The National Agriculture Imagery Program (NAIP) acquires imagery during the agricultural growing seasons in the continental U.S. A primary goal of the NAIP program is to enable availability of digital orthophotography within a year of acquisition.
Recreation Information Database (RIDB) Application Programming Interface (API) is provided for the purpose of accessing RIDB API endpoints which contain information for federal recreation areas, facilities, campsites, tours, and permits.
An easy-to-customize, low-cost, low disturbance, motorized, and adjustable proximal sensing cart for field-based high-throughput phenotyping is described. General dimensions, motor specifications, and a remote operation application are given. The cart, named "Professor", supports mounting multiple proximal sensors and cameras for characterizing plant traits grown under field conditions.
This site provides free access to Iowa geographic map data through an on-line map viewer and through Web Map Service (WMS) connections for GIS. The site was developed by the Iowa State University Geographic Information Systems Support and Research Facility in cooperation with the Iowa Department of Natural Resources, the USDA Natural Resources Conservation Service, and the Massachusetts Institute of Technology. This site was first launched in March 1999.
This dataset contains GPP from Vegetation Photosynthesis Model (VPM) at two spatial resolution (0.05 and 0.5 degree) and three temporal resolution (8-day, monthly, annual). The units for the three temporal resolution are g C m-2 day-1, g C m-2 month-1, g C m-2 year-1, with no conversion factor.
Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.
This dataset consists of four sets of flower images, from three different fruit tree species: apple, peach, and pear, and accompanying ground truth images. This data is provided to support a paper as well as to provide labeled data to the community for the development of new algorithms and models for object detection.
Data from: Forest harvest dataset for northern Colorado Rocky Mountains (1984–2015) generated from a Landsat time series and existing forest harvest records
This dataset provides a shapefile containing approximately 3500 polygons with the location, extent, size, and year of clearcut harvest events occurring between 1984 and 2015 in forested areas of northern Colorado.
Data from: Data on morphological features of mycosis induced by Colletotrichum nymphaeae and Lecanicillium longisporum on citrus orthezia scale
Symptoms of mycosis induced by two native fungal entomopathogens of the citrus orthezia scale, Praelongorthezia praelonga (Hemiptera: Ortheziidae), an important pest of citrus orchards, are described.
A joint project of The University of Georgia - Warnell School of Forestry and Natural Resources, College of Agricultural and Environmental Sciences - Department of Entomology, Center for Invasive Species and Ecosystem Health, Georgia Museum of Natural History, The Entomology Society of America and USDA Identification Technology Program, Insect Images image categories include: Insect Orders: Hymenoptera; Coleoptera; Hemiptera; Lepidoptera; Blattodea; Odonata; Dermaptera; Diptera; Orthoptera; Neuroptera; Phthiraptera; Mantodea; Thysanura; Isoptera; Thysanoptera; Phasmatoptera; and Related Organisms.