01465nam a2200241 a 450000100080000000500110000800800410001910000220006024501030008226001860018552006550037165000090102665300160103565300340105165300110108565300130109665300140110965300130112365300290113665300180116570000180118370000220120120521122019-03-08       2016    bl uuuu     u00u1 u    #d1 aNACHTIGALL, L. G.  aClassification of apple tree disorders using Convolutional Neural Networks.h[electronic resource]  aIn: INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENSE, 28., 2016, San Jose, United States. Anais...San Jose, United States: IEEE, Paper Submission 127, p. 472-476c2016  aAbstract?This paper studies the use of Convolutional Neural Networks to automatically detect and classify diseases, nutritional deficiencies and damage by herbicides on apple trees from images of their leaves. This task is fundamental to guarantee a high quality of the resulting yields and is currently largely performed by experts in the field, which can severely limit scale and add to costs. By using a novel data set containing labeled examples consisting of 2539 images from 6 known disorders, we show that trained Convolutional Neural Networks are able to match or outperform experts in this task, achieving a 97.3% accuracy on a hold-out set.  aMaca  aApple trees  aConvolutional Neural Networks  aDamage  aDiseases  aHerbicide  aMacieira  aNutritional deficiencies  aRedes neurais1 aARAUJO, R. M.1 aNACHTIGALL, G. R.