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Identificator: A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry
Year:
2012
Authors :
אלעד, יגאל
;
.
פרימן, סטנלי
;
.
Volume :
84
Co-Authors:
Pertot, I., Research and Innovation Centre, Fondazione Edmund Mach, S. Michele all'Adige, Via Mach 1, 38010 TN, Italy
Kuflik, T., Information Systems Department, The University of Haifa, Mount Carmel, Haifa 31905, Israel
Gordon, I., Information Systems Department, The University of Haifa, Mount Carmel, Haifa 31905, Israel
Freeman, S., Department of Plant Pathology and Weed Research, The Volcani Center, ARO, Bet Dagan 50250, Israel
Elad, Y., Department of Plant Pathology and Weed Research, The Volcani Center, ARO, Bet Dagan 50250, Israel
Facilitators :
From page:
144
To page:
154
(
Total pages:
11
)
Abstract:
Identificator is a web-based tool used to help non experts in identifying plant diseases, based on the selection of pictures and/or short text descriptions (when no suitable images exist) representing the symptoms on a specific sample of plant organs. The system is based on a multi-access key of identification and specifically on the selection of pictures by the user and can be used remotely from a desktop as well as from a smart phone or personal digital assistant. The system was developed following a simple approach: visual identification where images and/or short descriptions are used to uniquely identify diseases when possible and suggest refining the visual identification process in cases of ambiguous identification. It has been designed in a way that allows easy definition of additional diseases by uploading the correct images and defining the identification rules and diseases. In this way the system may aid growers in identifying various diseases when using the system remotely while the system is developed and maintained centrally. This approach may ease the process of manual visual disease identification until machine vision technology is mature enough to perform this task automatically. We tested the system for visual identification of strawberry diseases using a computer and samples of infected plants. The evaluation showed that it is effective and accurate in enabling its users to identify strawberry diseases. © 2012 Elsevier B.V.
Note:
Related Files :
computer vision
Fruits
Personal digital assistants
Plant Disease
Plant organs
Plant pathogen
Sampling
symptom
עוד תגיות
תוכן קשור
More details
DOI :
10.1016/j.compag.2012.02.014
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
22149
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:49
Scientific Publication
Identificator: A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry
84
Pertot, I., Research and Innovation Centre, Fondazione Edmund Mach, S. Michele all'Adige, Via Mach 1, 38010 TN, Italy
Kuflik, T., Information Systems Department, The University of Haifa, Mount Carmel, Haifa 31905, Israel
Gordon, I., Information Systems Department, The University of Haifa, Mount Carmel, Haifa 31905, Israel
Freeman, S., Department of Plant Pathology and Weed Research, The Volcani Center, ARO, Bet Dagan 50250, Israel
Elad, Y., Department of Plant Pathology and Weed Research, The Volcani Center, ARO, Bet Dagan 50250, Israel
Identificator: A web-based tool for visual plant disease identification, a proof of concept with a case study on strawberry
Identificator is a web-based tool used to help non experts in identifying plant diseases, based on the selection of pictures and/or short text descriptions (when no suitable images exist) representing the symptoms on a specific sample of plant organs. The system is based on a multi-access key of identification and specifically on the selection of pictures by the user and can be used remotely from a desktop as well as from a smart phone or personal digital assistant. The system was developed following a simple approach: visual identification where images and/or short descriptions are used to uniquely identify diseases when possible and suggest refining the visual identification process in cases of ambiguous identification. It has been designed in a way that allows easy definition of additional diseases by uploading the correct images and defining the identification rules and diseases. In this way the system may aid growers in identifying various diseases when using the system remotely while the system is developed and maintained centrally. This approach may ease the process of manual visual disease identification until machine vision technology is mature enough to perform this task automatically. We tested the system for visual identification of strawberry diseases using a computer and samples of infected plants. The evaluation showed that it is effective and accurate in enabling its users to identify strawberry diseases. © 2012 Elsevier B.V.
Scientific Publication
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