<i>High Throughput Phenotyping for Fusiform Rust Disease Resistance in Loblolly Pine Using Hyperspectral Imaging</i>

In an extensive series of trials with open-pollinated families of loblolly pine (Pinus taeda L.), resistance to fusiform rust disease (caused by Cronartium quercuum (Berk.) Miyabe ex Shirai f. sp. fusiforme) at individual test sites was relatively unpredictable for the families deemed most resistant. The most resistant families were also the most stable for performance across test sites, with stability defined as the slope of the regression of family means for rust infection versus site means for rust infection. A family's R-50 value (its predicted rust infection level when the site mean infection is 50%) was correlated to its stability parameter or slope (r = 0.78). On average, any one family's level of infection (% galled) was reasonably predictable for any given infection level at a given site; the average coefficient of determination (r2) was 0.78 for the regression of family means for rust infection versus site means for rust infection. However, the six most stable families for resistance had the lowest r2 values (average r2 = 0.58). We speculated that the lower predictability for the most resistant families was due to interactions of specific resistance genes in these families and corresponding avirulence and (or) virulence levels in the pathogen populations that may differ among sites. Although the predictability of the individual resistant families was relatively low, if these families were bulked into a resistant seed lot, they performed in a more predictable manner with r2 = 0.74 for the regression of the bulk mean versus site means. Bulks of four to six highly resistant families appeared to be a good solution to obtain stable and predictable performance across a range of sites.

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Correspondence of Loblolly Pine Response for Fusiform Rust Disease from Local and Wide-Ranging Tests in the Southern United States

Forest Science ◽

10.5849/fs-2016-093r1 ◽

2017 ◽

Vol 63 (5) ◽

pp. 496-503

Author(s):

Jesse Spitzer ◽

Fikret Isik ◽

Ross W. Whetten ◽

Alfredo E. Farjat ◽

Steven E. McKeand

Keyword(s):

United States ◽

Loblolly Pine ◽

Southern United States ◽

Rust Disease ◽

Fusiform Rust

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Loblolly Pine Seed Sources Differ in Susceptibility to the Southern Pine Beetle in South Carolina

Southern Journal of Applied Forestry ◽

10.1093/sjaf/16.4.169 ◽

1992 ◽

Vol 16 (4) ◽

pp. 169-174 ◽

Cited By ~ 1

Author(s):

H. R. Powers ◽

R. P. Belanger ◽

W. D. Pepper ◽

F. L. Hastings

Keyword(s):

South Carolina ◽

Disease Resistance ◽

Loblolly Pine ◽

Slash Pine ◽

Southern Pine Beetle ◽

Southern Pine ◽

Fusiform Rust ◽

Seed Sources ◽

Pine Beetle ◽

Stand Characteristics

Abstract In a planting near Aiken, SC, loblolly pine saplings from an eastern seed source were significantly more susceptible to the southern pine beetle (SPB) than were loblolly saplings from western seed sources. Two eastern sources of slash pine also resisted beetle attack. Study plots wereoriginally established to evaluate disease resistance and growth of fusiform rust resistant and susceptible seed lots. There was no relationship between stand characteristics or rust infection patterns and SPB damage. South. J. Appl. For. 16(4):169-174

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Performance of Livingston Parish Loblolly Pine in the Georgia Piedmont

Southern Journal of Applied Forestry ◽

10.1093/sjaf/10.2.84 ◽

1986 ◽

Vol 10 (2) ◽

pp. 84-87

Author(s):

H. R. Powers

Keyword(s):

Pinus Taeda ◽

Loblolly Pine ◽

Coastal Plain ◽

Source Material ◽

Atlantic Coastal Plain ◽

Rust Disease ◽

Fusiform Rust ◽

Pinus Taeda L ◽

Georgia Piedmont ◽

Atlantic Coastal

Abstract Seedlings of Livingston Parish (Louisiana) loblolly pine (Pinus taeda L.) have been widely used across the Gulf and south Atlantic Coastal Plain to reduce the damage caused by the fusiform rust disease. Since this seed-source material provided the first rust-resistant seedlings available to forestland managers, it was used wherever rust damage was heavy, in some cases into the Piedmont north of the recommended area of planting. This paper evaluates the performance of ten-year-old Livingston Parish trees in such an area. The rust resistance of the Livingston Parish trees was outstanding, with 83% being free of disease as compared with only 14% of the commercial controls. There was no difference in growth between the two groups of trees, and ice breakage was not significantly greater in the Livingston Parish trees. South. J. Appl. For. 10:84-87, May 1986.

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Corrigendum: High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response

Journal of Experimental Botany ◽

10.1093/jxb/erab126 ◽

2021 ◽

Author(s):

Bikram P Banerjee ◽

Sameer Joshi ◽

Emily Thoday-Kennedy ◽

Raj K Pasam ◽

Josquin Tibbits ◽

...

Keyword(s):

Hyperspectral Imaging ◽

High Throughput ◽

Nitrogen Response ◽

High Throughput Phenotyping

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

Remote Sensing ◽

10.3390/rs13183595 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3595

Author(s):

Piyush Pandey ◽

Kitt G. Payn ◽

Yuzhen Lu ◽

Austin J. Heine ◽

Trevor D. Walker ◽

...

Keyword(s):

Spectral Data ◽

Hyperspectral Imaging ◽

Loblolly Pine ◽

Disease Incidence ◽

Fungal Spores ◽

The United States ◽

Classification Model ◽

Support Vector ◽

Timber Species ◽

Fusiform Rust

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.

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