Multispectral Image Processing to Assess Sugarcane Nitrogen Needs
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Abstract
Population growth has led to an exponential demand for agricultural products, to meet this demand it is necessary to improve management and achieve efficient use of resources without compromising the sustainability of ecosystems, particularly agricultural ones. One of the technologies that facilitate these tasks is precision agriculture (PA), which focuses on the optimization of resources and inputs based on the compilation of precise and timely geo-information of variables of agricultural interest with high spatio-temporal variability, obtained through remote sensors of three types: images captured by satellites or airplanes, images obtained from manned and unmanned aerial vehicles (UAVs) and specific information with sensors mounted on machinery or in the field. These limitations were overcome by using multispectral images, which has increased applications for agricultural purposes. Currently, multispectral images allow quantifying soil moisture, monitoring the presence of droughts and the degree of crop water stress, estimating the temporal and spatial variability of evapotranspiration, monitoring phenology, detecting nutritional deficiencies, estimating the degree of weed infestation. and insects, calculate organic carbon and soil salinity, and estimate yields and agricultural production. The use of geospatial technologies in the PA has changed the paradigm of agriculture and today constitutes a viable alternative to face the challenges that food production demands in a world with high climate variability.
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