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פותח על ידי קלירמאש פתרונות בע"מ -
Using Deep Learning for Image-Based Potato Tuber Disease Detection
Year:
2019
Source of publication :
Phytopathology
Authors :
ארליך, אורלי
;
.
צרור, לאה
;
.
Volume :
109
Co-Authors:

Oppenheim, D  - Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

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

Facilitators :
From page:
1083
To page:
1087
(
Total pages:
5
)
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.

Note:
Related Files :
Image recognition
spp.
tuber blemish diseases
עוד תגיות
תוכן קשור
More details
DOI :
10.1094/PHYTO-08-18-0288-R
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
42101
Last updated date:
02/03/2022 17:27
Creation date:
02/07/2019 14:17
Scientific Publication
Using Deep Learning for Image-Based Potato Tuber Disease Detection
109

Oppenheim, D  - Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

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

Using Deep Learning for Image-Based Potato Tuber Disease Detection

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.

Scientific Publication
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