Araştırma Makalesi
BibTex RIS Kaynak Göster

Detection of Apple Leaf Diseases using Faster R-CNN

Yıl 2020, Cilt: 8 Sayı: 1, 1110 - 1117, 31.01.2020
https://doi.org/10.29130/dubited.648387

Öz

Image recognition-based automated disease detection systems play an
important role in the early detection of plant leaf diseases. In this study, an
apple leaf disease detection system was proposed using Faster Region-Based
Convolutional Neural Network (Faster R-CNN) with Inception v2 architecture. Applications
for the detection of diseases were carried out in apple orchards in Yalova,
Turkey. Leaf images were obtained from different apple orchards for two years. In
our observations, it was determined that apple trees of Yalova had black spot
(venturia inaequalis) disease. The proposed
system in the study detects a large number of leaves in an image, then
successfully classifies diseased and healthy ones. The disease detection system
trained has achieved an average of 84.5% accuracy.

Destekleyen Kurum

Research Fund of Yalova University

Proje Numarası

2018/AP/0001

Kaynakça

  • [1] E. Kiani and T. Mamedov, “Identification of Plant Disease Infection using Soft-Computing: Application to modern botany,” Procedia Computer Science, vol. 120, pp. 893-900, 2017.
  • [2] V. Singh and A.K. Misra, “Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques,” Information Processing in Agriculture, vol. 4, no. 1, pp. 41-49, 2017.
  • [3] K. Golhani, S.K. Balasundram, G. Vadamalai, and B. Pradhan, “A Review of Neural Networks in Plant Disease Detection using Hyperspectral Data,” Information Processing in Agriculture, vol. 5, no. 3, pp. 354-371, 2018.
  • [4] A. Kamilaris, and F.X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018.
  • [5] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” In Advances in neural information processing systems, pp. 1097-1105, 2012.
  • [6] K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” In European conference on computer vision, pp. 630-645, 2016.
  • [7] G. Huang, K.Q. Weinberger, and L. Van Der Maaten, “Densely Connected Convolutional Networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
  • [8] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.
  • [9] K. Simonyan, and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Int. Conf. Learn. Represent, pp. 1-14, 2015.
  • [10] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142-158, 2015.
  • [11] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Realtime Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149, 2017.
  • [12] K.P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018.
  • [13] G. Hu, X. Yang, Y. Zhang, and M. Wan, “Identification of Tea Leaf Diseases by using an Improved Deep Convolutional Neural Network,” Sustainable Computing: Informatics and Systems, 100353, 2019.
  • [14] G. Geetharamani, and A. Pandian, “Identification of Plant Leaf Diseases using a Nine-layer Deep Convolutional Neural Network,” Computers & Electrical Engineering, vol. 76, pp. 323-338, 2019.
  • [15] A.K. Rangarajan, R. Purushothaman, and A. Ramesh, “Tomato Crop Disease Classification using Pre-trained Deep Learning Algorithm,” Procedia Computer Science, vol. 133, pp. 1040-1047, 2018.
  • [16] M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” In 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, pp. 382-385, 2018.
  • [17] E.C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A Comparative Study of Fine-tuning Deep Learning Models for Plant Disease Identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272-279, 2019.
  • [18] M.M. Ozguven, and K. Adem, “Automatic Detection and Classification of Leaf Spot Disease in Sugar Beet using Deep Learning Algorithms,” Physica A: Statistical Mechanics and its Applications, vol. 535, 122537, 2019.
  • [19] H. Huang, et al., “Faster R-CNN for Marine Organisms Detection and Recognition using Data Augmentation,” Neurocomputing, vol. 337, pp. 372-384, 2019.
  • [20] L. Quan, et al. “Maize Seedling Detection under Different Growth Stages and Complex Field Environments Based on an Improved Faster R–CNN,” Biosystems Engineering, vol. 184, pp. 1-23, 2019.
  • [21] X. Lei, and Z. Sui, “Intelligent Fault Detection of High Voltage Line Based on the Faster R-CNN,” Measurement, vol. 138, pp. 379-385, 2019.
  • [22] R. Girshick, “Fast r-cnn,” In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
  • [23] V. Kafedziski, S. Pecov, and D. Tanevski, “Detection and Classification of Land Mines from Ground Penetrating Radar Data Using Faster R-CNN,” In 2018 26th Telecommunications Forum (TELFOR), IEEE, pp. 1-4, 2018.
  • [24] A. Alamsyah, M.A.A. Saputra, and R.A. Masrury, “Object Detection Using Convolutional Neural Network To Identify Popular Fashion Product,” In Journal of Physics: Conference Series, vol. 1192, no. 1, pp. 012040, 2019.

Faster R-CNN Kullanarak Elma Yaprağı Hastalıklarının Tespiti

Yıl 2020, Cilt: 8 Sayı: 1, 1110 - 1117, 31.01.2020
https://doi.org/10.29130/dubited.648387

Öz

Görüntü tanıma tabanlı otomatik hastalık algılama
sistemleri, bitkilerde görülen yaprak hastalıklarının erken tespitinde önemli
bir rol oynamaktadır. Bu çalışmada, Inception v2 mimarisi ile Daha Hızlı
Bölgesel Evrişimsel Sinir Ağı (Faster R-CNN) kullanılarak bir elma yaprağı
hastalığı tespit sistemi önerilmiştir. Hastalıkların tespiti için uygulamalar Türkiye’nin
Yalova ilindeki elma bahçelerinde gerçekleştirilmiştir. Yaprak görüntüleri iki
yıl boyunca farklı elma bahçelerinden elde edilmiştir. Yaptığımız gözlemlerde
Yalova'nın elma ağaçlarında özellikle kara leke hastalığının olduğu tespit
edilmiştir. Çalışmada önerilen sistem bir görüntü içerisindeki çok fazla sayıda
bulunan yaprakları tespit etmekte, ardından hastalıklı ve sağlıklı olanları
başarılı bir şekilde sınıflandırmaktadır. Eğitilen hastalık tespit sistemi
ortalama %84.5 doğruluk elde etmiştir.

Proje Numarası

2018/AP/0001

Kaynakça

  • [1] E. Kiani and T. Mamedov, “Identification of Plant Disease Infection using Soft-Computing: Application to modern botany,” Procedia Computer Science, vol. 120, pp. 893-900, 2017.
  • [2] V. Singh and A.K. Misra, “Detection of Plant Leaf Diseases using Image Segmentation and Soft Computing Techniques,” Information Processing in Agriculture, vol. 4, no. 1, pp. 41-49, 2017.
  • [3] K. Golhani, S.K. Balasundram, G. Vadamalai, and B. Pradhan, “A Review of Neural Networks in Plant Disease Detection using Hyperspectral Data,” Information Processing in Agriculture, vol. 5, no. 3, pp. 354-371, 2018.
  • [4] A. Kamilaris, and F.X. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018.
  • [5] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” In Advances in neural information processing systems, pp. 1097-1105, 2012.
  • [6] K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” In European conference on computer vision, pp. 630-645, 2016.
  • [7] G. Huang, K.Q. Weinberger, and L. Van Der Maaten, “Densely Connected Convolutional Networks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
  • [8] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.
  • [9] K. Simonyan, and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Int. Conf. Learn. Represent, pp. 1-14, 2015.
  • [10] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-Based Convolutional Networks for Accurate Object Detection and Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 38, no. 1, pp. 142-158, 2015.
  • [11] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Realtime Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149, 2017.
  • [12] K.P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018.
  • [13] G. Hu, X. Yang, Y. Zhang, and M. Wan, “Identification of Tea Leaf Diseases by using an Improved Deep Convolutional Neural Network,” Sustainable Computing: Informatics and Systems, 100353, 2019.
  • [14] G. Geetharamani, and A. Pandian, “Identification of Plant Leaf Diseases using a Nine-layer Deep Convolutional Neural Network,” Computers & Electrical Engineering, vol. 76, pp. 323-338, 2019.
  • [15] A.K. Rangarajan, R. Purushothaman, and A. Ramesh, “Tomato Crop Disease Classification using Pre-trained Deep Learning Algorithm,” Procedia Computer Science, vol. 133, pp. 1040-1047, 2018.
  • [16] M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” In 2018 3rd International Conference on Computer Science and Engineering (UBMK), IEEE, pp. 382-385, 2018.
  • [17] E.C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A Comparative Study of Fine-tuning Deep Learning Models for Plant Disease Identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272-279, 2019.
  • [18] M.M. Ozguven, and K. Adem, “Automatic Detection and Classification of Leaf Spot Disease in Sugar Beet using Deep Learning Algorithms,” Physica A: Statistical Mechanics and its Applications, vol. 535, 122537, 2019.
  • [19] H. Huang, et al., “Faster R-CNN for Marine Organisms Detection and Recognition using Data Augmentation,” Neurocomputing, vol. 337, pp. 372-384, 2019.
  • [20] L. Quan, et al. “Maize Seedling Detection under Different Growth Stages and Complex Field Environments Based on an Improved Faster R–CNN,” Biosystems Engineering, vol. 184, pp. 1-23, 2019.
  • [21] X. Lei, and Z. Sui, “Intelligent Fault Detection of High Voltage Line Based on the Faster R-CNN,” Measurement, vol. 138, pp. 379-385, 2019.
  • [22] R. Girshick, “Fast r-cnn,” In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
  • [23] V. Kafedziski, S. Pecov, and D. Tanevski, “Detection and Classification of Land Mines from Ground Penetrating Radar Data Using Faster R-CNN,” In 2018 26th Telecommunications Forum (TELFOR), IEEE, pp. 1-4, 2018.
  • [24] A. Alamsyah, M.A.A. Saputra, and R.A. Masrury, “Object Detection Using Convolutional Neural Network To Identify Popular Fashion Product,” In Journal of Physics: Conference Series, vol. 1192, no. 1, pp. 012040, 2019.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Melike Sardoğan Bu kişi benim 0000-0001-6946-2578

Yunus Özen 0000-0003-3225-8797

Adem Tuncer 0000-0001-7305-1886

Proje Numarası 2018/AP/0001
Yayımlanma Tarihi 31 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 8 Sayı: 1

Kaynak Göster

APA Sardoğan, M., Özen, Y., & Tuncer, A. (2020). Detection of Apple Leaf Diseases using Faster R-CNN. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 8(1), 1110-1117. https://doi.org/10.29130/dubited.648387
AMA Sardoğan M, Özen Y, Tuncer A. Detection of Apple Leaf Diseases using Faster R-CNN. DÜBİTED. Ocak 2020;8(1):1110-1117. doi:10.29130/dubited.648387
Chicago Sardoğan, Melike, Yunus Özen, ve Adem Tuncer. “Detection of Apple Leaf Diseases Using Faster R-CNN”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 8, sy. 1 (Ocak 2020): 1110-17. https://doi.org/10.29130/dubited.648387.
EndNote Sardoğan M, Özen Y, Tuncer A (01 Ocak 2020) Detection of Apple Leaf Diseases using Faster R-CNN. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 8 1 1110–1117.
IEEE M. Sardoğan, Y. Özen, ve A. Tuncer, “Detection of Apple Leaf Diseases using Faster R-CNN”, DÜBİTED, c. 8, sy. 1, ss. 1110–1117, 2020, doi: 10.29130/dubited.648387.
ISNAD Sardoğan, Melike vd. “Detection of Apple Leaf Diseases Using Faster R-CNN”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 8/1 (Ocak 2020), 1110-1117. https://doi.org/10.29130/dubited.648387.
JAMA Sardoğan M, Özen Y, Tuncer A. Detection of Apple Leaf Diseases using Faster R-CNN. DÜBİTED. 2020;8:1110–1117.
MLA Sardoğan, Melike vd. “Detection of Apple Leaf Diseases Using Faster R-CNN”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, c. 8, sy. 1, 2020, ss. 1110-7, doi:10.29130/dubited.648387.
Vancouver Sardoğan M, Özen Y, Tuncer A. Detection of Apple Leaf Diseases using Faster R-CNN. DÜBİTED. 2020;8(1):1110-7.

Cited By









DIFFERENT APPLICATION AREAS OF OBJECT DETECTION WITH DEEP LEARNING
Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi
Sevcan TURAN
https://doi.org/10.51513/jitsa.957371