دورية أكاديمية
Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector
| العنوان: | Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector |
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| المؤلفون: | Sinan Uğuz |
| المصدر: | Sakarya University Journal of Computer and Information Sciences, Vol 3, Iss 3, Pp 158-168 (2020) |
| بيانات النشر: | Sakarya University, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Electronic computers. Computer science LCC:Information technology |
| مصطلحات موضوعية: | zeytin halkalı leke, single shot detector, derin öğrenme, nesne tespiti, olive peacock spot, deep learning, object detection, Electronic computers. Computer science, QA75.5-76.95, Information technology, T58.5-58.64 |
| الوصف: | Among the artificial intelligence based studies conducted in the field of agriculture, disease recognition methods founded on deep learning are observed to become widespread. Due to the diversity and regional specificity of many plant species, studies performed in this field are not at the desired level. Olive peacock spot disease of the olive plant which grows only in certain regions in the world is a widely encountered disease particularly in Turkey. The aim of this research is to develop an olive peacock spot disease detection system using a Single Shot Detector (SSD) which is one the popular deep learning architectures to support olive farmers. This study presents a data set consisting of 1460 olive leaves samples for the detection of olive peacock spot disease. All of the images of the olive leaves which produced under controlled conditions were collected from Aegean region of Turkey during spring and summer. The data set was trained with different intersection over union (IoU) threshold values using SSD architecture. A 96 % average precision (AP) value was obtained with IoU=0.5. As IOU value goes up from 0.5, erroneously classified olive peacock spot disease symptoms growed larger as well. The AP curve becomes flat when between 0.1 and 0.5, and it decreases when greater than 0.5. This analysis showed that the IoU significantly influenced the performance of SSD based model in detection of olive peacock spot disease. In addition to, trainings were performed by employing Pytorch library and a GUI was developed for the SSD based application using PyQt5 which is one of Pyhton's libraries. Results showed that the SSD was a robust tool for recognizing the olive peacock spot disease. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2636-8129 |
| Relation: | https://dergipark.org.tr/tr/download/article-file/1159417; https://doaj.org/toc/2636-8129 |
| DOI: | 10.35377/saucis.03.03.755269 |
| URL الوصول: | https://doaj.org/article/3b95ed2e7a8b4878a6c9a95efc605c18 |
| رقم الأكسشن: | edsdoj.3b95ed2e7a8b4878a6c9a95efc605c18 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26368129 |
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| DOI: | 10.35377/saucis.03.03.755269 |