Identification of Leaf Diseases from Figs Using Deep Learning Methods

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.

Keywords

• Data Analysis
• Deep Learning Methods
• Disease Detection
• Image Classification
• Fig Leaves Diseases

Derin Öğrenme Yöntemleri Kullanılarak İncir Yaprak Hastalıklarının Tanımlanması

Abstract

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.

Keywords

• Veri Analizi
• Derin Öğrenme Yöntemleri
• Hastalık Tespiti
• Görüntü Sınıflandırma
• İncir Yaprakları Hastalıkları

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