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Identifying Optimal Wavelengths as Disease Signatures Using Hyperspectral Sensor and Machine Learning
Article

Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings

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Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA
2
Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC 27695, USA
3
Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Thomas Udelhoven
Remote Sens. 2021, 13(18), 3595; https://doi.org/10.3390/rs13183595
Received: 21 July 2021 / Revised: 31 August 2021 / Accepted: 3 September 2021 / Published: 9 September 2021
(This article belongs to the Special Issue Plant Phenotyping for Disease Detection)
Loblolly pine is an economically important timber species in the United States, with almost 1 billion seedlings produced annually. The most significant disease affecting this species is fusiform rust, caused by Cronartium quercuum f. sp. fusiforme. Testing for disease resistance in the greenhouse involves artificial inoculation of seedlings followed by visual inspection for disease incidence. An automated, high-throughput phenotyping method could improve both the efficiency and accuracy of the disease screening process. This study investigates the use of hyperspectral imaging for the detection of diseased seedlings. A nursery trial comprising families with known in-field rust resistance data was conducted, and the seedlings were artificially inoculated with fungal spores. Hyperspectral images in the visible and near-infrared region (400–1000 nm) were collected six months after inoculation. The disease incidence was scored with traditional methods based on the presence or absence of visible stem galls. The seedlings were segmented from the background by thresholding normalized difference vegetation index (NDVI) images, and the delineation of individual seedlings was achieved through object detection using the Faster RCNN model. Plant parts were subsequently segmented using the DeepLabv3+ model. The trained DeepLabv3+ model for semantic segmentation achieved a pixel accuracy of 0.76 and a mean Intersection over Union (mIoU) of 0.62. Crown pixels were segmented using geometric features. Support vector machine discrimination models were built for classifying the plants into diseased and non-diseased classes based on spectral data, and balanced accuracy values were calculated for the comparison of model performance. Averaged spectra from the whole plant (balanced accuracy = 61%), the crown (61%), the top half of the stem (77%), and the bottom half of the stem (62%) were used. A classification model built using the spectral data from the top half of the stem was found to be the most accurate, and resulted in an area under the receiver operating characteristic curve (AUC) of 0.83. View Full-Text
Keywords: plant imaging; computer vision; forestry; disease discrimination; hyperspectral imaging; plant phenotyping; machine learning plant imaging; computer vision; forestry; disease discrimination; hyperspectral imaging; plant phenotyping; machine learning
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MDPI and ACS Style

Pandey, P.; Payn, K.G.; Lu, Y.; Heine, A.J.; Walker, T.D.; Acosta, J.J.; Young, S. Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings. Remote Sens. 2021, 13, 3595. https://doi.org/10.3390/rs13183595

AMA Style

Pandey P, Payn KG, Lu Y, Heine AJ, Walker TD, Acosta JJ, Young S. Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings. Remote Sensing. 2021; 13(18):3595. https://doi.org/10.3390/rs13183595

Chicago/Turabian Style

Pandey, Piyush, Kitt G. Payn, Yuzhen Lu, Austin J. Heine, Trevor D. Walker, Juan J. Acosta, and Sierra Young. 2021. "Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings" Remote Sensing 13, no. 18: 3595. https://doi.org/10.3390/rs13183595

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