Mask R-CNN Model for Banana Diseases Segmentation

Christian A. Elinisa () and Neema Mduma
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Christian A. Elinisa: School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology
Neema Mduma: School of Computational and Communication Science and Engineering, The Nelson Mandela African Institution of Science and Technology

A chapter in Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, 2024, pp 227-237 from Springer

Abstract: Abstract Early detection of banana diseases is necessary for developing an effective control plan and minimizing quality and financial losses. Fusarium Wilt Race 1 and Black Sigatoka diseases are among the most harmful banana diseases globally. In this study, we propose a model based on the Mask R-CNN architecture to effectively segment the damage of these two banana diseases. We also include a CNN model for classifying these diseases. We used an image dataset of 6000 banana leaves and stalks collected in the field. In our experiment, Mask R-CNN achieved a mean average precision of 0.04529, while the CNN model achieved an accuracy of 96.75%. The Mask R-CNN model was able to accurately segment areas where the banana leaves and stalk were affected by Black Sigatoka and Fusarium Wilt Race 1 diseases in the image dataset. This model can assist farmers to take the required measures for early control and minimize the harmful effects of these diseases and rescue their yields.

Keywords: Instance segmentation; Convolutional Neural Networks; Banana disease detection; Mask R-CNN; Fusarium Wilt Race 1; Black Sigatoka; Deep learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-56576-2_20

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DOI: 10.1007/978-3-031-56576-2_20

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