Reference number : GC-2017-Q1-02242, amendment number : , agreement type : G, recipient legal name : Adamchuk, Viacheslav (McGill University)|Adamchuk, Viacheslav (Université McGill), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The ultimate goal of this research program is to advance proximal soil and plant sensing technologies to enable low-cost acquisition of accurate information about spatial and temporal changes in crop growing conditions across a landscape. Proximal sensing systems rely on gathering signals from transducers placed in contact, or less than 2 m away from the target. Knowing and understanding the heterogeneity of soil and plant properties help farmers and other land managers optimize their decision-making process to develop profitable, sustainable, and environmentally friendly operations. During the 2017-2022 period , the emphasis will be placed on integrating several soil sensing techniques in a single, on-the-spot soil analyzer and the development of an optimized methodology for deployment of this system in both piloted and autonomous modes of operation. The minimum integrated package will include: an integrated cartridge of several ion-selective membranes (pH, nitrate, potassium, sodium, etc), digital microscope, spectroscope, soil respiration sensor as well as mechanical resistance and water content sensors. The system will be tested in both mineral and organic soil conditions. The integrated plant sensing platform will be extended to combine multispectral, ultrasonic and thermal measurements with low-grade laser scanning and digital imagery components. This platform will serve as the basic crop phenotyping unit; it will be tested on commodity crops (corn and soybean) as well as forage and vegetable crops. Finally, a cloud-based geospatial data analytic tool will be developed to blend proximal sensing data obtained using systems being developed with conventional high-density data layers, such as field topography, apparent soil electrical conductivity, yield maps, satellite and low-altitude imagery, etc. The neighborhood search clustering process will be used to increase the value of information obtained by minimizing the cost and maximizing the accuracy of targeted thematic maps revealing spatially variable soil productivity and potential crop stress assessments. The program will engage at least six HQPs over the next five years. The results will be disseminated within the precision agriculture scientific community, relevant agribusinesses and service providers as well as crop producer associations., program purpose french : The ultimate goal of this research program is to advance proximal soil and plant sensing technologies to enable low-cost acquisition of accurate information about spatial and temporal changes in crop growing conditions across a landscape. Proximal sensing systems rely on gathering signals from transducers placed in contact, or less than 2 m away from the target. Knowing and understanding the heterogeneity of soil and plant properties help farmers and other land managers optimize their decision-making process to develop profitable, sustainable, and environmentally friendly operations. During the 2017-2022 period , the emphasis will be placed on integrating several soil sensing techniques in a single, on-the-spot soil analyzer and the development of an optimized methodology for deployment of this system in both piloted and autonomous modes of operation. The minimum integrated package will include: an integrated cartridge of several ion-selective membranes (pH, nitrate, potassium, sodium, etc), digital microscope, spectroscope, soil respiration sensor as well as mechanical resistance and water content sensors. The system will be tested in both mineral and organic soil conditions. The integrated plant sensing platform will be extended to combine multispectral, ultrasonic and thermal measurements with low-grade laser scanning and digital imagery components. This platform will serve as the basic crop phenotyping unit; it will be tested on commodity crops (corn and soybean) as well as forage and vegetable crops. Finally, a cloud-based geospatial data analytic tool will be developed to blend proximal sensing data obtained using systems being developed with conventional high-density data layers, such as field topography, apparent soil electrical conductivity, yield maps, satellite and low-altitude imagery, etc. The neighborhood search clustering process will be used to increase the value of information obtained by minimizing the cost and maximizing the accuracy of targeted thematic maps revealing spatially variable soil productivity and potential crop stress assessments. The program will engage at least six HQPs over the next five years. The results will be disseminated within the precision agriculture scientific community, relevant agribusinesses and service providers as well as crop producer associations., agreement title english : Integrated Proximal Sensing of Soil and Crop, agreement title french : Integrated Proximal Sensing of Soil and Crop, agreement number : RGPIN, agreement value : 205000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada