IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i9p1350-d903527.html
   My bibliography  Save this article

Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey

Author

Listed:
  • Tiago Domingues

    (Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisboa, Portugal
    Inov Inesc Inovação, Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal)

  • Tomás Brandão

    (Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisboa, Portugal)

  • João C. Ferreira

    (Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, 1649-026 Lisboa, Portugal
    Inov Inesc Inovação, Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal)

Abstract

Considering the population growth rate of recent years, a doubling of the current worldwide crop productivity is expected to be needed by 2050. Pests and diseases are a major obstacle to achieving this productivity outcome. Therefore, it is very important to develop efficient methods for the automatic detection, identification, and prediction of pests and diseases in agricultural crops. To perform such automation, Machine Learning (ML) techniques can be used to derive knowledge and relationships from the data that is being worked on. This paper presents a literature review on ML techniques used in the agricultural sector, focusing on the tasks of classification, detection, and prediction of diseases and pests, with an emphasis on tomato crops. This survey aims to contribute to the development of smart farming and precision agriculture by promoting the development of techniques that will allow farmers to decrease the use of pesticides and chemicals while preserving and improving their crop quality and production.

Suggested Citation

  • Tiago Domingues & Tomás Brandão & João C. Ferreira, 2022. "Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey," Agriculture, MDPI, vol. 12(9), pages 1-23, September.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1350-:d:903527
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/9/1350/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/9/1350/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shan-e-Ahmed Raza & Gillian Prince & John P Clarkson & Nasir M Rajpoot, 2015. "Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    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. Yulia Resti & Chandra Irsan & Adinda Neardiaty & Choirunnisa Annabila & Irsyadi Yani, 2023. "Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests," Mathematics, MDPI, vol. 11(8), pages 1-21, April.
    2. Tengxiang Yang & Chengqian Jin & Youliang Ni & Zhen Liu & Man Chen, 2023. "Path Planning and Control System Design of an Unmanned Weeding Robot," Agriculture, MDPI, vol. 13(10), pages 1-15, October.
    3. Tiago Domingues & Tomás Brandão & Ricardo Ribeiro & João C. Ferreira, 2022. "Insect Detection in Sticky Trap Images of Tomato Crops Using Machine Learning," Agriculture, MDPI, vol. 12(11), pages 1-19, November.

    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. Maimunah Mohd Ali & Norhashila Hashim & Samsuzana Abd Aziz & Ola Lasekan, 2022. "Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms," Agriculture, MDPI, vol. 12(7), pages 1-17, July.
    2. Ganbayar Batchuluun & Se Hyun Nam & Kang Ryoung Park, 2022. "Deep Learning-Based Plant Classification Using Nonaligned Thermal and Visible Light Images," Mathematics, MDPI, vol. 10(21), pages 1-18, November.
    3. Ganbayar Batchuluun & Se Hyun Nam & Chanhum Park & Kang Ryoung Park, 2022. "Super-Resolution Reconstruction-Based Plant Image Classification Using Thermal and Visible-Light Images," Mathematics, MDPI, vol. 11(1), pages 1-22, December.
    4. Alejandro Pena & Juan C. Tejada & Juan David Gonzalez-Ruiz & Mario Gongora, 2022. "Deep Learning to Improve the Sustainability of Agricultural Crops Affected by Phytosanitary Events: A Financial-Risk Approach," Sustainability, MDPI, vol. 14(11), pages 1-28, May.
    5. Aneta Saletnik & Bogdan Saletnik & Grzegorz Zaguła & Czesław Puchalski, 2024. "Raman Spectroscopy for Plant Disease Detection in Next-Generation Agriculture," Sustainability, MDPI, vol. 16(13), pages 1-18, 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:9:p:1350-:d:903527. 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.