Detection of Apple Leaf Diseases using Faster R-CNN

Abstract

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.

Keywords

• Convolutional neural network (CNN),Faster R-CNN,Leaf disease detection

Faster R-CNN Kullanarak Elma Yaprağı Hastalıklarının Tespiti

Abstract

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.

Keywords

• Evrişimsel sinir ağı (ESA),Daha hızlı bölgesel-ESA,Yaprak hastalığı tespiti

References

1. [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. [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. [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. [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. [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. [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. [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. [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. [9] K. Simonyan, and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Int. Conf. Learn. Represent, pp. 1-14, 2015.
10. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [22] R. Girshick, “Fast r-cnn,” In Proceedings of the IEEE international conference on computer vision, pp. 1440-1448, 2015.
23. [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. [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.