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Genomes To Fields 2014

    Phenotypic, genotypic, and environment data for the 2014 field season: The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Carolyn_Lawrence_Dill_G2F_Nov_2016_V.3).

    Genomes To Fields (G2F) Inbred Ear Imaging Data 2017

      A subset of ~30 inbreds were evaluated in 2014 and 2015 to develop an image based ear phenotyping tool. The data is stored in [CyVerse](http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017).

      Maize-GAMER: Maize B73 RefGen_v3 5b+

        This dataset from maize-GAMER is a new high-coverage and reproducible functional annotation of maize (*Zea mays*) protein coding genes based on Gene Ontology (GO) term assignments that covers all genes in the B73 RefGen_v3 5b+ set. Data are compressed gzip (.gz) files.

        USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013

          The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (*Zea mays* L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information.

          Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) Simulation Model

            The Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model simulates crop growth, competition, light interception by leaves, biomass accumulation, partitioning of biomass into grain, water use, nutrient uptake, and growth constraints such as water, temperature, and nutrient stress. Plant development is temperature driven, with duration of growth stages dependent on degree days. Each plant species has a defined base temperature and optimum temperature.

            USDA Plants Database API in R

              The USDA maintains a database of plant information - [USDA Plants Database](http://plants.usda.gov/java/) - containing trait data, some of its life history. This resource is an independently created RESTful API for that data. The API, and open issues for bugs/feature requests can be found in the GitHub repository. This tool can be used from the command line, R, Ruby, Python, a browser, etc.

              NLET - National Load Estimating Tool

                NLET (National Load Estimating Tool) is a web-based tool for estimating pollutant loads in watersheds across the contiguous United States. This tool helps visualize the effects of land use patterns, cultivated crops, and conservation practices through graphical representation.

                The National Robotics Engineering Center Agricultural Person-Detection Dataset

                  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.