سال انتشار:
2019
عنوان انگلیسی مقاله:
Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms
ترجمه فارسی عنوان مقاله:
تشخیص خودکار و طبقه بندی بیماری لکه برگ در چغندرقند با استفاده از الگوریتم های یادگیری عمیق
منبع:
Sciencedirect - Elsevier - Physica A, 535 (2019) 122537: doi:10:1016/j:physa:2019:122537
نویسنده:
Mehmet Metin Ozguven a, Kemal Ademb,∗
چکیده انگلیسی:
Depending on the severity of the leaf spot disease in the field, it can cause a loss
in sugar yield by 10% to 50%. Therefore, disease symptoms should be detected ontime
and relevant measures should be taken instantly to prevent further spread or
progress of the disease. In this study, an Updated Faster R-CNN architecture developed by
changing the parameters of a CNN model and a Faster R-CNN architecture for automatic
detection of leaf spot disease (Cercospora beticola Sacc.) in sugar beet were proposed.
The method, proposed for the detection of disease severity by imaging-based expert
systems, was trained and tested with 155 images and according to the test results,
the overall correct classification rate was found to be 95.48%. In addition, the proposed
approach showed that changes in CNN parameters according to the image and regions
to be detected could increase the success of Faster R-CNN architecture. The proposed
approach yielded better outcomes for relevant parameters than the modern methods
specified in previous literature. Therefore, it is believed that the method will reduce the
time spent in diagnosis of sugar beet leaf spot disease in the large production areas as
well as reducing the human error and time to identify the severity and course of the
disease.
Keywords: Sugar beet | Leaf spot disease | CNN | Faster R-CNN
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 0