Sınıflandırma Probleminde Derin Özellik Birleştirme Yaklaşımıyla Domates Yaprağı Görüntülerinde Hastalık Tespiti

Yıl 2022, Sayı: 44, 84 - 92, 31.12.2022

Serdar Ertem , Erdal Özbay

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

Öz

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.

Anahtar Kelimeler

Domates yaprağı, CNN, SVM, PCA, Sınıflandırma

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

Öz

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.

Anahtar Kelimeler

Domates yaprağı, CNN, SVM, PCA, Sınıflandırma

Kaynakça

• Acikgoz, H. (2022). A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Applied Energy, (305). doi:https://doi.org/10.1016/j.apenergy.2021.117912.
• Agarwal, M., Singh, A., Arjaria, S., Sinha, A., & Gupta, S. (2020). ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Computer Science, 167, 293-301.
• Ahlawat, S., & Choudhary, A. (2020). Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167, 2554-2560.
• Al-Amin, M., Karim, D. Z., & Bushra, T. A. (2019, December). Prediction of rice disease from leaves using deep convolution neural network towards a digital agricultural system. In 2019 22nd International Conference on Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.
• Arivazhagan, S., Shebiah, R. N., Ananthi, S. N., & Varthini, S. V. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: The CIGR Journal, 15, 211–217.
• Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A., & Stefanovic, D. (2019). Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection. Symmetry, 11(7). doi:10.3390/sym11070939
• Bakr, M., Abdel-Gaber, S., Nasr, M., & Hazman, M. (2022). DenseNet Based Model for Plant Diseases Diagnosis. European Journal of Electrical Engineering and Computer Science, 6(5), 1-9.
• Brahimi, M., Boukhalfa, K., & Moussaoui, A. (2017). Deep learning for tomato diseases: classification and symptoms visualization. Applied Artificial Intelligence, 31(4), 299-315.
• Chaovalitwongse, W. A., Fan, Y. J., & Sachdeo, R. C. (2007). On the time series $ k $-nearest neighbor classification of abnormal brain activity. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37(6), 1005-1016.
• Cheng B. and Matson E. T., “A feature-based machine learning agent for automatic rice and weed discrimination,” International Conference on Artifcial Intelligence and Sof Computing, pp. 517– 527, 2015.
• Cheng, X., Zhang, Y., Chen, Y., Wu, Y., and Yue, Y., “Pest identifcation via deep residual learning in complex background,” Computers and Electronics in Agriculture, vol. 141, pp. 351–356, 2017.
• Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. doi:https://doi.org/10.1016/j.compag.2018.01.009
• GeethaRamani, R., & ArunPandian, J. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput. Electr. Eng., 76, 323-338.
• H. Sabrol, & K. Satish. (2016). Tomato plant disease classification in digital images using classification tree (pp. 1242–1246). Presented at the 2016 International Conference on Communication and Signal Processing (ICCSP). doi:10.1109/ICCSP.2016.7754351
• Hoo, Z. H., Candlish, J., & Teare, D. (2017). What is an ROC curve? Emergency Medicine Journal, 34(6), 357–359. doi:10.1136/emermed-2017-206735
• Jolliffe, I. T. (2002). Principal component analysis for special types of data (pp. 338-372). Springer New York.
• Kaggle. (2021, December 6). Kaggle. Kaggle data set. dataset. https://www.kaggle.com/datasets. Erişim: 18.10.2022
• Liu, Y., Tang, F., Zhou, D., Meng, Y., & Dong, W. (2016). Flower classification via convolutional neural network. In 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA) (pp. 110–116). doi:10.1109/FSPMA.2016.7818296
• Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7.
• Mokhtar, U. et al. (2015). SVM-Based Detection of Tomato Leaves Diseases. In: , et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_55
• Öksüz, C., & Güllü, M. K. (2020, October). Deep Feature Extraction Based Fine-Tuning. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
• Sannakki, S., Rajpurohit, V. S., Sumira, F., & Venkatesh, H. (2013). A neural network approach for disease forecasting in grapes using weather parameters. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1–5). doi:10.1109/ICCCNT.2013.6726613
• Shruthi, U., Nagaveni, V., & Raghavendra, B. K. (2019, March). A review on machine learning classification techniques for plant disease detection. In 2019 5th International conference on advanced computing & communication systems (ICACCS) (pp. 281-284). IEEE.
• Sibiya, M., & Sumbwanyambe, M. (2019). A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 1(1), 119-131.
• Song, K., Sun, X. Y., & Ji, J. W. (2007). Corn leaf disease recognition based on support vector machine method. Transactions of the CSAE, 23(1), 155-157.
• Suryawati, E., Sustika, R., Yuwana, R. S., Subekti, A., & Pardede, H. F. (2018, October). Deep structured convolutional neural network for tomato diseases detection. In 2018 international conference on advanced computer science and information systems (ICACSIS) (pp. 385-390). IEEE.
• Sünnetci, K. M. , Alkan, A. & Tar, E. (2021). Göğüs X-Ray görüntülerinin AlexNet tabanlı sınıflandırılması . Computer Science , 5th International Artificial Intelligence and Data Processing symposium, 375-384. DOI: 10.53070/bbd.989192
• Tan, L., Lu, J., & Jiang, H. (2021). Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods. AgriEngineering, 3(3), 542–558. https://doi.org/10.3390/agriengineering3030035
• Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric., 161, 272–279.
• Tüfekçi, M., & Karpat, F. (2019). Derin Öğrenme Mimarilerinden Konvolüsyonel Sinir Ağları (CNN) Üzerinde Görüntü İşleme-Sınıflandırma Kabiliyetininin Arttırılmasına Yönelik Yapılan Çalışmaların İncelenmesi. In International Conference on Human-Computer Interaction, Optimization and Robotic Applications (pp. 28-31).
• Vapnik, V. N. (1995). The nature of statistical learning. Theory.
• Walliser, J. (2018). How to identify and control tomato plant disease. https://savvygardening.com/tomato-plant-disease/