Using Different Water Sources," Agriculture, MDPI, vol. 12(7), pages 1-19, July.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:1044-:d:865304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/7/1044/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/7/1044/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pereira, L.S. & Paredes, P. & Jovanovic, N., 2020. "Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach," Agricultural Water Management, Elsevier, vol. 241(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anatolii Kucher & Vitaliy Krupin & Dariia Rudenko & Lesia Kucher & Mykola Serbov & Piotr Gradziuk, 2023. "Sustainable and Efficient Water Management for Resilient Regional Development: The Case of Ukraine," Agriculture, MDPI, vol. 13(7), pages 1-22, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Zhangsheng & Li, Yue & Wang, Rong & Xu, Xu & Ren, Dongyang & Huang, Quanzhong & Xiong, Yunwu & Huang, Guanhua, 2023. "Evaluation of irrigation water saving and salinity control practices of maize and sunflower in the upper Yellow River basin with an agro-hydrological model based method," Agricultural Water Management, Elsevier, vol. 278(C).
    2. Darouich, Hanaa & Karfoul, Razan & Ramos, Tiago B. & Moustafa, Ali & Shaheen, Baraa & Pereira, Luis S., 2021. "Crop water requirements and crop coefficients for jute mallow (Corchorus olitorius L.) using the SIMDualKc model and assessing irrigation strategies for the Syrian Akkar region," Agricultural Water Management, Elsevier, vol. 255(C).
    3. França, Ana Carolina Ferreira & Coelho, Rubens Duarte & da Silva Gundim, Alice & de Oliveira Costa, Jéfferson & Quiloango-Chimarro, Carlos Alberto, 2024. "Effects of different irrigation scheduling methods on physiology, yield, and irrigation water productivity of soybean varieties," Agricultural Water Management, Elsevier, vol. 293(C).
    4. Xing Liu & Zhaoyang Cai & Yan Xu & Huihui Zheng & Kaige Wang & Fengrong Zhang, 2022. "Suitability Evaluation of Cultivated Land Reserved Resources in Arid Areas Based on Regional Water Balance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1463-1479, March.
    5. Mondol, Md Anarul Haque & Zhu, Xuan & Dunkerley, David & Henley, Benjamin J., 2022. "Changing occurrence of crop water surplus or deficit and the impact of irrigation: An analysis highlighting consequences for rice production in Bangladesh," Agricultural Water Management, Elsevier, vol. 269(C).
    6. Liu, Meihan & Paredes, Paula & Shi, Haibin & Ramos, Tiago B. & Dou, Xu & Dai, Liping & Pereira, Luis S., 2022. "Impacts of a shallow saline water table on maize evapotranspiration and groundwater contribution using static water table lysimeters and the dual Kc water balance model SIMDualKc," Agricultural Water Management, Elsevier, vol. 273(C).
    7. Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
    8. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
    9. Liu, Meihan & Shi, Haibin & Paredes, Paula & Ramos, Tiago B. & Dai, Liping & Feng, Zhuangzhuang & Pereira, Luis S., 2022. "Estimating and partitioning maize evapotranspiration as affected by salinity using weighing lysimeters and the SIMDualKc model," Agricultural Water Management, Elsevier, vol. 261(C).
    10. Ramos, Tiago B. & Oliveira, Ana R. & Darouich, Hanaa & Gonçalves, Maria C. & Martínez-Moreno, Francisco J. & Rodríguez, Mario Ramos & Vanderlinden, Karl & Farzamian, Mohammad, 2023. "Field-scale assessment of soil water dynamics using distributed modeling and electromagnetic conductivity imaging," Agricultural Water Management, Elsevier, vol. 288(C).
    11. Qiu, Rangjian & Li, Longan & Liu, Chunwei & Wang, Zhenchang & Zhang, Baozhong & Liu, Zhandong, 2022. "Evapotranspiration estimation using a modified crop coefficient model in a rotated rice-winter wheat system," Agricultural Water Management, Elsevier, vol. 264(C).
    12. Shao, Guomin & Han, Wenting & Zhang, Huihui & Zhang, Liyuan & Wang, Yi & Zhang, Yu, 2023. "Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods," Agricultural Water Management, Elsevier, vol. 276(C).
    13. Tamimi, Mansoor Al & Green, Steve & Hammami, Zied & Ammar, Khalil & Ketbi, Mouza Al & Al-Shrouf, Ali M. & Dawoud, Mohamed & Kennedy, Lesley & Clothier, Brent, 2022. "Evapotranspiration and crop coefficients using lysimeter measurements for food crops in the hyper-arid United Arab Emirates," Agricultural Water Management, Elsevier, vol. 272(C).
    14. Lucas Borges Ferreira & Fernando França da Cunha & Sidney Sara Zanetti, 2021. "Selecting models for the estimation of reference evapotranspiration for irrigation scheduling purposes," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.
    15. Liu, Xuchen & Liu, Junming & Huang, Chao & Liu, Huihao & Meng, Ye & Chen, Haiqing & Ma, Shoutian & Liu, Zhandong, 2024. "The impacts of irrigation methods and regimes on the water and nitrogen utilization efficiency in subsoiling wheat fields," Agricultural Water Management, Elsevier, vol. 295(C).
    16. Qi, Zhi & Gao, Ya & Sun, Chen & Ramos, Tiago B. & Mu, Danning & Xun, Yihao & Huang, Guanhua & Xu, Xu, 2024. "Assessing water-nitrogen use, crop growth and economic benefits for maize in upper Yellow River basin: Feasibility analysis for border and drip irrigation," Agricultural Water Management, Elsevier, vol. 295(C).
    17. Mingze Yao & Manman Gao & Jingkuan Wang & Bo Li & Lizhen Mao & Mingyu Zhao & Zhanyang Xu & Hongfei Niu & Tieliang Wang & Lei Sun & Dongshuang Niu, 2023. "Estimating Evapotranspiration of Greenhouse Tomato under Different Irrigation Levels Using a Modified Dual Crop Coefficient Model in Northeast China," Agriculture, MDPI, vol. 13(9), pages 1-19, September.
    18. Arnesh Telukdarie & Noluthando Gamede & Inderasan Munien & Andre Vermeulen & Uche Onkonkwo, 2023. "The Potential Future Of Agriculture For Small Farms: Supervised Machine-Learning Smart Irrigation Concept For Vegetables," Big Data In Agriculture (BDA), Zibeline International Publishing, vol. 5(2), pages 57-63, July.
    19. Martínez-Romero, A. & López-Urrea, R. & Montoya, F. & Pardo, J.J. & Domínguez, A., 2021. "Optimization of irrigation scheduling for barley crop, combining AquaCrop and MOPECO models to simulate various water-deficit regimes," Agricultural Water Management, Elsevier, vol. 258(C).
    20. Lankford, Bruce A., 2023. "Resolving the paradoxes of irrigation efficiency: Irrigated systems accounting analyses depletion-based water conservation for reallocation," Agricultural Water Management, Elsevier, vol. 287(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:1044-:d:865304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.