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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
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    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.
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    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.

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