Title:	Classification of apple tree disorders using Convolutional Neural Networks.
Authors:	NACHTIGALL, L. G. ARAUJO, R. M. NACHTIGALL, G. R.
Affiliation:	GILMAR RIBEIRO NACHTIGALL, CNPUV.
Date Issued:	2016
Citation:	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.
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
Keywords:	Macieira Redes neurais Convolutional Neural Networks Diseases Nutritional deficiencies Damage Apple trees Herbicide
Type of Material:	Artigo em anais e proceedings
Access:	openAccess
Appears in Collections:	Artigo em anais de congresso (CNPUV)