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| Filter results5 paper(s) found. |
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1. Management Of Remote Imagery For Precision AgricultureSatellite and airborne remotely sensed images cover large areas, which normally include dozens of agricultural plots. Agricultural operations such as sowing, fertilization, and pesticide applications are designed for the whole plot area, i.e. 5 to 20 ha, or through precision agriculture. This takes into account the spatial variability of biotic and of abiotic factors and uses diverse technologies to apply inputs at variable rates, fitted to the needs of each small defined area, i.e. 25 to 200... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, J.M. Pe, M. Jurado-exp, I. Castillejo-gonz, A. Garc, F. Lopez-granados, L. Prassack |
2. Sectioning And Assessment Remote Images For Precision Agriculture: The Case Of Orobanche Crenate In Pea CropThe software SARI® has been developed to implement precision agriculture strategies through remote sensing imagery. It is written in IDL® and works as an add-on of ENVI®. It has been designed to divide remotely sensed imagery into “micro-images”, each corresponding to a small area (“micro-plot”), and to determine the quantitative agronomic and/or environmental biotic (i.e. weeds, pathogens) and/or non-biotic (i.e. nutrient levels) indicator/s... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, M. Gomez-casero, J.M. Pe, M. Jurado-exp, F. Lopez-granados, I. Castillejo-gonz, A. Garc |
3. Mapping the Leaf Area Index In Vineyard Using a Ground-Based LIDAR ScannerThe leaf area index (LAI) is defined as the one-sided leaf area per unit ground area and is probably the most widely used index to characterize grapevine vigour. However, direct LAI measurement requires the use of destructive leaves sampling methods which are costly and time-consuming and so are other indirect methods. Faced with these techniques, vineyard leaf area can be indirectly estimated using ground-based LIDAR sensors that scan the vines and get information about the geometry and/or structure... J. Arno, I. Del moral, A. Escolà, J. Company, J.A. MartÍnez-casasnovas, J. Masip, R. Sanz, J.R. Rosell |
4. Semiautomatization in Open Source Software of a Method for Monitoring the Land Cover Change with GEE and Sentinel-2Land cover change is a dynamic process that unfolds spatially and temporally. As such, it is imperative to develop semi-automatic methods within freely available software to enhance processing efficiency and reduce costs. The amalgamation of open-source applications, platforms, and software for satellite image processing has emerged as a compelling alternative, fostering advancements in land cover change classification and monitoring. This study introduces a semi-automated methodology using the... S.A. Rubaino sosa, Y. Rubiano, J.H. Bernal riobo |
5. Evaluating the Potential of In-season Spatial Prediction of Corn Yield and Responses to Nitrogen by Combining Crop Growth Modeling, Satellite Remote Sensing and Machine LearningNitrogen (N) is a critical yield-limiting factor for corn (Zea mays L.). However, over-application of N fertilizers is a common problem in the US Midwest, leading to many environmental problems. It is crucial to develop efficient precision N management (PNM) strategies to improve corn N management. Different PNM strategies have been developed using proximal and remote sensing, crop growth modeling and machine learning. These strategies have both advantages and disadvantages. There is... X. Zhen, Y. Miao, K. Mizuta, S. Folle, J. Lu, R.P. Negrini, G. Feng, Y. Huang |