01908naa a2200289 a 450000100080000000500110000800800410001902400610006010000220012124501030014326000090024652009990025565000280125465000190128265000200130165000330132165000220135465300210137665300270139765300190142465300210144365300290146465300280149365300180152165300220153977300570156120968292020-01-07       2018    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.biosystemseng.2018.05.0132DOI1 aBARBEDO, J. G. A.  aFactors influencing the use of deep learning for plant disease recognition.h[electronic resource]  c2018  aDeep learning is quickly becoming one of the most important tools for image classification. This technology is now beginning to be applied to the tasks of plant disease classification and recognition. The positive results that are being obtained using this approach hide some issues that are seldom taken into account in the respective experiments. This article presents an investigation into the main factors that affect the design and effectiveness of deep neural nets applied to plant pathology. An in-depth analysis of the subject, in which advantages and shortcomings are highlighted, should lead to more realistic conclusions on the subject. The arguments used throughout the text are built upon both studies found in the literature and experiments carried out using an image database carefully built to reflect and reproduce many of the conditions expected to be found in practice. This database, which contains almost 50,000 images, is being made freely available for academic purposes.  aArtificial intelligence  aImage analysis  aNeural networks  aPlant diseases and disorders  aDoença de Planta  aDeep neural nets  aDisease classification  aImage database  aImage processing  aInteligência artificial  aProcessamento de imagem  aRedes neurais  aTransfer learning  tBiosystems Engineeringgv. 172, p. 84-91, Aug. 2018.