@article{pandey_veazie_whipker_young_2023, title={Predicting foliar nutrient concentrations and nutrient deficiencies of hydroponic lettuce using hyperspectral imaging}, volume={230}, ISSN={["1537-5129"]}, url={https://doi.org/10.1016/j.biosystemseng.2023.05.005}, DOI={10.1016/j.biosystemseng.2023.05.005}, abstractNote={Effective management of plant essential nutrients is necessary for hydroponically grown lettuce to achieve high yields and maintain production. This study investigated in situ hyperspectral imaging of hydroponic lettuce for predicting nutrient concentrations and identifying nutrient deficiencies for: nitrogen (N), phosphorous (P), potassium (K), calcium (Ca), magnesium (Mg), and sulphur (S). A greenhouse study was conducted using 'Salanova Green' lettuce grown with controlled solution treatments with varying macronutrient fertility rates of 0, 8, 16, 32, 64, and 100% each for N, P, K, Ca, Mg, and S. Plants were imaged using a hyperspectral line scanner at six and eight weeks after transplanting; then, plant tissues were sampled, and nutrient concentrations measured. Partial least squares regression (PLSR) models were developed to predict nutrient concentrations for each nutrient individually (PLS1) and for all six nutrient concentrations (PLS2). Several binary classification models were also developed to predict nutrient deficiencies. The PLS1 and PLS2 models predicted nutrient concentrations with Coefficient of Determination (R2) values from 0.60 to 0.88 for N, P, K, and S, while results for Ca and Mg yielded R2 values of 0.12–0.34, for both harvest dates. Similarly, plants deficient in N, P, K, and S were classified more accurately compared to plants deficient in Ca and Mg for both harvest dates, with F1 values (F-scores) ranging from 0.71 to 1.00, with the exception of K which had F1 scores of 0.40–0.67. Overall, results indicate that both leaf tissue nutrient concentration and nutrient deficiencies can be predicted using hyperspectral data collected for whole plants.}, journal={BIOSYSTEMS ENGINEERING}, author={Pandey, Piyush and Veazie, Patrick and Whipker, Brian and Young, Sierra}, year={2023}, month={Jun}, pages={458–469} }
 @article{veazie_pandey_young_ballance_hicks_whipker_2022, title={Impact of Macronutrient Fertility on Mineral Uptake and Growth of Lactuca sativa 'Salanova Green' in a Hydroponic System}, volume={8}, ISSN={["2311-7524"]}, url={https://doi.org/10.3390/horticulturae8111075}, DOI={10.3390/horticulturae8111075}, abstractNote={Lactuca sativa (commonly referred to as lettuce) is one of the most popular grown hydroponic crops. While other fertilizer rate work has been conducted on lettuce, the impact of each element has not been evaluated independently or by determining adequate foliar tissue concentrations when all nutrients are plant-available. This study explores the impact that macronutrients have on the growth and yield of lettuce at different stages of the production cycle. Additionally, this study explores the adequate nutrient rates by regressing nutrient curves to find the concentration of each element that corresponds to optimal growth. Plants were grown under varying macronutrient concentrations (0, 8, 16, 32, 64, and 100%) utilizing the concentrations of a modified Hoagland’s solution based on 150 mg·L−1 N. Lettuce plants were grown in a silica sand culture and received a nutrient solution in which a single element was altered. Visual symptomology was documented, and leaf tissue mineral nutrient concentrations and biomass were measured at Weeks 3, 6, and 8 after transplant. Optimal elemental leaf tissue concentration and biomass varied by macronutrient rates and weeks of growth. Nitrogen rate produced a linear increase in total plant dry weight, but foliar N followed a quadratic plateau pattern. Other elements, such as phosphorus, potassium, and magnesium, produced distinct total plant dry weight plateaus despite increasing fertility concentrations. These results demonstrate that fertility recommendation can be lowered for nutrients where higher rates do not result in higher plant biomass or foliar nutrient concentrations.}, number={11}, journal={HORTICULTURAE}, author={Veazie, Patrick and Pandey, Piyush and Young, Sierra and Ballance, M. Seth and Hicks, Kristin and Whipker, Brian}, year={2022}, month={Nov} }
 @article{lu_payn_pandey_acosta_heine_walker_young_2021, title={HYPERSPECTRAL IMAGING WITH COST-SENSITIVE LEARNING FOR HIGH-THROUGHPUT SCREENING OF LOBLOLLY PINE (PINUS TAEDA L.) SEEDLINGS FOR FREEZE TOLERANCE}, volume={64}, ISSN={["2151-0040"]}, url={http://dx.doi.org/10.13031/trans.14708}, DOI={10.13031/trans.14708}, abstractNote={HighlightsA hyperspectral imaging approach was developed for freeze-tolerance phenotyping of loblolly pine seedlings.Image acquisition was conducted before and periodically after artificial freezing of the seedlings.A hyperspectral data processing pipeline was developed to extract the spectra from seedling segments.Cost-sensitive support vector machine (SVM) was used for classifying stressed and healthy seedlings.Post-freeze scanning of seedlings on day 41 achieved the highest screening accuracy of 97%.Abstract. 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. Keywords: Cost-sensitive learning, Freeze tolerance, Hyperspectral imaging, Plant phenotyping, Support vector machine.}, number={6}, journal={TRANSACTIONS OF THE ASABE}, publisher={American Society of Agricultural and Biological Engineers (ASABE)}, author={Lu, Yuzhen and Payn, Kitt G. and Pandey, Piyush and Acosta, Juan J. and Heine, Austin J. and Walker, Trevor D. and Young, Sierra}, year={2021}, pages={2045–2059} }
 @article{pandey_payn_lu_heine_walker_acosta_young_2021, title={Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings}, volume={13}, ISSN={["2072-4292"]}, url={https://doi.org/10.3390/rs13183595}, DOI={10.3390/rs13183595}, abstractNote={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.}, number={18}, journal={REMOTE SENSING}, publisher={MDPI AG}, author={Pandey, Piyush and Payn, Kitt G. and Lu, Yuzhen and Heine, Austin J. and Walker, Trevor D. and Acosta, Juan J. and Young, Sierra}, year={2021}, month={Sep} }
 @article{lu_walker_acosta_young_pandey_heine_payn_2021, title={Prediction of Freeze Damage and Minimum Winter Temperature of the Seed Source of Loblolly Pine Seedlings Using Hyperspectral Imaging}, volume={67}, ISSN={["1938-3738"]}, url={https://doi.org/10.1093/forsci/fxab003}, DOI={10.1093/forsci/fxab003}, abstractNote={Abstract
               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 < .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.}, number={3}, journal={FOREST SCIENCE}, publisher={Oxford University Press (OUP)}, author={Lu, Yuzhen and Walker, Trevor D. and Acosta, Juan J. and Young, Sierra and Pandey, Piyush and Heine, Austin J. and Payn, Kitt G.}, year={2021}, month={Jun}, pages={321–334} }