02017naa a2200253 a 450000100080000000500110000800800410001902400610006010000220012124501320014326000090027552011700028465000190145465000190147365000330149265000320152565000220155765000120157965300290159165300190162065300380163965300210167777300650169820405452023-05-24       2016    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.biosystemseng.2016.01.0172DOI1 aBARBEDO, J. G. A.  aA review on the main challenges in automatic plant disease identification based on visible range images.h[electronic resource]  c2016  aThe problem associated with automatic plant disease identification using visible range images has received considerable attention in the last two decades, however the techniques proposed so far are usually limited in their scope and dependent on ideal capture conditions in order to work properly. This apparent lack of significant advancements may be partially explained by some difficult challenges posed by the subject: presence of complex backgrounds that cannot be easily separated from the region of interest (usually leaf and stem), boundaries of the symptoms often are not well defined, uncontrolled capture conditions may present characteristics that make the image analysis more difficult, certain diseases produce symptoms with a wide range of characteristics, the symptoms produced by different diseases may be very similar, and they may be present simultaneously. This paper provides an analysis of each one of those challenges, emphasizing both the problems that they may cause and how they may have potentially affected the techniques proposed in the past. Some possible solutions capable of overcoming at least some of those challenges are proposed.  aDigital images  aImage analysis  aPlant diseases and disorders  aSigns and symptoms (plants)  aDoença de planta  aSintoma  aAutomatic identification  aImagem digital  aProcessamento de imagens digitais  aVisible symptoms  tBiosystems Engineering, Londongv. 144, p. 52-60, Apr. 2016.