Hyperspectral Imaging with Cost-Sensitive Learning for High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedlings for Freeze Tolerance

Loblolly pine (Pinus taeda L.) is a commercially important timber species planted across a wide temperature gradient in the southeastern U.S. It is critical to ensure that the planting stock is suitably adapted to the growing environment to achieve high productivity and survival. Long-term field studies, although considered the most reliable method for assessing cold hardiness of loblolly pine, are extremely resource-intensive and time-consuming. The development of a high-throughput screening tool to characterize and classify freeze tolerance among different genetic entries of seedlings will facilitate accurate deployment of highly productive and well-adapted families across the landscape. This study presents a novel approach using hyperspectral imaging to screen loblolly pine seedlings for freeze tolerance. A diverse population of 1549 seedlings raised in a nursery were subjected to an artificial mid-winter freeze using a freeze chamber. A custom-assembled hyperspectral imaging system was used for in-situ scanning of the seedlings before and periodically after the freeze event, followed by visual scoring of the frozen seedlings. A hyperspectral data processing pipeline was developed to segment individual seedlings and extract the spectral data. Examination of the spectral features of the seedlings revealed reductions in chlorophylls and water concentrations in the freeze-susceptible plants. Because the majority of seedlings were freeze-stressed, leading to severe class imbalance in the hyperspectral data, a cost-sensitive learning technique that aims to optimize a class-specific cost matrix in classification schemes was proposed for modeling the imbalanced hyperspectral data, classifying the seedlings into healthy and freeze-stressed phenotypes. Cost optimization was effective for boosting the classification accuracy compared to regular modeling that assigns equal costs to individual classes. Full-spectrum, cost-optimized support vector machine (SVM) models achieved geometric classification accuracies of 75% to 78% before and within 10 days after the freeze event, and up to 96% for seedlings 41 days after the freeze event. The top portions of seedlings were more indicative of freeze events than the middle and bottom portions, leading to better classification accuracies. Further, variable selection enabled significant reductions in wavelengths while achieving even better accuracies of up to 97% than full-spectrum SVM modeling. This study demonstrates that hyperspectral imaging can provide tree breeders with a valuable tool for improved efficiency and objectivity in the characterization and screening of freeze tolerance for loblolly pine

Terpenes associated with resistance against the gall wasp, Leptocybe invasa, in Eucalyptus grandis

Leptocybe invasa is an insect pest causing gall formation on oviposited shoot tips and leaves of Eucalyptus trees leading to leaf deformation, stunting, and death in severe cases. We previously observed different constitutive and induced terpenes, plant specialized metabolites that may act as attractants or repellents to insects, in a resistant and susceptible clone of Eucalyptus challenged with L.invasa. We tested the hypothesis that specific terpenes are associated with pest resistance in a Eucalyptus grandis half-sib population. Insect damage was scored over 2 infestation cycles, and leaves were harvested for near-infrared reflectance (NIR) and terpene measurements. We used Bayesian model averaging for terpene selection and obtained partial least squares NIR models to predict terpene content and L.invasa infestation damage. In our optimal model, 29% of the phenotypic variation could be explained by 7 terpenes, and the monoterpene combination, limonene, -terpineol, and 1,8-cineole, could be predicted with an NIR prediction ability of .67. Bayesian model averaging supported -pinene, -terpinene, and iso-pinocarveol as important for predicting L.invasa infestation. Susceptibility was associated with increased -terpinene and -pinene, which may act as a pest attractant, whereas reduced susceptibility was associated with iso-pinocarveol, which may act to recruit parasitoids or have direct toxic effectsDepartment of Science and Technology Eucalyptus; National Research Foundation, Grant/ Award Number: 8966

Terpenes associated with resistance against the gall wasp, Leptocybe invasa, in Eucalyptus grandis

Leptocybe invasa is an insect pest causing gall formation on oviposited shoot tips and leaves of Eucalyptus trees leading to leaf deformation, stunting, and death in severe cases. We previously observed different constitutive and induced terpenes, plant specialized metabolites that may act as attractants or repellents to insects, in a resistant and susceptible clone of Eucalyptus challenged with L. invasa. We tested the hypothesis that specific terpenes are associated with pest resistance in a Eucalyptus grandis half‐sib population. Insect damage was scored over 2 infestation cycles, and leaves were harvested for near‐infrared reflectance (NIR) and terpene measurements. We used Bayesian model averaging for terpene selection and obtained partial least squares NIR models to predict terpene content and L. invasa infestation damage. In our optimal model, 29% of the phenotypic variation could be explained by 7 terpenes, and the monoterpene combination, limonene, α‐terpineol, and 1,8‐cineole, could be predicted with an NIR prediction ability of .67. Bayesian model averaging supported α‐pinene, γ‐terpinene, and iso‐pinocarveol as important for predicting L. invasa infestation. Susceptibility was associated with increased γ‐terpinene and α‐pinene, which may act as a pest attractant, whereas reduced susceptibility was associated with iso‐pinocarveol, which may act to recruit parasitoids or have direct toxic effects.Supplementary Table 1. Environmental and phenotype data (full population) for the three Eucalyptus grandis sites surveyed for Leptocybe invasa infestation. [Excel file]Supplementary Table 2. Predictor variable datasets and outlier detection prior to partial least squares modeling. (A) Predictor variable datasets used for outlier detection and partial least squares modeling. (B) The proportion of samples classified as outliers (and thus trimmed) for each set of models, prior to partial least squares modeling. [Excel file]Supplementary Table 3. The 48 measured terpenes. (A) The name, major ions and retention time of the 48 measured terpenes. (B) The motivation for combining groups of terpenes. (C) The correlation of terpenes with the Leptocybe invasa screenings (LS1, LS2) and individual breeding values (IBV) for the Siya Qubeka (SQF) site. [Excel fie]Supplementary Table 4. Leptocybe invasa heritability estimates for L. invasa screening 1 (LS1) and L. invasa screening 2 (LS2) for the Eucalyptus grandis population across sites. [Excel file]Supplementary Table 5. Summary of the best partial least squares models, based on near-infrared reflectance (NIR) data, for Leptocybe invasa screenings (LS1, LS2) and individual breeding values (IBV) at the Mtunzini (MTZ) and Nyalazi (NYL) sites. [Excel file] Supplementary Table 6. Bayesian model selection results to identify the most important terpenes for predicting Leptocybe invasa infestation. (A) Bayesian model selection results at the Siya Qubeka (SQF) site. (B) Bayesian model selection results at the Mtunzini (MTZ) site. (C) Bayesian model selection results at the Nyalazi (NYL) site. (D) Bayesian model selection results across all three sites. [Excel file]The National Research Foundation (NRF) South Africa Bioinformatics and Functional Genomics Programme (Grant ID 89669) and the Department of Science and Technology Eucalyptus genomics platform grant.https://wileyonlinelibrary.com/journal/pce2019-08-01hj2018Forestry and Agricultural Biotechnology Institute (FABI)Genetic

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

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

Prediction of Freeze Damage and Minimum Winter Temperature of the Seed Source of Loblolly Pine Seedlings Using Hyperspectral Imaging

The most important climatic variable influencing growth and survival of loblolly pine is the yearly average minimum winter temperature (MWT) at the seed source origin, and it is used to guide the transfer of improved seed lots throughout the species’ distribution. This study presents a novel approach for the assessment of freeze-induced damage and prediction of MWT at seed source origin of loblolly pine seedlings using hyperspectral imaging. A population comprising 98 seed lots representing a wide range of MWT at seed source origin was subjected to an artificial freeze event. The visual assessment of freeze damage and MWT were evaluated at the family level and modeled with hyperspectral image data combined with chemometric techniques. Hyperspectral scanning of the seedlings was conducted prior to the freeze event and on four occasions periodically after the freeze. A significant relationship (R2 = 0.33; p \u3c .001) between freeze damage and MWT was observed. Prediction accuracies of freeze damage and MWT based on hyperspectral data varied among seedling portions (full-length, top, middle, and bottom portion of aboveground material) and scanning dates. Models based on the top portion were the most predictive of both freeze damage and MWT. The highest prediction accuracy of MWT [RPD (ratio of prediction to deviation) = 2.12, R2 = 0.78] was achieved using hyperspectral data obtained prior to the freeze event. Adoption of this assessment method would greatly facilitate the characterization and deployment of well-adapted loblolly pine families across the landscape