Dr. Megan M. Dewdney Profile

Dr. Megan M. Dewdney

at Univ of Florida

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Publications (2)

SPIE Journal Paper | 8 February 2024 Open Access

Automated classification of citrus disease on fruits and leaves using convolutional neural network generated features from hyperspectral images and machine learning classifiers

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan M. Dewdney, Mark A. Ritenour

JARS, Vol. 18, Issue 01, 014512, (February 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.014512

KEYWORDS: Diseases and disorders, Education and training, Image classification, Reflectivity, Matrices, Classification systems, Principal component analysis, Hyperspectral imaging, Cameras, Machine learning

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Proceedings Article | 13 June 2023 Presentation + Paper

Citrus disease classification with convolution neural network generated features and machine learning classifiers on hyperspectral image data

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Quentin Frederick, Megan Dewdney, Mark Ritenour

Proceedings Volume 12539, 1253902 (2023) https://doi.org/10.1117/12.2665768

KEYWORDS: Diseases and disorders, Image classification, Reflectivity, Principal component analysis, Convolution, Classification systems, Hyperspectral imaging, Imaging systems, Deep learning, Hyperspectral systems

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Citrus black spot (CBS) is a quarantine fungal disease caused by Phyllosticta citricarpa that can limit market access for fruit. It causes lesions on fruit surfaces and may lead to premature fruit drops, reducing yield. Leaf symptoms are uncommon for CBS, although the fungus reproduces in leaf litter. Similarly, citrus canker is another serious disease caused by the bacterium Xanthomonas citri subsp. citri (syn. X. axonopodis pv. citri) and leads to economic losses for growers from fruit drops and blemishes. Therefore, early detection and management of groves infected by CBS or canker via fruit and/or leaf inspection can benefit the Florida citrus industry. Manual inspection to classify disease symptoms on either fruits or leaves is a tedious and labor intensive process. Hence, there is need to develop computer vision system for autonomous classification of fruits and leaves that can speed up their management in fields. In this paper, we demonstrate the capability of convolution neural network (CNN)-based deep learning along with classical machine learning (ML) based computer vision algorithms to classify ‘Valencia’ orange fruit surfaces with CBS infection along with four other conditions and ‘Furr’ mandarin leaves with canker and four other conditions. Fruits with CBS and four other conditions (marketable, greasy spot, melanose and wind scar) were classified using a custom shallow CNN with SoftMax and RBF SVM at an overall accuracy of 89.8% and 92.1%, respectively. Similarly, a custom VGG16 network with SoftMax could classify canker leaves with F1-score of 85% and overall accuracy of 82% including other four conditions (control/healthy, greasy spot, melanose and scab). In addition, it was found that by replacing SoftMax with RBF SVM in the VGG16 network, the overall classification accuracy improved to 93% i.e., an increment of 11% points (13.41%). The preliminary findings reported in this paper demonstrate the capability of HSI system for automated citrus fruit and leaf disease classification using shallow and deep CNN generated features and ML classifiers.

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