Proceedings
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| Filter results4 paper(s) found. |
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1. Pesticide Drift Control with Wireless Sensor NetworksPrecision Agriculture is an agricultural practice that uses technology based on the principle of variability. The geographically referenced data implement the process of agricultural automation so as to dose fertilizers and pesticides. The efficient application of low cost pesticides without contamination the environment is an agricultural production challenge. The main effect to be avoided during application is pesticide drift. To minimize it is important to know the environmental conditions,... C.E. Cugnasca, I.M. Santos |
2. NOAA's National Geodetic Survey?s National Spatial Reference System And The National Height ModernizatioThe National Geodetic Survey (NGS) is responsible for the establishment and maintenance of the National Spatial Reference System (NSRS). NGS manages a network of Continuously Operating Reference Stations (CORS) that provides Global Navigation Satellite System (GNSS) data and serves as the backbone of the NSRS. Our goal is to maintain a network of stations to serve as control for any project undertaken by local surveyors. In addition, numerous other applications benefit from an... J.D. Rigney |
3. Ground Vehicle Mapping of Fields Using LiDAR to Enable Prediction of Crop BiomassMapping field environments into point clouds using a 3D LIDAR has the ability to become a new approach for online estimation of crop biomass in the field. The estimation of crop biomass in agriculture is expected to be closely correlated to canopy heights. The work presented in this paper contributes to the mapping and textual analysis of agricultural fields. Crop and environmental state information can be used to tailor treatments to the specific site. This paper presents the current results... M.P. Christiansen, M.S. Laursen, R.N. Jørgensen, S. Skovsen, R. Gislum |
4. Using a Fully Convolutional Neural Network for Detecting Locations of Weeds in Images from Cereal FieldsInformation about the presence of weeds in fields is important to decide on a weed control strategy. This is especially crucial in precision weed management, where the position of each plant is essential for conducting mechanical weed control or patch spraying. For detecting weeds, this study proposes a fully convolutional neural network, which detects weeds in images and classifies each one as either a monocot or dicot. The network has been trained on over 13 000 weed annotations... M. Dyrmann, S. Skovsen, R.N. Jørgensen, M.S. Laursen |