IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p12005-d668404.html
   My bibliography  Save this article

Is Einkorn Wheat ( Triticum monococcum L.) a Better Choice than Winter Wheat ( Triticum aestivum L.)? Wheat Quality Estimation for Sustainable Agriculture Using Vision-Based Digital Image Analysis

Author

Listed:
  • Edina Csákvári

    (Environmental Sciences Doctoral School, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary
    ELKH Centre for Ecological Research, Institute of Ecology and Botany, Alkotmány u. 2-4, 2163 Vácrátót, Hungary)

  • Melinda Halassy

    (ELKH Centre for Ecological Research, Institute of Ecology and Botany, Alkotmány u. 2-4, 2163 Vácrátót, Hungary)

  • Attila Enyedi

    (Institute of Information Technology, Dennis Gabor College, Fejér Lipót u. 70, 1119 Budapest, Hungary)

  • Ferenc Gyulai

    (Environmental Sciences Doctoral School, Hungarian University of Agriculture and Life Sciences, Páter Károly u. 1, 2100 Gödöllő, Hungary)

  • József Berke

    (Institute of Information Technology, Dennis Gabor College, Fejér Lipót u. 70, 1119 Budapest, Hungary)

Abstract

Einkorn wheat ( Triticum monococcum L. ssp. monococcum ) plays an increasingly important role in agriculture, promoted by organic farming. Although the number of comparative studies about modern and ancient types of wheats is increasing, there are still some knowledge gaps about the nutritional and health benefit differences between ancient and modern bread wheats. The aim of the present study was to compare ancient, traditional and modern wheat cultivars—including a field study and a laboratory stress experiment using vision-based digital image analysis—and to assess the feasibility of imaging techniques. Our study shows that modern winter wheat had better yield and grain quality compared to einkorn wheats, but the latter were not far behind; thus the cultivation of various species could provide a diverse and sustainable agriculture which contributes to higher agrobiodiversity. The results also demonstrate that digital image analysis could be a viable alternate method for the real-time estimation of aboveground biomass and for predicting yield and grain quality parameters. Digital area outperformed other digital variables in biomass prediction in relation to drought stress, but height and Feret’s diameter better correlated with yield and grain quality parameters. Based on these results we suggest that the combination of various vision-based methods could improve the performance estimation of modern and ancient types of wheat in a non-destructive and real-time manner.

Suggested Citation

  • Edina Csákvári & Melinda Halassy & Attila Enyedi & Ferenc Gyulai & József Berke, 2021. "Is Einkorn Wheat ( Triticum monococcum L.) a Better Choice than Winter Wheat ( Triticum aestivum L.)? Wheat Quality Estimation for Sustainable Agriculture Using Vision-Based Digital Image Analysis," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12005-:d:668404
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/12005/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/21/12005/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Magdalena Ruiz & Encarna Zambrana & Rosario Fite & Aida Sole & Jose Luis Tenorio & Elena Benavente, 2019. "Yield and Quality Performance of Traditional and Improved Bread and Durum Wheat Varieties under Two Conservation Tillage Systems," Sustainability, MDPI, vol. 11(17), pages 1-22, August.
    2. Szilvia Bencze & Marianna Makádi & Tibor J. Aranyos & Mihály Földi & Péter Hertelendy & Péter Mikó & Sara Bosi & Lorenzo Negri & Dóra Drexler, 2020. "Re-Introduction of Ancient Wheat Cultivars into Organic Agriculture—Emmer and Einkorn Cultivation Experiences under Marginal Conditions," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    3. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
    Full references (including those not matched with items on IDEAS)

    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. Gniewko Niedbała & Danuta Kurasiak-Popowska & Magdalena Piekutowska & Tomasz Wojciechowski & Michał Kwiatek & Jerzy Nawracała, 2022. "Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean ( Glycine max [L.] Merrill) Cultivar Augusta," Agriculture, MDPI, vol. 12(6), pages 1-17, May.
    2. Juan Diego Valenzuela-Cobos & Fabricio Guevara-Viejó & Purificación Vicente-Galindo & Purificación Galindo-Villardón, 2023. "Eco-Friendly Biocontrol of Moniliasis in Ecuadorian Cocoa Using Biplot Techniques," Sustainability, MDPI, vol. 15(5), pages 1-12, February.
    3. Sebastian Kujawa & Gniewko Niedbała, 2021. "Artificial Neural Networks in Agriculture," Agriculture, MDPI, vol. 11(6), pages 1-6, May.
    4. Majid Yousefian & Feizollah Shahbazi & Kianoosh Hamidian, 2021. "Crop Yield and Physicochemical Properties of Wheat Grains as Affected by Tillage Systems," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    5. Xiaohong Zhou & Donghong Ding, 2022. "Factors Influencing Farmers’ Willingness and Behaviors in Organic Agriculture Development: An Empirical Analysis Based on Survey Data of Farmers in Anhui Province," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
    6. Guiomar Carranza-Gallego & Gloria I. Guzmán & Roberto Garcia-Ruíz & Manuel González de Molina & Eduardo Aguilera, 2019. "Addressing the Role of Landraces in the Sustainability of Mediterranean Agroecosystems," Sustainability, MDPI, vol. 11(21), pages 1-16, October.
    7. Maurice Osewe & Chris Miyinzi Mwungu & Aijun Liu, 2020. "Does Minimum Tillage Improve Smallholder Farmers’ Welfare? Evidence from Southern Tanzania," Land, MDPI, vol. 9(12), pages 1-12, December.
    8. Elżbieta Suchowilska & Teresa Bieńkowska & Kinga Stuper-Szablewska & Marian Wiwart, 2020. "Concentrations of Phenolic Acids, Flavonoids and Carotenoids and the Antioxidant Activity of the Grain, Flour and Bran of Triticum polonicum as Compared with Three Cultivated Wheat Species," Agriculture, MDPI, vol. 10(12), pages 1-19, November.
    9. Mohsen Sabzi-Nojadeh & Gniewko Niedbała & Mehdi Younessi-Hamzekhanlu & Saeid Aharizad & Mohammad Esmaeilpour & Moslem Abdipour & Sebastian Kujawa & Mohsen Niazian, 2021. "Modeling the Essential Oil and Trans -Anethole Yield of Fennel ( Foeniculum vulgare Mill. var. vulgare ) by Application Artificial Neural Network and Multiple Linear Regression Methods," Agriculture, MDPI, vol. 11(12), pages 1-17, November.
    10. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    11. Siraj Osman Omer, 2021. "Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 7(3), pages 158-166, 07-2021.
    12. Meenakshi Sharma & Prashant Kaushik & Aakash Chawade, 2021. "Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research," Sustainability, MDPI, vol. 13(15), pages 1-14, August.
    13. Mohammad Rokhafrouz & Hooman Latifi & Ali A. Abkar & Tomasz Wojciechowski & Mirosław Czechlowski & Ali Sadeghi Naieni & Yasser Maghsoudi & Gniewko Niedbała, 2021. "Simplified and Hybrid Remote Sensing-Based Delineation of Management Zones for Nitrogen Variable Rate Application in Wheat," Agriculture, MDPI, vol. 11(11), pages 1-24, November.
    14. Eszter Sugár & Nándor Fodor & Renáta Sándor & Péter Bónis & Gyula Vida & Tamás Árendás, 2019. "Spelt Wheat: An Alternative for Sustainable Plant Production at Low N-Levels," Sustainability, MDPI, vol. 11(23), pages 1-16, November.

    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:jsusta:v:13:y:2021:i:21:p:12005-:d:668404. 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.