Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1052112
Research center of Embrapa/Collection: Embrapa Uva e Vinho - Artigo em anais de congresso (ALICE)
Date Issued: 2016
Type of Material: Artigo em anais de congresso (ALICE)
Authors: NACHTIGALL, L. G.
ARAUJO, R. M.
NACHTIGALL, G. R.
Additional Information: GILMAR RIBEIRO NACHTIGALL, CNPUV.
Title: Classification of apple tree disorders using Convolutional Neural Networks.
Publisher: In: INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENSE, 28., 2016, San Jose, United States. Anais...San Jose, United States: IEEE, Paper Submission 127, p. 472-476, 2016.
Language: pt_BR
Keywords: Macieira
Redes neurais
Convolutional Neural Networks
Diseases
Nutritional deficiencies
Damage
Apple trees
Herbicide.
Description: Abstract?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.
Thesagro: Maca
Data Created: 2016-08-30
Appears in Collections:Artigo em anais de congresso (CNPUV)

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