Abstract
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.
Highlights
This article is an open access articlePinus taeda L., commonly known as loblolly pine, is the most important forest tree species in the southern United States and is grown for timber, construction lumber, plywood, and pulpwood
We present a deep learning-based approach for the segmentation of specific regions of interest in Vis–NIR images of loblolly pine seedlings followed by a study in disease discrimination using hyperspectral data extracted from these ROIs
The segmentation of ROIs based on the features extracted from one or more image channels or their combinations is a common technique that has been used in studies using hyperspectral imaging
Summary
This article is an open access articlePinus taeda L., commonly known as loblolly pine, is the most important forest tree species in the southern United States and is grown for timber, construction lumber, plywood, and pulpwood. The fungus typically infects the stem of a young tree, leading to the creation of tumor-like growths known as “rust galls”. Segmentation of plant pixels was carried out by thresholding a normalized difference vegetation index (NDVI) image derived using the two image channels at 705 nm and 750 nm corresponding to the red edge and the near-infrared regions, respectively. This ratio is referred to as ND705 in the literature; the mathematical relationship used for the calculation is shown in Equation (1) [22,23].
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