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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: [email protected]
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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 

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