02081naa a2200253 a 450000100080000000500110000800800410001902200140006010000290007424501120010326000090021552013710022465000170159565000210161265000160163365300100164965300080165965300170166770000200168470000200170470000200172470000270174477300560177121428832022-05-11       2022    bl uuuu     u00u1 u    #d  a1809-44301 aANDRADE JUNIOR, A. S. de  aRemote Detection of water and nutritional status of soybeans using UAV-based images.h[electronic resource]  c2022  aDigital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p&lt0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p&lt0.001), thus being considered promising for the detection of leaf N content in soybeans.  aGas exchange  aVegetation index  aGlycine Max  aDrone  aRPA  aTroca gasosa1 aSILVA, S. P. da1 aSETÚBAL, I. S.1 aSOUZA, H. A. de1 aVIEIRA, P. F. de M. J.  tEngenharia Agrícolagv. 42, n. 2, e20210177, 2022.