Data from: A scalable, low-cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations
This data set contains images of potato tubers from clones in the A08241 F1 breeding population grown in Aberdeen, Idaho during the 2019 field season.
CameraTrapDetectoR Pig Model
This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR pig-only model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts wild pigs in camera trap images. It also includes categories for non-pig detections (animal, human, or vehicle), and a background class.
CameraTrapDetectoR General Model
This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR general model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts mammals and birds in camera trap images, and includes categories for humans, vehicles, and a background class.
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CameraTrapDetectoR Family Model
This dataset contains the model weights, architecture, and class label dictionary for CameraTrapDetectoR family model version 1. The model architecture is a FasterRCNN object detection model with a ResNet50 backbone. This model identifies and counts animals from 33 North American taxonomic families in camera trap images, including humans vehicles and a background class.
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CameraTrapDetectoR Species Model
CameraTrapDetectoR is an R package that uses deep learning computer vision models to automatically detect, count, and classify common North American domestic and wild species in camera trap images. Data for all versions of the taxonomic species model are located in this dataset. This dataset contains model weights, architectures, and class label dictionaries for the CameraTrapDetectoR species models.
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PhenoCam images from ARSLTARMDCR site, Caroline County, Maryland, USA since 2017
This data set consists of repeat digital imagery from a tower-mounted digital camera (hereafter, PhenoCam) maintained by the USDA-ARS Hydrology Remote Sensing Laboratory (HRSL) in the Lower Chesapeake Bay (LCB) watershed. HRSL is a member of the PhenoCam network, which has as its mission to serve as a long-term, continental-scale, phenological observatory. Imagery is uploaded to the PhenoCam server every 30 minutes.
PhenoCam images from ARSOPE3LTAR site, Beltsville Agricultural Research Center, Maryland, USA since 2017
This data set consists of repeat digital imagery from a tower-mounted digital camera (hereafter, PhenoCam) maintained by the USDA-ARS Hydrology Remote Sensing Laboratory (HRSL) in the Lower Chesapeake Bay (LCB) watershed. HRSL is a member of the PhenoCam network, which has as its mission to serve as a long-term, continental-scale, phenological observatory. Imagery is uploaded to the PhenoCam server every 30 minutes.
Data from: Efficient imaging and computer vision detection of two cell shapes in young cotton fibers
Data presented show the AI-assisted visualization of multiple cotton fiber tips in one image. Cotton fiber sample preparation, digital image collection, and image analysis are described, including test images, training/validation sets, and annotations.
Data from: Solving the Robot-World Hand-Eye(s) Calibration Problem with Iterative Methods
These datasets were generated for calibrating robot-camera systems and to develop and test new robot-world, hand-eye calibration methods.