IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0193620.html
   My bibliography  Save this article

Evaluation of a miniaturized NIR spectrometer for cultivar identification: The case of barley, chickpea and sorghum in Ethiopia

Author

Listed:
  • Frédéric Kosmowski
  • Tigist Worku

Abstract

Crop cultivar identification is fundamental for agricultural research, industry and policies. This paper investigates the feasibility of using visible/near infrared hyperspectral data collected with a miniaturized NIR spectrometer to identify cultivars of barley, chickpea and sorghum in the context of Ethiopia. A total of 2650 grains of barley, chickpea and sorghum cultivars were scanned using the SCIO, a recently released miniaturized NIR spectrometer. The effects of data preprocessing techniques and choosing a machine learning algorithm on distinguishing cultivars are further evaluated. Predictive multiclass models of 24 barley cultivars, 19 chickpea cultivars and 10 sorghum cultivars delivered an accuracy of 89%, 96% and 87% on hold-out sample. The Support Vector Machine (SVM) and Partial least squares discriminant analysis (PLS-DA) algorithms consistently outperformed other algorithms. Several cultivars, believed to be widely adopted in Ethiopia, were identified with perfect accuracy. These results advance the discussion on cultivar identification survey methods by demonstrating that miniaturized NIR spectrometers represent a low-cost, rapid and viable tool. We further discuss the potential utility of the method for adoption surveys, field-scale agronomic studies, socio-economic impact assessments and value chain quality control. Finally, we provide a free tool for R to easily carry out crop cultivar identification and measure uncertainty based on spectral data.

Suggested Citation

  • Frédéric Kosmowski & Tigist Worku, 2018. "Evaluation of a miniaturized NIR spectrometer for cultivar identification: The case of barley, chickpea and sorghum in Ethiopia," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    • Handle: RePEc:plo:pone00:0193620
    DOI: 10.1371/journal.pone.0193620
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193620
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0193620&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0193620?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Michael R. Carter & Rachid Laajaj & Dean Yang, 2013. "The Impact of Voucher Coupons on the Uptake of Fertilizer and Improved Seeds: Evidence from a Randomized Trial in Mozambique," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(5), pages 1345-1351.
    3. Alfaro, Esteban & Gamez, Matias & García, Noelia, 2013. "adabag: An R Package for Classification with Boosting and Bagging," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i02).
    4. Salvador Gutiérrez & Javier Tardaguila & Juan Fernández-Novales & María P Diago, 2015. "Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-15, November.
    5. Asfaw, Solomon & Shiferaw, Bekele & Simtowe, Franklin & Lipper, Leslie, 2012. "Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia," Food Policy, Elsevier, vol. 37(3), pages 283-295.
    6. Maredia, Mywish K. & Reyes, Byron A. & Manu-Aduening, Joseph & Dankyi, Awere & Hamazakaza, Petan & Muimui, Kennedy & Rabbi, Ismail & Kulakow, Peter & Parkes, Elizabeth & Abdoulaye, Tahirou & Katungi, , 2016. "Testing Alternative Methods of Varietal Identification Using DNA Fingerprinting: Results of Pilot Studies in Ghana and Zambia," Food Security International Development Working Papers 246950, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    7. Martina Vrešak & Merete Halkjaer Olesen & René Gislum & Franc Bavec & Johannes Ravn Jørgensen, 2016. "The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-10, March.
    8. Verkaart, Simone & Munyua, Bernard G. & Mausch, Kai & Michler, Jeffrey D., 2017. "Welfare impacts of improved chickpea adoption: A pathway for rural development in Ethiopia?," Food Policy, Elsevier, vol. 66(C), pages 50-61.
    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. Calogero Carletto, 2021. "Better data, higher impact: improving agricultural data systems for societal change [Correlated non-classical measurement errors, ‘second best’ policy inference, and the inverse size-productivity r," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 48(4), pages 719-740.

    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. Ayala Wineman & Timothy Njagi & C. Leigh Anderson & Travis W. Reynolds & Didier Yélognissè Alia & Priscilla Wainaina & Eric Njue & Pierre Biscaye & Miltone W. Ayieko, 2020. "A Case of Mistaken Identity? Measuring Rates of Improved Seed Adoption in Tanzania Using DNA Fingerprinting," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 719-741, September.
    2. Armel Nonvide, Gbêtondji Melaine, 2023. "Impact of information and communication technologies on agricultural households’ welfare in Benin," Telecommunications Policy, Elsevier, vol. 47(6).
    3. Yigezu, Yigezu A. & Alwang, Jeffrey & Rahman, M. Wakilur & Mollah, M. Bazlur R. & El-Shater, Tamer & Aw-Hassan, Aden & Sarker, Ashutosh, 2019. "Is DNA fingerprinting the gold standard for estimation of adoption and impacts of improved lentil varieties?," Food Policy, Elsevier, vol. 83(C), pages 48-59.
    4. Bairagi, Subir & Bhandari, Humnath & Kumar Das, Subrata & Mohanty, Samarendu, 2021. "Flood-tolerant rice improves climate resilience, profitability, and household consumption in Bangladesh," Food Policy, Elsevier, vol. 105(C).
    5. Felister Y. Tibamanya & Mursali A. Milanzi & Arne Henningsen, 2021. "Drivers of and Barriers to Adoption of Improved Sun- flower Varieties amongst Smallholder Farmers in Singida, Tanzania: the Double-Hurdle Approach," IFRO Working Paper 2021/03, University of Copenhagen, Department of Food and Resource Economics.
    6. Jourdain C. Lokossou & Hippolyte D. Affognon & Alphonse Singbo & Michel B. Vabi & Ayoni Ogunbayo & Paul Tanzubil & Alcade C. Segnon & Geoffrey Muricho & Haile Desmae & Hakeem Ajeigbe, 2022. "Welfare impacts of improved groundnut varieties adoption and food security implications in the semi-arid areas of West Africa," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(3), pages 709-728, June.
    7. Mesele Belay & Markew Mengiste, 2023. "The ex‐post impact of agricultural technology adoption on poverty: Evidence from north Shewa zone of Amhara region, Ethiopia," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1327-1337, April.
    8. Kazushi Takahashi & Rie Muraoka & Keijiro Otsuka, 2020. "Technology adoption, impact, and extension in developing countries’ agriculture: A review of the recent literature," Agricultural Economics, International Association of Agricultural Economists, vol. 51(1), pages 31-45, January.
    9. Wubneshe Dessalegn Biru & Manfred Zeller & Tim K. Loos, 2020. "The Impact of Agricultural Technologies on Poverty and Vulnerability of Smallholders in Ethiopia: A Panel Data Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 517-544, January.
    10. Abebayehu Girma Geffersa & Frank W. Agbola & Amir Mahmood, 2022. "Improved maize adoption and impacts on farm household welfare: Evidence from rural Ethiopia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(4), pages 860-886, October.
    11. Wossen, Tesfamicheal & Alene, Arega & Abdoulaye, Tahirou & Feleke, Shiferaw & Manyong, Victor, 2019. "Agricultural technology adoption and household welfare: Measurement and evidence," Food Policy, Elsevier, vol. 87(C), pages 1-1.
    12. Garbero, A. & Marion, P., 2018. "IFAD RESEARCH SERIES 28 - Understanding the dynamics of adoption decisions and their poverty impacts: the case of improved maize seeds in Uganda," IFAD Research Series 280077, International Fund for Agricultural Development (IFAD).
    13. Aseres Mamo Eshetie & Eunice Matafwali & Gershom Endelani Mwalupaso & Jie Li & Aijun Liu, 2022. "Nexus of Cash Crop Production Using Improved Varieties and Household Food Security," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 34(4), pages 1803-1830, August.
    14. Tambo, Justice A. & Wünscher, Tobias, 2016. "Beyond adoption: welfare effects of farmer innovation behavior in Ghana," Discussion Papers 235297, University of Bonn, Center for Development Research (ZEF).
    15. Bonatti, Alessandro & Hörner, Johannes, 2017. "Learning to disagree in a game of experimentation," Journal of Economic Theory, Elsevier, vol. 169(C), pages 234-269.
    16. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    17. Jie Zhao & Ji Chen & Damien Beillouin & Hans Lambers & Yadong Yang & Pete Smith & Zhaohai Zeng & Jørgen E. Olesen & Huadong Zang, 2022. "Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    18. Bradfield, Tracy & Butler, Robert & Dillon, Emma J. & Hennessy, Thia & Loughrey, Jason, 2023. "The impact of long-term land leases on farm investment: Evidence from the Irish dairy sector," Land Use Policy, Elsevier, vol. 126(C).
    19. Seydou Zakari & Germaine Ibro & Bokar Moussa & Tahirou Abdoulaye, 2022. "Adaptation Strategies to Climate Change and Impacts on Household Income and Food Security: Evidence from Sahelian Region of Niger," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    20. Piaopiao Chen & Agnès H. Michel & Jianzhi Zhang, 2022. "Transposon insertional mutagenesis of diverse yeast strains suggests coordinated gene essentiality polymorphisms," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0193620. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.