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A General Survey on Plants Disease Detection Using Image Processing, Deep Transfer Learning and Machine Learning Techniques

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Ubiquitous Networking (UNet 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12845))

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Abstract

The agricultural field is one of the mainstays of the Moroccan economy, with the growth of the human population, it becomes extremely difficult to meet the food needs of everyone. So, to meet the growing demand, the agricultural sector needs a boost to increase agricultural productivity. However, the agro-industrial part in Morocco is facing serious problems due to lack of water, common disasters and insect infestations as well as plant diseases. The presence of vermin and plant diseases is a major consideration that causes heavy losses in the country’s economy. Thus, health surveillance and the early detection of plant diseases are crucial tasks to contain the spread of disease and protect the crop. As a result, several pest control methods against diseases have been exploited by farmers to increase yield. In this paper, we provide a general survey of several studies conducted and methods used with their advantages and disadvantages over the past decade in the field of plant disease recognition using image processing techniques, deep learning, transfer learning, hyperspectral image analysis, machine learning and IoT system. Also, this survey presents the challenges to be overcome in the process of automatic identification of plant diseases. Therefore, these gaps that need to be filled form the basis for future work to be undertaken and which we will discuss later.

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Authors and Affiliations

  1. EST Research Group ENSEM, Hassan II University, Casablanca, Morocco

    Houda Orchi, Mohamed Sadik & Mohammed Khaldoun

Authors
  1. Houda Orchi
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  2. Mohamed Sadik
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  3. Mohammed Khaldoun
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Corresponding author

Correspondence to Houda Orchi .

Editor information

Editors and Affiliations

  1. University of Quebec at Montreal (UQAM), Motreal, QC, Canada

    Halima Elbiaze

  2. ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Essaid Sabir

  3. Universidad Publica de Navarra (UPNA), Pampelune, Spain

    Francisco Falcone

  4. ENSEM, Hassan II University of Casablanca, Casablanca, Morocco

    Mohamed Sadik

  5. University of Lorraine, Nancy, France

    Samson Lasaulce

  6. Sorbonne University, Villetaneuse, France

    Jalel Ben Othman

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Orchi, H., Sadik, M., Khaldoun, M. (2021). A General Survey on Plants Disease Detection Using Image Processing, Deep Transfer Learning and Machine Learning Techniques. In: Elbiaze, H., Sabir, E., Falcone, F., Sadik, M., Lasaulce, S., Ben Othman, J. (eds) Ubiquitous Networking. UNet 2021. Lecture Notes in Computer Science(), vol 12845. Springer, Cham. https://doi.org/10.1007/978-3-030-86356-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-86356-2_18

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