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Acknowledgement to Reviewers of Computers in 2019
Open AccessArticle

A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images

1
Department of Biosystems Engineering, College of Agriculture and University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran
2
Department of Teoría de la Señal y Comunicaciones e Ingeniería Telemática, University of Valladolid, 47011 Valladolid, Spain
3
Castilla-León Neuroscience Institute (INCYL), University of Salamanca, 37007 Salamanca, Spain
*
Author to whom correspondence should be addressed.
Computers 2020, 9(1), 6; https://doi.org/10.3390/computers9010006
Received: 19 December 2019 / Revised: 24 January 2020 / Accepted: 24 January 2020 / Published: 28 January 2020
A computer vision system for automatic recognition and classification of five varieties of plant leaves under controlled laboratory imaging conditions, comprising: 1–Cydonia oblonga (quince), 2–Eucalyptus camaldulensis dehn (river red gum), 3–Malus pumila (apple), 4–Pistacia atlantica (mt. Atlas mastic tree) and 5–Prunus armeniaca (apricot), is proposed. 516 tree leaves images were taken and 285 features computed from each object including shape features, color features, texture features based on the gray level co-occurrence matrix, texture descriptors based on histogram and moment invariants. Seven discriminant features were selected and input for classification purposes using three classifiers: hybrid artificial neural network–ant bee colony (ANN–ABC), hybrid artificial neural network–biogeography based optimization (ANN–BBO) and Fisher linear discriminant analysis (LDA). Mean correct classification rates (CCR), resulted in 94.04%, 89.23%, and 93.99%, for hybrid ANN–ABC; hybrid ANN–BBO; and LDA classifiers, respectively. Best classifier mean area under curve (AUC), mean sensitivity, and mean specificity, were computed for the five tree varieties under study, resulting in: 1–Cydonia oblonga (quince) 0.991 (ANN–ABC), 95.89% (ANN–ABC), 95.91% (ANN–ABC); 2–Eucalyptus camaldulensis dehn (river red gum) 1.00 (LDA), 100% (LDA), 100% (LDA); 3–Malus pumila (apple) 0.996 (LDA), 96.63% (LDA), 94.99% (LDA); 4–Pistacia atlantica (mt. Atlas mastic tree) 0.979 (LDA), 91.71% (LDA), 82.57% (LDA); and 5–Prunus armeniaca (apricot) 0.994 (LDA), 88.67% (LDA), 94.65% (LDA), respectively.
Keywords: apple, apricot, classification, computer vision, mt. Atlas mastic tree, neural network, precision agriculture, quince, river red gum, site-specific spray apple, apricot, classification, computer vision, mt. Atlas mastic tree, neural network, precision agriculture, quince, river red gum, site-specific spray
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MDPI and ACS Style

Sabzi, S.; Pourdarbani, R.; Arribas, J.I. A Computer Vision System for the Automatic Classification of Five Varieties of Tree Leaf Images. Computers 2020, 9, 6.

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