DSpace Repository

ÖNERİLEN KONVOLÜSYON SİNİR AĞI YAKLAŞIMI KULLANARAK ELMA YAPRAĞI HASTALIKLARININ SINIFLANDIRILMASI

Show simple item record

dc.creator ÇETİNER, Halit; ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ, TEKNİK BİLİMLER MESLEK YÜKSEKOKULU
dc.date 2021-12-20T00:00:00Z
dc.date.accessioned 2022-05-10T10:56:39Z
dc.date.available 2022-05-10T10:56:39Z
dc.identifier https://dergipark.org.tr/tr/pub/jesd/issue/66319/980629
dc.identifier 10.21923/jesd.980629
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/96102
dc.description Tarım arazilerindeki elma ağaçlarını sürekli olarak kontrol etmek zordur. Ağaç yapraklarında oluşan bir hastalık durumunda diğer yapraklara hastalığın bulaş riski yüksektir. Erken dönemde hastalığın otomatik tespitini gerçekleştirerek bitkinin daha fazla bozulmasını önlemek gereklidir. Eğer hastalık tespitinde geç kalınırsa planlanan üretim gerçekleştirilememektedir. Bir çiftçi ya da tarım uzmanı tarafından hastalıkların tespit edilmesi durumunda geç kalınmaktadır. Buna ek olarak tarım arazileri büyüdükçe ihtiyaç duyulan uzman sayısı da ona göre artış göstermektedir. Bu sebeplerden dolayı elma ağaçlarına ait yaprak görüntülerini kullanarak ağaç yaprakları elma kabuğu, yaprak pası, sağlıklı elma ve birden fazla hastalık durumları olmak üzere 4 farklı sınıfa gruplandırılmıştır. Öne sürülen yöntemde görüntülerde gürültülerin temizlenmesi, ilgili alanın tespiti ve YUV renk uzayı üzerinde histogram eşitleme gerçekleştirilmiştir. Kullanılan veri setinde sınıf dağılımlarının dengesiz olmasından dolayı SMOTE yöntemi ile azınlık olarak kalan sınıflar için veri büyütmesi uygulanmıştır. Sonrasında DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50V2 önceden eğitilmiş ağ modelleri kullanılarak öznitelikler çıkartılmıştır. Çıkartılan öznitelikler geliştirilen CNN tabanlı bir yöntemle 99% doğruluk oranında sınıflandırılma gerçekleştirilmiştir.
dc.description It is difficult to constantly control apple trees in farmland. In case of a disease on tree leaves, the risk of disease transmission to other leaves is high. It is necessary to prevent further deterioration of the plant by performing automatic detection of the disease in the early period. If the disease detection is delayed, the planned production cannot be realized. It is too late if diseases are detected by a farmer or agronomist. In addition, as the agricultural lands grow, the number of experts needed increases accordingly. For these reasons, leaf images of apple trees are grouped into 4 different classes: apple peel, leaf rust, healthy apple and multiple disease states. In the proposed method, noise removal in the images, detection of the relevant area and histogram equalization on the YUV color space are performed. Due to the unbalanced class distribution in the data set used, data augmentation was applied for the minority classes with the SMOTE method. Afterwards, features are extracted using pre-trained network models DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50V2. Extracted features were classified with a CNN-based method developed with an accuracy of 99%.
dc.format application/pdf
dc.language en
dc.publisher Süleyman Demirel Üniversitesi
dc.publisher Süleyman Demirel University
dc.relation https://dergipark.org.tr/tr/download/article-file/1916039
dc.source Volume: 9, Issue: 4 1130-1140 en-US
dc.source 1308-6693
dc.source Mühendislik Bilimleri ve Tasarım Dergisi
dc.subject Konvolüsyonel Sinir Ağı,Görüntü İşleme,Elma Yaprağı,SMOTE,Önceden Eğitilmiş Ağ
dc.subject Convolutional Neural Network,Image Processing,Apple leaf,SMOTE,Pre Trained Network.
dc.title ÖNERİLEN KONVOLÜSYON SİNİR AĞI YAKLAŞIMI KULLANARAK ELMA YAPRAĞI HASTALIKLARININ SINIFLANDIRILMASI tr-TR
dc.title CLASSIFICATION OF APPLE LEAF DISEASES USING THE PROPOSED CONVOLUTION NEURAL NETWORK APPROACH en-US
dc.type info:eu-repo/semantics/article
dc.citation Annabel, L. S. P., Annapoorani, T., & Deepalakshmi, P. (2019). Machine Learning for Plant Leaf Disease Detection and Classification – A Review. 2019 International Conference on Communication and Signal Processing (ICCSP), 538–542. https://doi.org/10.1109/ICCSP.2019.8698004
dc.citation Aurangzeb, K., Akmal, F., Khan, M. A., Sharif, M., & Javed, M. Y. (2020). Advanced Machine Learning Algorithm Based System for Crops Leaf Diseases Recognition. 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), 146–151. https://doi.org/10.1109/CDMA47397.2020.00031
dc.citation Bansal, P., Kumar, R., & Kumar, S. (2021). Disease Detection in Apple Leaves Using Deep Convolutional Neural Network. In Agriculture (Vol. 11, Issue 7). https://doi.org/10.3390/agriculture11070617
dc.citation Deng, X., Xu, D., Zeng, M., & Qi, Y. (2019). Does Internet use help reduce rural cropland abandonment? Evidence from China. Land Use Policy, 89, 104243. https://doi.org/10.1016/j.landusepol.2019.104243
dc.citation Divakar, S., Bhattacharjee, A., & Priyadarshini, R. (2021). Smote-DL: A Deep Learning Based Plant Disease Detection Method. 2021 6th International Conference for Convergence in Technology (I2CT), 1–6. https://doi.org/10.1109/I2CT51068.2021.9417920
dc.citation Dubey, S. R., & Jalal, A. S. (2016). Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing, 10(5), 819–826. https://doi.org/10.1007/s11760-015-0821-1
dc.citation Duralija, B., Putnik, P., Brdar, D., Bebek Markovinović, A., Zavadlav, S., Pateiro, M., Domínguez, R., Lorenzo, J. M., & Bursać Kovačević, D. (2021). The Perspective of Croatian Old Apple Cultivars in Extensive Farming for the Production of Functional Foods. In Foods (Vol. 10, Issue 4). https://doi.org/10.3390/foods10040708
dc.citation Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009
dc.citation Gargade, A., & Khandekar, S. A. (2019). A Review: Custard Apple Leaf Parameter Analysis and Leaf Disease Detection using Digital Image Processing. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), 267–271. https://doi.org/10.1109/ICCMC.2019.8819867
dc.citation Gobalakrishnan, N., Pradeep, K., Raman, C. J., Ali, L. J., & Gopinath, M. P. (2020). A Systematic Review on Image Processing and Machine Learning Techniques for Detecting Plant Diseases. 2020 International Conference on Communication and Signal Processing (ICCSP), 465–468. https://doi.org/10.1109/ICCSP48568.2020.9182046
dc.citation Gollapudi, S. (2019). Learn Computer Vision Using OpenCV. https://doi.org/10.1007/978-1-4842-4261-2
dc.citation Han, H., Xiong, J., & Zhao, K. (2021). Digital inclusion in social media marketing adoption: the role of product suitability in the agriculture sector. Information Systems and E-Business Management. https://doi.org/10.1007/s10257-021-00522-7
dc.citation He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
dc.citation Hoang, N.-D., & Nguyễn Quốc, L. (2018). Metaheuristic Optimized Edge Detection for Recognition of Concrete Wall Cracks: A Comparative Study on the Performances of Roberts, Prewitt, Canny, and Sobel Algorithms. Advances in Civil Engineering, 2018, 1–16. https://doi.org/10.1155/2018/7163580
dc.citation Hou, J., Huo, X., & Yin, R. (2019). Does computer usage change farmers’ production and consumption? Evidence from China. China Agricultural Economic Review, 11(2), 387–410. https://doi.org/10.1108/CAER-09-2016-0149
dc.citation Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
dc.citation Iqbal, Z., Khan, M. A., Sharif, M., Shah, J. H., ur Rehman, M. H., & Javed, K. (2018). An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and Electronics in Agriculture, 153, 12–32. https://doi.org/10.1016/j.compag.2018.07.032
dc.citation Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., & Nappi, M. (2021). Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques. IEEE Access, 9, 39707–39716. https://doi.org/10.1109/ACCESS.2021.3064084
dc.citation Khan, M. A., Akram, T., Sharif, M., & Saba, T. (2020). Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection. Multimedia Tools and Applications, 79(35), 25763–25783. https://doi.org/10.1007/s11042-020-09244-3
dc.citation Liang, Q., Xiang, S., Hu, Y., Coppola, G., Zhang, D., & Sun, W. (2019). PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network. Computers and Electronics in Agriculture, 157, 518–529. https://doi.org/10.1016/j.compag.2019.01.034
dc.citation Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
dc.citation Ni, A., Huang, L., & Xiong, F. (2021). A new perspective of innovation-driven agricultural sustainable development: a case of China. IOP Conference Series: Earth and Environmental Science, 667, 12096. https://doi.org/10.1088/1755-1315/667/1/012096
dc.citation Prashar, K., Talwar, R., & Kant, C. (2017). Robust Automatic Cotton Crop Disease Recognition (ACDR) Method using the Hybrid Feature Descriptor with SVM.
dc.citation Shi, Y., Wang, X. F., Zhang, S. W., & Zhang, C. L. (2015). PNN based crop disease recognition with leaf image features and meteorological data. International Journal of Agricultural and Biological Engineering, 8, 60–68. https://doi.org/10.3965/j.ijabe.20150804.1719
dc.citation Shrivastava, G. (2021). Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning. International Journal of Computer Applications, 174. https://doi.org/10.5120/ijca2021920990
dc.citation Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41–49. https://doi.org/10.1016/j.inpa.2016.10.005
dc.citation Sottocornola, G., Stella, F., & Zanker, M. (2021). Counterfactual Contextual Multi-Armed Bandit: a Real-World Application to Diagnose Apple Diseases.
dc.citation Sujatha, R., Chatterjee, J. M., Jhanjhi, N. Z., & Brohi, S. N. (2021). Performance of deep learning vs machine learning in plant leaf disease detection. Microprocessors and Microsystems, 80, 103615. https://doi.org/10.1016/j.micpro.2020.103615
dc.citation Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Thirty-First AAAI Conference on Artificial Intelligence.
dc.citation Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. B. (2016). Rethinking the Inception Architecture for Computer Vision. https://doi.org/10.1109/CVPR.2016.308
dc.citation Tahir, M. Bin, Khan, M. A., Javed, K., Kadry, S., Zhang, Y.-D., Akram, T., & Nazir, M. (2021). Recognition of Apple Leaf Diseases using Deep Learning and Variances-Controlled Features Reduction. Microprocessors and Microsystems, 104027. https://doi.org/10.1016/j.micpro.2021.104027
dc.citation Thapa, R., Zhang, K., Snavely, N., Belongie, S., & Khan, A. (2020). The Plant Pathology Challenge 2020 data set to classify foliar disease of apples. Applications in Plant Sciences, 8(9), e11390. https://doi.org/10.1002/aps3.11390
dc.citation Tiwari, D., Ashish, M., Gangwar, N., Sharma, A., Patel, S., & Bhardwaj, S. (2020). Potato Leaf Diseases Detection Using Deep Learning. https://doi.org/10.1109/ICICCS48265.2020.9121067
dc.citation Turkoglu, M., Hanbay, D., & Sengur, A. (2019). Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01591-w
dc.citation Wang, G., Sun, Y., & Wang, J. (2017). Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning. Computational Intelligence and Neuroscience, 2017, 2917536. https://doi.org/10.1155/2017/2917536
dc.citation Zhang, S., Huang, W., & Zhang, C. (2019). Three-channel convolutional neural networks for vegetable leaf disease recognition. Cognitive Systems Research, 53, 31–41. https://doi.org/10.1016/j.cogsys.2018.04.006
dc.citation Zhu, X., Hu, R., Zhang, C., & Shi, G. (2021). Does Internet use improve technical efficiency? Evidence from apple production in China. Technological Forecasting and Social Change, 166, 120662. https://doi.org/10.1016/j.techfore.2021.120662


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account