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Palabras clave – provistas por la editorial

SVM; budding rate; UAV; geometric consistency; radiometric consistency; point clouds; ICP; reflectance maps; vegetation indices; Parrot Sequoia; artificial intelligence; precision agriculture; agricultural robot; optimization algorithm; online operation; segmentation; coffee leaf rust; machine learning; deep learning; remote sensing; Fourth Industrial Revolution; Agriculture 4.0; failure strain; sandstone; digital image correlation; Hill–Tsai failure criterion; finite element method; reference evapotranspiration; moisture sensors; machine learning regression; frequency-domain reflectometry; randomizable filtered classifier; convolutional neural network; U-Net; land use; banana plantation; Panama TR4; aerial photography; remote images; systematic mapping study; agriculture; applications; total leaf area; mixed pixels; Cabernet Sauvignon; NDVI; Normalized Difference Vegetation Index; precision viticulture; 3D model; spatial vision; fertirrigation; teaching–learning; spectrometry; Sentinel-2; pasture quality index; normalized difference vegetation index; normalized difference water index; supplementation; decision making; digital agriculture; grape yield estimate; berries counting; Dilated CNN; machine learning algorithms; classification performance; winter wheat mapping; large-scale; water stress; Prunus avium L.; stem water potential; low-cost thermography; thermal indexes; canopy temperature; non-water-stressed baselines; non-transpiration baseline; soil moisture; andosols; image processing; greenhouse; automatic tomato harvesting; n/a