2024

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Author: search.filters.author.Jeon, MHas files: Yes

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    PublicationOpen Access
    Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Kalupahana, D; Kahatapitiya, N.S; Silva, B.N; Kim, J; Jeon, M; Wijenayake, U; Wijesinghe, R. E
    Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies. © 2024 by the authors.
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    PublicationEmbargo
    Comparative quantifications and morphological monitoring of the topical treatment approach for onychomycosis-affected in vivo toenail using optical coherence tomography: A case study
    (Elsevier Ltd, 2024-02) Saleah, S.S; Gu, Y; Wijesinghe, R.E; Seong, D; Cho, H; Jeon, M; Kim, J
    Onychomycosis is one of the most common toenail fungal infections that affect the quality of life of many patients. Long-term and noninvasive monitoring of morphological changes of onychomycosis-affected nail plate aids the medication process and provides comfort for patients. However, existing medical and dermatological imaging methods have various types of limitations in nail investigation due to low resolution, lack of volumetric data, the necessity of highly trained personnel for image analysis, and the variety of protocols. In this study, qualitative monitoring-based quantitative assessments were performed to assess the morphological changes of onychomycosis-affected toenail for 15 consecutive weeks using high-resolution optical coherence tomography (OCT). Layer intensity and surface roughness measuring algorithms were applied to two-dimensional OCT cross-sectional images to detect gradual changes in the morphological structure of the diseased toenail. A depth intensity profile and the angle formed between the nail plate and nail fold were also used to analyze the thickness and shape of the toenail plates, respectively. The quantitative and morphological monitoring results revealed significant changes in the toenail structure before and during the treatment process, confirming the healing of the diseased toenail. Therefore, the proposed noninvasive optical analysis approach can be applied to monitor nail abnormalities and evaluate the process of diseased toenail medication
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    PublicationEmbargo
    Non-destructive morphological screening for the assessment of postharvest storage effect on pears stored with apples using optical coherence tomography
    (Elsevier GmbH, 2024-04) Luna, J.A; Wijesinghe, R.E; Lee, S.Y; Ravichandran, N.K; Saleah, S.A; Seong, D; Jung, H.Y; Jeon, M; Kim, J
    The use of a limited and inadequate storage facility for the storage of multiple food items for an extended period of time results in the loss of structural integrity and freshness while storing fruit in confined single storage without adequate individual packaging methods can result in morphological changes and the degradation of the quality of the fruit. In this study, the effects of postharvest storage on pears co-stored with apples were investigated via non-invasive screening of the structural deformation of pears and the respective anatomical changes of the sub-surface. The anatomical changes were monitored for a prolonged time (12 d) under inadequate and confined storage conditions using swept-source optical coherence tomography (SS-OCT) and the results were comparatively analyzed using appropriately stored specimens. In addition, the OCT cross-sectional images were analyzed for the assessment of the dispersed intensity profile using a customized intensity-based image-processing algorithm. The results revealed the internal morphological variations and corresponding intensity fluctuations, thickness variations, and internal gap formations. This confirmed the potential applicability of OCT as a real-time, non-invasive high-resolution assessment technique for determining fruit quality in diverse environments, such as post-harvest storage and transportation systems.