02939naa a2200457 a 450000100080000000500110000800800410001902400520006010000160011224501670012826000090029550000150030452015660031965000130188565000280189865000210192665000330194765000140198065000260199465000220202065300140204265300280205665300250208465300150210965300180212465300250214265300230216765300290219065300210221965300180224065300160225865300140227465300110228865300260229965300250232570000220235070000210237270000190239370000190241277300500243121221992021-10-20       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1186/s42483-020-00049-82DOI1 aBOCK, C. H.  aFrom visual estimates to fully automated sensor-based measurements of plant disease severitybstatus and challenges for improving accuracy.h[electronic resource]  c2020  aArticle 9.  aAbstract. The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.  aAccuracy  aArtificial intelligence  aDisease severity  aPlant diseases and disorders  aPrecision  aPrecision agriculture  aDoença de Planta  aAcurácia  aAprendizado de máquina  aAprendizado profundo  aAssessment  aDeep learning  aDigital technologies  aDispositivo móvel  aInteligência artificial  aMachine learning  aMobile device  aPhenotyping  aPrecisão  aSensor  aSeveridade da doença  aTecnologias digitais1 aBARBEDO, J. G. A.1 aDEL PONTE, E. M.1 aBOHNENKAMP, D.1 aMAHLEIN, A. K.  tPhytopathology Researchgv. 2, p. 1-30, 2020.