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| Filter results6 paper(s) found. |
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1. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
2. Measuring Pasture Mass and Quality Indices Over Time Using Proximal and Remote SensorsTraditionally pasture has been measured or evaluated in terms of a dry matter yield estimate, which has no reference to other important quality factors. The work in this paper measures pasture growth rates on different slopes and aspects and pasture quality through nitrogen N% and metabolizable energy and ME concentration. It is known that permanent pasture species vary greatly in terms of quality and nutritional value through different stages of maturity. Pasture quality decreases as grass tillers... I.J. Yule, M.C. Grafton, L.A. Willis, P.J. Mcveagh |
3. Spatial-temporal Evaluation of Plant Phenotypic Traits Via Imagery Collected by Unmanned Aerial Systems (UAS)Unmanned aerial systems (UAS) and a stereovision approach were implemented to generate a 3D reconstruction of the top of the canopy. The 3D reconstruction or CSM (crop surface model) was utilized to evaluate biophysical parameters for both spatial- and temporal-scales. The main goal of the project was to evaluate sUAVs technology to assist plant height and biomass estimation. The main outcome of this process was to utilize CSMs to gain insights in the spatial-temporal dynamic of plants within... S. Varela, G. Balboa, V. Prasad, T. Griffin, I. Ciampitti, A. Ferguson |
4. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching patterns... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba |
5. Use of Proximal Soil Sensing to Delineate Management Zones in a Commercial Potato Field in Prince Edward Island, CanadaManagement zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties. Such MZs can often be used for site-specific management of crop production inputs. The purpose of this study was to determine the efficiency of two proximal soil sensors for delineating MZs in an 8.1-ha commercial potato (Solanum tuberosum L.) field in Prince Edward Island (PEI), Canada. A galvanic contact resistivity sensor (Veris-3100 [Veris]) and electromagnetic induction sensors... A. Cambouris, A. Lajili, K. Chokmani , I. Perron, V. Adamchuk, A. Biswas , B. Zebrath |
6. Development of an Online Decision-Support Infrastructure for Optimized Fertilizer ManagementDetermination of an optimum fertilizer application rate involves various influential factors, such as past management, soil characteristics, weather, commodity prices, cost of input materials and risk preference. Spatial and temporal variations in these factors constitute sources of uncertainties in selecting the most profitableapplication rate. Therefore, a decision support system (DSS) that could help to minimize production risks in the context of uncertain crop performance is needed. This... S. Shinde, V. Adamchuk, R. Lacroix, N. Tremblay, Y. Bouroubi |