Sınıflandırma Probleminde Derin Özellik Birleştirme Yaklaşımıyla Domates Yaprağı Görüntülerinde Hastalık Tespiti

Year 2022, Issue: 44, 84 - 92, 31.12.2022

Serdar Ertem , Erdal Özbay

https://doi.org/10.31590/ejosat.1216380

Abstract

Domates, yaşadığımız coğrafya ve dünyanın birçok yerinde üretimi yapılan ve en çok tüketilen önemli sebze türlerindendir. Domates üretiminde verim ve kaliteyi olumsuz yönde etkileyen en önemli faktörlerin başında zararlı organizma olarak adlandırılan hastalık gelmektedir. Domates, çevresel ve iklim faktörlerine bağlı olarak ekim sürecinin her aşamasında birçok hastalığa yakalanabilir. Bitki hastalıklarında yapılması gereken ilk şey hastalığın doğru tespit edilmesi ve gereken önlemlerin alınmasıdır. Bu çalışmada 9 hastalıklı ve 1 sağlıklı sınıftan oluşan toplam 18.160 domates yaprağı görüntüsü bulunan veri seti kullanılmıştır (Kaggle, 2021).Genel erişime açık Kaggle domates yaprağı hastalığı veri seti üzerinde deneysel sonuçlar elde edilmiştir. Analizler yapılırken veri seti, %80 eğitim ve %20 test verisi olarak ayrılmıştır. Çalışmada, Evrişimli Sinir Ağı (CNN) tabanlı DenseNet-201, ResNet-101 ve ShuffleNet modelleri kullanılarak domates yaprağı görüntülerinden 3000 adet öznitelik çıkarılmıştır. Bu çalışmada öznitelik sayısını düşürmek için Temel Bileşen Analizi (PCA) kullanılarak boyut indirgeme yapılmıştır. Domates yaprağındaki hastalıkları sınıflandırmak amacıyla Destek-Vektör Makinaları (SVM) algoritması kullanılmıştır. Eğitilen ağ mimarileri tek tek incelenmiştir. Bu incelemeler sonucunda mimarilerin doğruluk oranları AlexNet, DenseNet-201, GoogleNet, MobileNet, ResNet-101 ve ShuffleNet için sırası ile %93.5, %97.1, %91.0, %94.5, %97.4 ve %96.6 bulunmuştur. Yapılan analizlerden sonra doğruluk oranı yüksek olan DenseNet-201, ResNet-101 ve ShuffleNet ön eğitimli ağ mimarileri birleştirilerek ve Temel Bileşen Analizi (PCA) kullanılarak boyut indirgeme yapılmıştır. Bu çalışmada yapılan sınıflandırma analizlerine göre en iyi performans gösteren Cubic SVM sınıflandırıcı ve One-vs-All Çok Sınıflı bileşen metodu ile %99.2 doğruluk oranına ulaşılmıştır. Literatür incelemeleri sonucunda domates yaprağı hastalık tespiti için bu çalışmanın etkili ve yüksek bir performans gösterdiği sonucuna ulaşılmıştır.

Keywords

Domates yaprağı, CNN, SVM, PCA, Sınıflandırma

Disease Detection in Tomato Leaf Images by Deep Feature Combination Approach in Classification Problem

Abstract

Tomato is one of the most important vegetable species produced and consumed mostly in the geography we live in and in many parts of the world. One of the most important factors that negatively affect yield and quality in tomato production is a disease called harmful organisms. Tomatoes can suffer from many diseases at every stage of the planting process, depending on environmental and climatic factors. The first thing to do in plant diseases is to correctly identify the disease and take the necessary precautions. In this study, a dataset with a total of 18.160 tomato leaf images consisting of 9 diseased and 1 healthy class was used (Kaggle, 2021). Experimental results were obtained on the publicly accessible Kaggle tomato leaf disease dataset. During the analysis, the data set was divided into 80% training and 20% test data. In the study, 3000 features were extracted from tomato leaf images by using Convolutional Neural Network (CNN) based DenseNet-201, ResNet-101, and ShuffleNet models. In this study, dimension reduction was made using Principal Component Analysis (PCA) to reduce the number of features. Support-Vector Machines (SVM) algorithm was used to classify diseases in tomato leaves. The trained network architectures were examined one by one. As a result of these examinations, the accuracy rates of the architectures were found to be 93.5%, 97.1%, 91.0%, 94.5%, 97.4%, and 96.6% for AlexNet, DenseNet-201, GoogleNet, MobileNet, ResNet-101, and ShuffleNet, respectively. After the analysis, size reduction was made by combining DenseNet-201, ResNet-101, and ShuffleNet pre-trained network architectures with high accuracy and using Principal Component Analysis (PCA). According to the classification analyses made in this study, the Cubic SVM classifier and the One-vs-All Multi-Class component method, which performed the best, achieved 99.2% accuracy. As a result of the literature review, it was concluded that this study showed an effective and high performance for tomato leaf disease detection.

Keywords

Domates yaprağı, CNN, SVM, PCA, Sınıflandırma

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