Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T14:08:29.307Z Has data issue: false hasContentIssue false
  • English
  • Français

Potato Disease Classification Using Convolution Neural Networks

Published online by Cambridge University Press:  01 June 2017

D. Oppenheim  and
G. Shani
D. Oppenheim*
Affiliation:
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel
G. Shani
Affiliation:
Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel
*
†E-mail: doropp@post.bgu.ac.il
Get access

Abstract

Many plant diseases have distinct visual symptoms which can be used to identify and classify them correctly. This paper presents a potato disease classification algorithm which leverages these distinct appearances and the recent advances in computer vision made possible by deep learning. The algorithm uses a deep convolutional neural network training it to classify the tubers into five classes, four diseases classes and a healthy potato class. The database of images used in this study, containing potatoes of different shapes, sizes and diseases, was acquired, classified, and labelled manually by experts. The models were trained over different train-test splits to better understand the amount of image data needed to apply deep learning for such classification tasks.

Keywords

Plant disease detection and classificationComputer visionConvolutional neural networkPotato diseases
Type
Crop Protection
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 244 - 249
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Masoud, A and Nahavandi, S 2016. Multi-Residual Networks: Improving the Speed and Accuracy of Residual Networks, https://arxiv.org/abs/1609.05672.Google Scholar
Chatfield, K, Simonyan, K, Vedaldi, A and Zisserman, A 2014. Return of the Devil in the Details: Delving Deep into Convolutional Nets. https://arxiv.org/abs/1405.3531.Google Scholar
Cubero, S, Lee, SW, Aleixos, N, Albert, F and Blasco, J 2016. Automated Systems Based on Machine Vision for Inspecting Citrus Fruits from the Field to Postharvest—a Review. Food and Bioprocess Technology 1–17.Google Scholar
Garcia, J and Barbedo, A 2016. A Review on the Main Challenges in Automatic Plant Disease Identification Based on Visible Range Images. Biosystems Engineering 144, 52–60.Google Scholar
Gongal, A, Amatya, S, Karkee, M, Zhang, Q and Lewis, K 2015. Sensors and Systems for Fruit Detection and Localization: A Review. Computers and Electronics in Agriculture 116, 8–19.Google Scholar
Krizhevsky, A, Sutskever, I and Hinton, GE 2012. ImageNet Classification with Deep Convolutional Neural Networks. Advances In Neural Information Processing Systems 1–9.Google Scholar
Mohanty, SP, Hughes, D and Salathé, M 2016. Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science 7.Google Scholar
Pareek, S 2016. Postharvest Ripening Physiology of Crops. CRC Press. Taylor and Francis, FL, USA.Google Scholar
Rich, AE 2013. Potato Diseases. Elsevier Science. Academic Press, New York, USA.Google Scholar
Sa, I, Ge, Z, Dayoub, F, Upcroft, B, Perez, T and McCool, C 2016. DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors 16 (8), 1222.Google Scholar
Sankaran, S, Mishra, A, Ehsani, R and Davis, C 2010. A Review of Advanced Techniques for Detecting Plant Diseases. Computers and Electronics in Agriculture 72 (1), 1–13.Google Scholar
Sermanet, P, Eigen, D, Zhang, X, Mathieu, M, Fergus, R and LeCun, Y 2013. OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks. https://arxiv.org/abs/1312.6229.Google Scholar
Tan, W, Zhao, C and Wu, H 2015. Intelligent Alerting for Fruit-Melon Lesion Image Based on Momentum Deep Learning. Multimedia Tools and Applications 75 (24), 16741–16761.Google Scholar
Taylor, RJ, Pasche, JS and Gudmestad, NC 2008. Susceptibility of Eight Potato Cultivars to Tuber Infection by Phytophthora Erythroseptica and Pythium Ultimum and Its Relationship to Mefenoxam-Mediated Control of Pink Rot and Leak. Annals of Applied Biology 152 (2), 189–199.Google Scholar
Zhang, B, Huang, W, Jiangbo, L, Zhao, C, Fan, S, Wu, J and Liu, C 2014. Principles, Developments and Applications of Computer Vision for External Quality Inspection of Fruits and Vegetables: A Review. Food Research International 62, 326–343.CrossRefGoogle Scholar