Dr. Megan M. Dewdney Profile

Dr. Megan M. Dewdney

at Univ of Florida

SPIE Involvement:

Author

Publications (4)

Proceedings Article | 7 June 2024 Presentation + Paper

Classifying adaxial and abaxial sides of diseased citrus leaves with selected hyperspectral bands and YOLOv8

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

Proceedings Volume 13053, 130530C (2024) https://doi.org/10.1117/12.3021207

KEYWORDS: Diseases and disorders, Cameras, Principal component analysis, Reflectivity, Inspection, Spatial resolution, Hyperspectral imaging, Image classification

Read Abstract +

Proceedings Article | 6 June 2024 Presentation + Paper

Detection of citrus black spot fungus using fluorescence imaging and deep learning on leaf surface

Pappu Kumar Yadav, Thomas Burks, Jianwei Qin, Moon Kim, Megan Dewdney, Mark Ritenour, Fartash Vasefi

Proceedings Volume 13060, 1306003 (2024) https://doi.org/10.1117/12.3013510

KEYWORDS: Fluorescence imaging, Education and training, Diseases and disorders, Deep learning, Imaging systems, Light emitting diodes, Fluorescence, Organization management, Matrices, Agriculture

Read Abstract +

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

Read Abstract +

DOWNLOAD PAPER

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

Read Abstract +

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.

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years