Automatically detection of fruits on trees using deep learning-based object detection methods
سال انتشار: 1399
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 288
فایل این مقاله در 6 صفحه با فرمت PDF قابل دریافت می باشد
- صدور گواهی نمایه سازی
- من نویسنده این مقاله هستم
این مقاله در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ELEMECHCONF06_235
تاریخ نمایه سازی: 22 آذر 1399
چکیده مقاله:
Automatic detection of fruits on trees is a step towards increasing the productivity of production and estimating the yield of trees in agricultural management, including warehousing, number of workforce, category, etc. Automation is one of the harvest mechanization methods and machine vision has a pivotal role in this process. Therefore, it is essential to create an efficient model in natural daylight for fruit detection. In this study, 134 colorful images of orange trees has been employed. Since light conditions, fruit overlapping and clustering are the most important challenges in locating and detecting fruits in the garden, photography has been done in cloudy weather and at the night to solve the unfavorable sunlight problem. Then, three models of deep learning methods were employed to identify oranges. These models are: Faster RCNN ResNet101 ،SSD Inception V2 and Faster RCNN Inception V2. The Faster RCNN ResNet101 outperforms the others at 95.4% of mAP.
کلیدواژه ها:
نویسندگان
Parvin Roosta
Member of the faculty of Pishtazan-e Shiraz Higher Education Institute, Shiraz, Iran
Mehdi Hazrati Fard
Master of Computer Engineering at the Pishtazan-e Shiraz Higher Education Institute, Shiraz ,Iran