Author(s): NACHTIGALL, L. G.; ARAUJO, R. M.; NACHTIGALL, G. R.

Summary: Rapid diagnosis ofsymptoms caused by pest attack, diseases and nutritional or physiological disorders in apple orchards is essential to avoid greater losses. This paper aimed to evaluate the efficiency of Convolutional Neural Networks (CNN) to automatically detect and classify symptoms of diseases, nutritional deficiencies and damage caused by herbicides in apple trees from images of their leaves and fruits. A novel data set was developed containing labeled examples consisting of approximately 10,000 images of leaves and apple fruits divided into 12 classes, which were classified by algorithms of machine learning, with emphasis on models of deep learning. The resultsshowed trained CNNs can overcome the performance of experts and other algorithms of machine learning in the classification of symptoms in apple trees from leaves images, with an accuracy of 97.3% and obtain 91.1% accuracy with fruit images. In this way, the use of Convolutional Neural Networks may enable the diagnosis of symptoms in apple trees in a fast, precise and usual way. Keywords Apple, Apple Disorders, Artificial Intelligence, Automatic Disease Identification, Classifications, Convolutional Neural Networks, Disorders, Machine Learning

Publication year: 2017

Types of publication: Journal article

Unit: Embrapa Grape & Wine

Keywords: Apple, Apple Disorders, Artificial Intelligence, Automatic Disease Identification, Classifications, Convolutional Neural, Doença, Doença de planta, Macieira, Maçã