01734naa a2200217 a 450000100080000000500110000800800410001902400350006010000220009524500960011726000090021352010220022265000120124465000150125665300270127165300320129870000210133070000240135170000150137577301260139021195072020-09-17       2020    bl uuuu     u00u1 u    #d7 a10.4018/IJAEIS.20200101012DOI1 aSANTOS, M. R. dos  aFenômicaba computer vision system for high-throughput phenotyping.h[electronic resource]  c2020  aComputer vision and image processing procedures could obtain crop data frequently and precisely, such as vegetation indexes, and correlating them with other variables, like biomass and crop yield. This work presents the development of a computer vision system for high-throughput phenotyping, considering three solutions: an image capture software linked to a low-cost appliance; an image-processing program for feature extraction; and a web application for results' presentation. As a case study, we used normalized difference vegetation index (NDVI) data from a wheat crop experiment of the Brazilian Agricultural Research Corporation. Regression analysis showed that NDVI explains 98.9, 92.8, and 88.2% of the variability found in the biomass values for crop plots with 82, 150, and 200 kg of N ha1 fertilizer applications, respectively. As a result, NDVI generated by our system presented a strong correlation with the biomass, showing a way to specify a new yield prediction model from the beginning of the crop.  aBiomass  aCrop yield  aComputer vision system  aHigh-throughput phenotyping1 aMADALOZZO, G. A.1 aFERNANDES, J. M. C.1 aRIEDER, R.  tInternational Journal of Agricultural and Environmental Information Systems (IJAEIS)gv. 11, n. 1, p. 1-22, Online, 2020.