IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i4p776-d1108830.html
   My bibliography  Save this article

Smart Framework for Quality Check and Determination of Adulterants in Saffron Using Sensors and AquaCrop

Author

Listed:
  • Kanwalpreet Kour

    (Chitkara University Institute of Engineering &Technology, Chitkara University, Rajpura 140401, India)

  • Deepali Gupta

    (Chitkara University Institute of Engineering &Technology, Chitkara University, Rajpura 140401, India)

  • Junaid Rashid

    (Department of Data Science, Sejong University, Seoul 05006, Republic of Korea)

  • Kamali Gupta

    (Chitkara University Institute of Engineering &Technology, Chitkara University, Rajpura 140401, India)

  • Jungeun Kim

    (Department of Software and CMPSI, Kongju National University, Cheonan 31080, Republic of Korea)

  • Keejun Han

    (School of Computer Engineering, Hansung University, Seoul 02876, Republic of Korea)

  • Khalid Mohiuddin

    (Department of Management Information Systems, College of Business, King Khalid University, Abha 61471, Saudi Arabia)

Abstract

Saffron is a rare and valuable crop that is only cultivated in specific regions with suitable topographical conditions. To improve saffron cultivation, it is crucial to monitor and precisely control the crop’s agronomic variables over at least one growth cycle to create a fully automated environment. To this end, agronomic variables in the Punjab region of India were analyzed and set points were calculated using third-order polynomial equations through the application of image processing techniques. The relationship between canopy cover, growth percentage, and agronomic variables was also investigated for optimal yield and quality. The addition of adulterants, such as turmeric and artificial colorants, to saffron is a major concern due to the potential for quality compromise and fraud by supply chain vendors. Hence, there is a need for devising an easy, reliable, and user-friendly mechanism to help in the detection of adulterants added to the saffron stigmas. This paper proposes an automated IoT-based saffron cultivation environment using sensors for determining set points of agronomical variables. In addition, a sensor-based chamber has been proposed to provide quality and adulteration checks of saffron and to eliminate product counterfeiting. The AquaCrop simulator was employed to evaluate the proposed framework’s performance. The results of the simulation show improved biomass, yield, and harvest index compared with the existing solutions in precision agriculture. Given the high value and demand for saffron, ensuring its purity and quality is essential to sustain its cultivation and the economic viability of the market.

Suggested Citation

  • Kanwalpreet Kour & Deepali Gupta & Junaid Rashid & Kamali Gupta & Jungeun Kim & Keejun Han & Khalid Mohiuddin, 2023. "Smart Framework for Quality Check and Determination of Adulterants in Saffron Using Sensors and AquaCrop," Agriculture, MDPI, vol. 13(4), pages 1-21, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:776-:d:1108830
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/4/776/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/4/776/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kanwalpreet Kour & Deepali Gupta & Kamali Gupta & Sapna Juneja & Manjit Kaur & Amal H. Alharbi & Heung-No Lee, 2022. "Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    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. Konstantinos Dolaptsis & Xanthoula Eirini Pantazi & Charalampos Paraskevas & Selçuk Arslan & Yücel Tekin & Bere Benjamin Bantchina & Yahya Ulusoy & Kemal Sulhi Gündoğdu & Muhammad Qaswar & Danyal Bust, 2024. "A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop," Agriculture, MDPI, vol. 14(2), pages 1-25, January.

    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. Mudita Uppal & Deepali Gupta & Sapna Juneja & Adel Sulaiman & Khairan Rajab & Adel Rajab & M. A. Elmagzoub & Asadullah Shaikh, 2022. "Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning," Sustainability, MDPI, vol. 14(18), pages 1-19, September.
    2. Monica Dutta & Deepali Gupta & Yasir Javed & Khalid Mohiuddin & Sapna Juneja & Zafar Iqbal Khan & Ali Nauman, 2023. "Monitoring Root and Shoot Characteristics for the Sustainable Growth of Barley Using an IoT-Enabled Hydroponic System and AquaCrop Simulator," Sustainability, MDPI, vol. 15(5), pages 1-17, March.

    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:13:y:2023:i:4:p:776-:d:1108830. 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.