IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i8p1226-d888479.html
   My bibliography  Save this article

Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique

Author

Listed:
  • Habib Khan

    (Sejong University, Seoul 05006, Korea)

  • Ijaz Ul Haq

    (Sejong University, Seoul 05006, Korea)

  • Muhammad Munsif

    (Sejong University, Seoul 05006, Korea)

  • Mustaqeem

    (Interaction Technology Laboratory, Department of Software Convergence, Sejong University, Seoul 05006, Korea)

  • Shafi Ullah Khan

    (Department of Electronics, Islamia College University, Peshawar 25000, Pakistan)

  • Mi Young Lee

    (Sejong University, Seoul 05006, Korea)

Abstract

Around the world, agriculture is one of the important sectors of human life in terms of food, business, and employment opportunities. In the farming field, wheat is the most farmed crop but every year, its ultimate production is badly influenced by various diseases. On the other hand, early and precise recognition of wheat plant diseases can decrease damage, resulting in a greater yield. Researchers have used conventional and Machine Learning (ML)-based techniques for crop disease recognition and classification. However, these techniques are inaccurate and time-consuming due to the unavailability of quality data, inefficient preprocessing techniques, and the existing selection criteria of an efficient model. Therefore, a smart and intelligent system is needed which can accurately identify crop diseases. In this paper, we proposed an efficient ML-based framework for various kinds of wheat disease recognition and classification to automatically identify the brown- and yellow-rusted diseases in wheat crops. Our method consists of multiple steps. Firstly, the dataset is collected from different fields in Pakistan with consideration of the illumination and orientation parameters of the capturing device. Secondly, to accurately preprocess the data, specific segmentation and resizing methods are used to make differences between healthy and affected areas. In the end, ML models are trained on the preprocessed data. Furthermore, for comparative analysis of models, various performance metrics including overall accuracy, precision, recall, and F1-score are calculated. As a result, it has been observed that the proposed framework has achieved 99.8% highest accuracy over the existing ML techniques.

Suggested Citation

  • Habib Khan & Ijaz Ul Haq & Muhammad Munsif & Mustaqeem & Shafi Ullah Khan & Mi Young Lee, 2022. "Automated Wheat Diseases Classification Framework Using Advanced Machine Learning Technique," Agriculture, MDPI, vol. 12(8), pages 1-20, August.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:8:p:1226-:d:888479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/8/1226/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/8/1226/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jenny C. Aker, 2011. "Dial “A” for agriculture: a review of information and communication technologies for agricultural extension in developing countries," Agricultural Economics, International Association of Agricultural Economists, vol. 42(6), pages 631-647, November.
    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. Meftah Salem M. Alfatni & Siti Khairunniza-Bejo & Mohammad Hamiruce B. Marhaban & Osama M. Ben Saaed & Aouache Mustapha & Abdul Rashid Mohamed Shariff, 2022. "Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis," Agriculture, MDPI, vol. 12(9), pages 1-28, September.
    2. Cuiling Li & Xiu Wang & Liping Chen & Xueguan Zhao & Yang Li & Mingzhou Chen & Haowei Liu & Changyuan Zhai, 2023. "Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns," Agriculture, MDPI, vol. 13(9), pages 1-26, August.

    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. Emmanuel Olatunbosun Benjamin & Oreoluwa Ola & Hannes Lang & Gertrud Buchenrieder, 2021. "Public-private cooperation and agricultural development in Sub-Saharan Africa: a review of Nigerian growth enhancement scheme and e-voucher program," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(1), pages 129-140, February.
    2. Blazquez-Soriano, Amparo & Ramos-Sandoval, Rosmery, 2022. "Information transfer as a tool to improve the resilience of farmers against the effects of climate change: The case of the Peruvian National Agrarian Innovation System," Agricultural Systems, Elsevier, vol. 200(C).
    3. Preusse, Verena & Wollni, Meike, 2021. "Adoption of sustainable agricultural practices in the context of urbanisation and environmental stress – Evidence from farmers in the rural-urban interface of Bangalore, India," 2021 Annual Meeting, August 1-3, Austin, Texas 312690, Agricultural and Applied Economics Association.
    4. World Bank, 2020. "Sudan Agriculture Value Chain Analysis," World Bank Publications - Reports 34103, The World Bank Group.
    5. George W. Norton & Jeffrey Alwang, 2020. "Changes in Agricultural Extension and Implications for Farmer Adoption of New Practices," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 8-20, March.
    6. Yoko Kijima, 2022. "Effect of Nigeria’s e-voucher input subsidy program on fertilizer use, rice production, and household income," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(4), pages 919-935, August.
    7. Torres Franco, Nicolás Arturo & Dávalos, Eleonora & Morales, Leonardo Fabio, 2021. "Heterogeneous Effects of Agricultural Technical Assistance in Colombia," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 53(4), pages 459-481, November.
    8. Chris Knudson & Zack Guido, 2019. "The missing middle of climate services: layering multiway, two-way, and one-way modes of communicating seasonal climate forecasts," Climatic Change, Springer, vol. 157(1), pages 171-187, November.
    9. Bahia, Kalvin & Castells, Pau & Cruz, Genaro & Masaki, Takaaki & Pedrós, Xavier & Pfutze, Tobias & Rodríguez-Castelán, Carlos & Winkler, Hernán, 2024. "The welfare effects of mobile broadband internet: Evidence from Nigeria," Journal of Development Economics, Elsevier, vol. 170(C).
    10. repec:pcz:journl:v:6:y:2012:i:1:p:151-161 is not listed on IDEAS
    11. Anderson Jock R. & Birner Regina & Nagarajan Latha & Naseem Anwar & Pray Carl E., 2021. "Private Agricultural R&D: Do the Poor Benefit?," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 19(1), pages 3-14, May.
    12. Sekabira, Haruna & Qaim, Matin, 2017. "Can mobile phones improve gender equality and nutrition? Panel data evidence from farm households in Uganda," Food Policy, Elsevier, vol. 73(C), pages 95-103.
    13. Ezinne M. Emeana & Liz Trenchard & Katharina Dehnen-Schmutz, 2020. "The Revolution of Mobile Phone-Enabled Services for Agricultural Development (m-Agri Services) in Africa: The Challenges for Sustainability," Sustainability, MDPI, vol. 12(2), pages 1-27, January.
    14. Kondylis, Florence & Mueller, Valerie, 2012. "Seeing is Believing? Evidence from a Demonstration Plot Experiment in Mozambique:," MSSP working papers 1, International Food Policy Research Institute (IFPRI).
    15. Wantchekon, Leonard & Riaz, Zara, 2019. "Mobile technology and food access," World Development, Elsevier, vol. 117(C), pages 344-356.
    16. Bjorn Van Campenhout & David J. Spielman & Els Lecoutere, 2021. "Information and Communication Technologies to Provide Agricultural Advice to Smallholder Farmers: Experimental Evidence from Uganda," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 317-337, January.
    17. G.T. Abate & Tanguy Bernard & Simrin Makhija & David J. Spielman, 2019. "Accelerating technical change through video-mediated agricultural extension: Evidence from Ethiopia," Working Papers hal-02879823, HAL.
    18. Liang Chi & Mengshuai Zhu & Chen Shen & Jing Zhang & Liwei Xing & Xiangyang Zhou, 2023. "Does the Winner Take All in E-Commerce of Agricultural Products under the Background of Platform Monopoly?," Agriculture, MDPI, vol. 13(2), pages 1-16, February.
    19. Yemane Asmelash Gebremariam & Joost Dessein & Beneberu Assefa Wondimagegnhu & Mark Breusers & Lutgart Lenaerts & Enyew Adgo & Zemen Ayalew & Amare Sewenet Minale & Jan Nyssen, 2021. "Determinants of Farmers’ Level of Interaction with Agricultural Extension Agencies in Northwest Ethiopia," Sustainability, MDPI, vol. 13(6), pages 1-24, March.
    20. Kamiche Zegarra, J. & Bravo-Ureta, B., 2018. "Are users of market information efficient? A stochastic production frontier model corrected by sample selection," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275870, International Association of Agricultural Economists.
    21. Ping Xue & Xinru Han & Yongchun Wang & Xiudong Wang, 2022. "Can Agricultural Machinery Harvesting Services Reduce Cropland Abandonment? Evidence from Rural China," Agriculture, MDPI, vol. 12(7), pages 1-15, June.

    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:8:p:1226-:d:888479. 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.