Using Deep Learning for Image-Based Potato Tuber Disease Detection

Author:

Oppenheim Dor1,Shani Guy2ORCID,Erlich Orly3,Tsror Leah3

Affiliation:

1. Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel;

2. Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel; and

3. Department of Plant Pathology, Institute of Plant Protection, Agricultural Research Organization, Gilat Center, M.P. Negev, Israel

Abstract

Many plant diseases have distinct visual symptoms, which can be used to identify and classify them correctly. This article presents a potato disease classification algorithm that leverages these distinct appearances and 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: namely, four disease classes and a healthy potato class. The database of images used in this study, containing potato tubers of different cultivars, sizes, and diseases, was acquired, classified, and labeled 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. The models were tested over a data set of images taken using standard low-cost RGB (red, green, and blue) sensors and were tagged by experts, demonstrating high classification accuracy. This is the first article to report the successful implementation of deep convolutional networks, popular in object identification, to the task of disease identification in potato tubers, showing the potential of deep learning techniques in agricultural tasks.

Publisher

Scientific Societies

Subject

Plant Science,Agronomy and Crop Science

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