Powdery Mildew Detection in Hazelnut with Deep Learning

Abstract

Hazelnut cultivation is widely practiced in our country. One of the major problems in hazelnut cultivation is powdery mildew disease on hazelnut tree leaves. In this study, the early detection of powdery mildew disease with the YOLO model based on machine learning was tested on a unique data set. Object detection on the image, which is widely applied in the detection of plant diseases, has been applied for the detection of powdery mildew diseases. According to the results obtained, it has been seen that powdery mildew disease can be detected on the image. In the network trained with the Yolov5 model, diseased areas were detected with 95% accuracy in leaf images containing many diseases. Detection of healthy leaves, on the other hand, was tried on images with complex backgrounds and could detect more than one leaf on an image with 85% accuracy. The Yolov5 model, which has been used in many studies for disease detection on plant leaves, also gave effective results for the detection of powdery mildew disease on hazelnut leaves. Early detection of powdery mildew with a method based on machine learning; will stop the possible spread of disease; It will increase the efficiency of hazelnut production by preventing the damage of hazelnut producers.

Keywords

• Powdery mildew disease in hazelnut
• Powdery mildew disease detection
• Object detection with Yolo
• Leaf disease
• Leaf disease detection

References

1. Anonim. Fındıkta Külleme. 2019 [cited 2022; Available from: https://arastirma.tarimorman.gov.tr/findik/Belgeler/Sol%20Men%C3%BC/ E%C4%9Ftim%20ve%20Yay%C4%B1m/%C3%87ift%C3%A7i%20E%C4%9Fitim/K%C3%.
2. Erdoğan, V., Fındık: Yetiştiricilik, Sorunlar, Öneriler ve Yenilikler. Türktob Dergisi, 2018. 27: p. 4-10.
3. Kurt, Ş., Bitki fungal hastalıkları. Akademisyen Kitap Evi, 2013.
4. Mohammadpoor, M., M.G. Nooghabi, and Z. Ahmedi, An Intelligent Technique for Grape Fanleaf Virus Detection. Int. J. Interact. Multim. Artif. Intell., 2020. 6(1): p. 62-67.
5. Zhang, S., et al., Cucumber leaf disease identification with global pooling dilated convolutional neural network. Computers and Electronics in Agriculture, 2019. 162: p. 422-430.
6. Liang, W.-j., et al., Rice blast disease recognition using a deep convolutional neural network. Scientific reports, 2019. 9(1): p. 1-10.
7. Wagh, T.A., et al., Grapes leaf disease detection using convolutional neural network. Int. J. Comput. Appl, 2019. 178(20).
8. Petrellis, N. A smart phone image processing application for plant disease diagnosis. in 2017 6th international conference on modern circuits and systems technologies (MOCAST). 2017. IEEE.

9. Mohanty, S.P., D.P. Hughes, and M. Salathé, Using deep learning for image-based plant disease detection. Frontiers in plant science, 2016. 7: p. 1419.
10. Singh, V. and A.K. Misra, Detection of plant leaf diseases using image segmentation and soft computing techniques. Information processing in Agriculture, 2017. 4(1): p. 41-49.
11. Yao, J., et al., A real-time detection algorithm for Kiwifruit defects based on YOLOv5. Electronics, 2021. 10(14): p. 1711.
12. Boyar, T., Powdery Mildew Disease on Hazelnut Leaves, in Leaf Disease on Hazelnut, T. BOYAR, Editor. 2022: https://drive.google. com/drive/folders/1iUNSnbPR9MFvehne1cHXcDE93pCJ2LdZ?us p=sharing.
13. Brin, L.P.S. Google. 1998; Available from: https://www.google.com. tr/.
14. Steve Chen, C.H., and Jawed Kari. Youtube. 2005; Available from: https://www.youtube.com.
15. SEZER, A., et al., Erysiphe corylacearum’un neden olduğu külleme hastalığına karşı Giresun ili fındık üretim alanlarında kimyasal mücadele olanaklarının belirlenmesi. Akademik Ziraat Dergisi. 8(Özel Sayı): p. 71-78.
16. Jocher, G. YOLOv5. 2020; Available from: https://ultralytics.com/yolov5.
17. Kubat, M., Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 1999. 13(4): p. 409-412.
18. Akkoyun, S., N. Yildiz, and H. Kaya, Neural Network Estimation for Attenuation Coefficients for Gamma-Ray Angular Distribution. Physics of Particles and Nuclei Letters, 2019. 16(4): p. 397-401.
19. Demirbay, B. and A.B. KARAKULLUKÇU, Artificial neural network (ANN) approach for dynamic viscosity of aqueous gelatin solutions: a soft computing study. Avrupa Bilim ve Teknoloji Dergisi, 2020(18): p. 465-475.
20. LeCun, Y., et al., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. 86(11): p. 2278-2324.
21. Ruan, J., Design and Implementation of Target Detection Algorithm Based on YOLO. Beijing University of Posts and Telecommunications: Beijing, China, 2019.
22. Krizhevsky, A., I. Sutskever, and G.E. Hinton, Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 2012. 25.
23. Gorban, A.N., E.M. Mirkes, and I.Y. Tyukin, How deep should be the depth of convolutional neural networks: a backyard dog case study. Cognitive Computation, 2020. 12(2): p. 388-397.
24. Tang, Y., Exponential stability of pseudo almost periodic solutions for fuzzy cellular neural networks with time-varying delays. Neural Processing Letters, 2019. 49(2): p. 851-861.
25. Arif, I., W. Aslam, and Y. Hwang, Barriers in adoption of internet banking: A structural equation modeling-Neural network approach. Technology in Society, 2020. 61: p. 101231.
26. Wu, W., et al., Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image. PloS one, 2021. 16(10): p. e0259283.
27. Fang, Y., et al., Accurate and Automated Detection of Surface Knots on Sawn Timbers Using YOLO-V5 Model. BioResources, 2021. 16(3).