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Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms

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  • Ozguven, Mehmet Metin
  • Adem, Kemal

Abstract

Depending on the severity of the leaf spot disease in the field, it can cause a loss in sugar yield by 10% to 50%. Therefore, disease symptoms should be detected on-time and relevant measures should be taken instantly to prevent further spread or progress of the disease. In this study, an Updated Faster R-CNN architecture developed by changing the parameters of a CNN model and a Faster R-CNN architecture for automatic detection of leaf spot disease (Cercospora beticola Sacc.) in sugar beet were proposed. The method, proposed for the detection of disease severity by imaging-based expert systems, was trained and tested with 155 images and according to the test results, the overall correct classification rate was found to be 95.48%. In addition, the proposed approach showed that changes in CNN parameters according to the image and regions to be detected could increase the success of Faster R-CNN architecture. The proposed approach yielded better outcomes for relevant parameters than the modern methods specified in previous literature. Therefore, it is believed that the method will reduce the time spent in diagnosis of sugar beet leaf spot disease in the large production areas as well as reducing the human error and time to identify the severity and course of the disease.

Suggested Citation

  • Ozguven, Mehmet Metin & Adem, Kemal, 2019. "Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    • Handle: RePEc:eee:phsmap:v:535:y:2019:i:c:s0378437119314529
    DOI: 10.1016/j.physa.2019.122537
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    References listed on IDEAS

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    1. Mehmet Metin Ozguven, 2018. "The Newest Agricultural Technologies," Current Investigations in Agriculture and Current Research, Lupine Publishers, LLC, vol. 5(1), pages 621-628, October.
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    Cited by:

    1. Iwona Jaskulska & Jarosław Kamieniarz & Dariusz Jaskulski & Maja Radziemska & Martin Brtnický, 2023. "Fungicidal Protection as Part of the Integrated Cultivation of Sugar Beet: An Assessment of the Influence on Root Yield in a Long-Term Study," Agriculture, MDPI, vol. 13(7), pages 1-10, July.
    2. Balaji Natesan & Anandakumar Singaravelan & Jia-Lien Hsu & Yi-Hsien Lin & Baiying Lei & Chuan-Ming Liu, 2022. "Channel–Spatial Segmentation Network for Classifying Leaf Diseases," Agriculture, MDPI, vol. 12(11), pages 1-20, November.
    3. Vinay Gautam & Naresh K. Trivedi & Aman Singh & Heba G. Mohamed & Irene Delgado Noya & Preet Kaur & Nitin Goyal, 2022. "A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment," Sustainability, MDPI, vol. 14(20), pages 1-19, October.

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