Classification of Apple Tree Disorders Using Convolutional Neural Networks

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 large...

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Bibliographic Details
Published in2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) pp. 472 - 476
Main Authors Nachtigall, Lucas G., Araujo, Ricardo M., Nachtigall, Gilmar R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2016
Subjects
Convolutional Neural Networks
Diseases
Feature extraction
Machine Learning
Magnesium
Multilayer perceptrons
Plant Disease Classification
Potassium
Training
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Summary: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.
ISSN:2375-0197
DOI:10.1109/ICTAI.2016.0078