Identification of Leaf Diseases from Figs Using Deep Learning Methods

Yılmaz Karatas; Elham Yasin; Talha Alperen Çengel; Bunyamin Gencturk; Müslüme Beyza Yıldız; Yavuz Selim Taspınar; Osman Özbek; Murat Koklu

Article Details

Identification of Leaf Diseases from Figs Using Deep Learning Methods

Authors:

Yılmaz Karatas | Elham Yasin | Talha Alperen Çengel | Bunyamin Gencturk | Müslüme Beyza Yıldız | Yavuz Selim Taspınar | Osman Özbek | Murat Koklu

Publisher:

Selcuk University

Main Topics:

Hassas Tarım Teknolojileri

Journal:

Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.414-426

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001333553

pISSN:

2458-8377

Keywords:

Abstract:

 Early detection of plant diseases is of great importance for agricultural production and plant health. Early detection is important to prevent the spread of diseases and reduce agricultural losses. The aim of this study is to use artificial intelligence technologies for the early detection of diseased fig plants and reduce agricultural losses. The fig leaf dataset used in the study has two classes: healthy and diseased leaves. There are a total of 2321 images in the dataset. Among these images, there are 1350 images representing diseased leaves and 971 images representing healthy leaves. The dataset is divided into 80 training data and 20 test data. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0, and DarkNet-53 algorithms were used to analyze the fig leaves dataset using a MATLAB graphical user interface GUI. The classification accuracy values of each algorithm are as follows: DarkNet-19 90.3, ResNet50 90.95, VGG-19 93.32, VGG-16 92.89, ShuffleNet 89.44, GoogLeNet 87.5, MobileNet-v2 87.5, EfficientNet-b0 85.56, and DarkNet53 91.59. These results evaluate the usability and performance of different algorithms for the early detection of plant diseases. The research emphasizes the importance of the effective use of artificial intelligence technologies in the agricultural industry.

 Early detection of plant diseases is of great importance for agricultural production and plant health. Early detection is important to prevent the spread of diseases and reduce agricultural losses. The aim of this study is to use artificial intelligence technologies for the early detection of diseased fig plants and reduce agricultural losses. The fig leaf dataset used in the study has two classes: healthy and diseased leaves. There are a total of 2321 images in the dataset. Among these images, there are 1350 images representing diseased leaves and 971 images representing healthy leaves. The dataset is divided into 80 training data and 20 test data. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0, and DarkNet-53 algorithms were used to analyze the fig leaves dataset using a MATLAB graphical user interface GUI. The classification accuracy values of each algorithm are as follows: DarkNet-19 90.3, ResNet50 90.95, VGG-19 93.32, VGG-16 92.89, ShuffleNet 89.44, GoogLeNet 87.5, MobileNet-v2 87.5, EfficientNet-b0 85.56, and DarkNet53 91.59. These results evaluate the usability and performance of different algorithms for the early detection of plant diseases. The research emphasizes the importance of the effective use of artificial intelligence technologies in the agricultural industry.

 Early detection of plant diseases is of great importance for agricultural production and plant health. Early detection is important to prevent the spread of diseases and reduce agricultural losses. The aim of this study is to use artificial intelligence technologies for the early detection of diseased fig plants and reduce agricultural losses. The fig leaf dataset used in the study has two classes: healthy and diseased leaves. There are a total of 2321 images in the dataset. Among these images, there are 1350 images representing diseased leaves and 971 images representing healthy leaves. The dataset is divided into 80 training data and 20 test data. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0, and DarkNet-53 algorithms were used to analyze the fig leaves dataset using a MATLAB graphical user interface GUI. The classification accuracy values of each algorithm are as follows: DarkNet-19 90.3, ResNet50 90.95, VGG-19 93.32, VGG-16 92.89, ShuffleNet 89.44, GoogLeNet 87.5, MobileNet-v2 87.5, EfficientNet-b0 85.56, and DarkNet53 91.59. These results evaluate the usability and performance of different algorithms for the early detection of plant diseases. The research emphasizes the importance of the effective use of artificial intelligence technologies in the agricultural industry.

 Bitki hastalıklarının erken teşhisi tarımsal üretim ve bitki sağlığı açısından büyük önem taşımaktadır. Erken teşhis, hastalıkların yayılmasını önlemek ve tarımsal kayıpları azaltmak için önemlidir. Bu çalışmanın amacı, hastalıklı incir bitkilerinin erken tespiti ve tarımsal kayıpların azaltılması için yapay zeka teknolojilerini kullanmaktır. Çalışmada kullanılan incir yaprağı veri kümesi sağlıklı ve hastalıklı yapraklar olmak üzere iki sınıfa sahiptir. Veri kümesinde toplam 2321 görüntü bulunmaktadır. Bu görüntüler arasında hastalıklı yaprakları temsil eden 1350 görüntü ve sağlıklı yaprakları temsil eden 971 görüntü bulunmaktadır. Veri kümesi 80 eğitim verisi ve 20 test verisi olarak ayrılmıştır. DarkNet-19, ResNet50, VGG-19, VGG-16, ShuffleNet, GoogLeNet, MobileNet-v2, EfficientNet-b0 ve DarkNet-53 algoritmaları MATLAB grafiksel kullanıcı arayüzü GUI kullanılarak incir yaprakları veri kümesini analiz etmek için kullanılmıştır. Her bir algoritmanın sınıflandırma doğruluk değerleri aşağıdaki gibidir: DarkNet-19 90,3, ResNet50 90,95, VGG-19 93,32, VGG-16 92,89, ShuffleNet 89,44, GoogLeNet 87,5, MobileNet-v2 87,5, EfficientNet-b0 85,56 ve DarkNet53 91,59. Bu sonuçlar, bitki hastalıklarının erken tespiti için farklı algoritmaların kullanılabilirliğini ve performansını değerlendirmektedir. Araştırma, yapay zekâ teknolojilerinin tarım sektöründe etkin kullanımının önemini vurgulamaktadır.

Article Details

Identification of Leaf Diseases from Figs Using Deep Learning Methods

Authors:

Yılmaz Karatas | Elham Yasin | Talha Alperen Çengel | Bunyamin Gencturk | Müslüme Beyza Yıldız | Yavuz Selim Taspınar | Osman Özbek | Murat Koklu

Publisher:

Selcuk University

Main Topics:

Hassas Tarım Teknolojileri

Journal:

Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.414-426

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001333553

pISSN:

2458-8377

Data Analysis

Deep Learning Methods

Disease Detection

Image Classification

Fig Leaves DiseasesVeri Analizi

Derin Öğrenme Yöntemleri

Hastalık Tespiti

Görüntü Sınıflandırma

İncir Yaprakları Hastalıkları

Issues

Article Details

Authors: Yılmaz Karatas | Elham Yasin | Talha Alperen Çengel | Bunyamin Gencturk | Müslüme Beyza Yıldız | Yavuz Selim Taspınar | Osman Özbek | Murat Koklu

Publisher: Selcuk University

Main Topics: Hassas Tarım Teknolojileri

Journal: Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.414-426

Document Type: Article

Publication Date: 12/2024

Document Language: English

Document ID (Org No.): 2001333553

pISSN: 2458-8377

Issues

Authors:

Yılmaz Karatas | Elham Yasin | Talha Alperen Çengel | Bunyamin Gencturk | Müslüme Beyza Yıldız | Yavuz Selim Taspınar | Osman Özbek | Murat Koklu

Publisher:

Selcuk University

Main Topics:

Hassas Tarım Teknolojileri

Journal:

Selcuk Journal of Agriculture and Food Sciences Vol.38 Issue 3, pp.414-426

Document Type:

Article

Publication Date:

12/2024

Document Language:

English

Document ID (Org No.):

2001333553

pISSN:

2458-8377