Deep Feature Extraction and Machine Learning Method for Leaf Disease Detection in Plants

Year 2022, Volume: 34 Issue: 1, 123 - 132, 20.03.2022

Orhan Yaman Türker Tuncer

https://doi.org/10.35234/fumbd.982348

Cited By: 3

Abstract

The development of deep learning methods has positively affected smart agriculture practices. Deep learning and machine learning are used in many areas such as detecting diseases in tree and plant leaves, predicting fruit and vegetable yields. In this study, leaf disease was detected by using deep learning and feature selection methods. For the proposed method, a total of 726 images of walnut leaves were collected. These images consist of two classes, healthy and diseased. Deep learning models were used to extract features from these images. 17 deep learning models were tested and the best two models were selected. These two models are designated as DarkNet53 and ResNet101. The features from these two models are combined. Thus, hybrid feature extraction was created. ReliefF algorithm is used for feature selection. Thus, the most weighty features were selected. Support Vector Machine (SVM) algorithm is used for the classification of selected features. With the proposed method, 99.58% accuracy was calculated.

Keywords

Leaf disease detection, deep feature extraction, ReliefF, DenseNet53, ResNet101.

Bitkilerdeki Yaprak Hastalığı Tespiti için Derin Özellik Çıkarma ve Makine Öğrenmesi Yöntemi

Abstract

Derin öğrenme yöntemlerinin gelişmesi akıllı tarım uygulamalarını olumlu yönde etkilemiştir. Ağaç ve bitki yapraklarındaki hastalıkların tespit edilmesi, meyve ve sebze rekoltelerinin tahmin edilmesi gibi birçok alanda derin öğrenme ve makine öğrenmesi kullanılmaktadır. Bu çalışmada derin öğrenme ve özellik seçme yöntemi kullanılarak yaprak hastalığı tespit edilmiştir. Önerilen yöntem için ceviz yapraklarından oluşan 726 görüntü toplanmıştır. Bu görüntüler sağlıklı ve hastalıklı olmak üzere iki sınıftan oluşmaktadır. Bu görüntülerden özellik çıkarımı yapmak için derin öğrenme modelleri kullanılmıştır. 17 adet derin öğrenme modeli test edilmiş ve en iyi iki model seçilmiştir. Bu iki model DarkNet53 ve ResNet101 olarak belirlenmiştir. Bu iki modelden elde edilen özellikler birleştirilmiştir. Böylece hibrit bir özellik çıkarımı oluşturulmuştur. Özellik seçimi için ReliefF algoritması kullanılmıştır. Böylece en ağırlıklı özellikler seçilmiştir. Seçilen özelliklerin sınıflandırılması için Destek Vektör Makinesi (DVM) algoritması kullanılmıştır. Önerilen yöntem ile %99.58 doğruluk hesaplanmıştır.

Keywords

Yaprak hastalığı tespiti, derin özellik çıkarımı, ReliefF, DenseNet53, ResNet101.

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