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.

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.

Using Unoccupied Aerial Systems (UASs) to collect image data for field phenotyping enables the coverage of large spatial scales, but the extraction of phenotypes at the level of an individual plant remains a challenge. Phenotypes are generally extracted at the plot level after manual delineation of the plots in an orthomosaic image. However, this approach is impractical when individual plant phenotypes are of interest, such as in mutant screenings, heterozygous population trials, and other cases where individual plants in a plot or a landscape may be very diverse. For mutant studies in particular, UASs have had limited utility since one typically wants to identify individual plants with outlier phenotypes. Current mutant population screenings require scientists to walk through the field on a regular basis to manually identify and record plants of interest. If UAS software could be designed to identify these plants, much larger studies could be conducted with lower labor costs. Here, we use deep Convolutional Neural Network (CNN) models to detect individual maize plants in raw UAS images followed by the extraction of individual phenotypes. The predictions from the CNN models are used to identify plants in the orthomosaic image to derive information on plot location. For the extraction of phenotypes, plant pixels are segmented from the background and traits including color-based indices and canopy coverage are calculated. Individual plant height is derived from the corresponding Digital Surface Model (DSM). This project will contribute to a more efficient approach for the phenotyping of individual plants using UAS images.