Abstract

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.

Highlights

  • In the past decades, the use of herbicides, pesticides, and other chemical substances has continuously been increased

  • Features of various types and in different fields include texture descriptors based on the histogram, texture features based on the gray level co-occurrence matrix, shape features, moment invariants and color features

  • Before the evaluation of the classifiers’ performance, selected features based on artificial neural network–differential evolution (ANN–DE) and artificial neural network–genetic algorithm (ANN–GA) approaches are shown

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Summary

Introduction

The use of herbicides, pesticides, and other chemical substances has continuously been increased. Overuse of these materials cause surface water pollution, environmental pollution, and animal and human toxicity [1] (Liu and O’Connell, 2002). For this reason, scientists proposed the use of precision agriculture. One of the advantages of precision agriculture is the use of chemical substances only over the area of interest, called site-specific spray. The first step in site-specific spray operations is the proper recognition of the area of interest. There have been various researchers working on the recognition of different plants and trees

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