کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
11029532 | 1646502 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Detection of passion fruits and maturity classification using Red-Green-Blue Depth images
ترجمه فارسی عنوان
شناسایی میوه های شور و بلوغ با استفاده از تصاویر عمیق قرمز-سبز-آبی
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
چکیده انگلیسی
A machine vision algorithm was developed to detect passion fruits and identify maturity of the detected fruits using natural outdoor RGB-D images. As different passion fruits on the same branch can be in different maturity stages, detection and maturity classification on a complex background are very important for yield mapping and development of intelligent mobile fruit-picking robots. In this study, a Kinect sensor was used for data acquisition, and maturity stages of the fruits were divided into five categories: young (Y), near-young (NY), near-mature (NM), mature (M) and after-mature (AM). The algorithm involved two stages. First, by colour and depth images, passion fruits were detected using faster region-based convolutional neural networks (Faster R-CNN), and colour-based detection was integrated with depth-based detection for improving detection performance. Second, for each detected fruit region, the dense scale invariant features transform (DSIFT) algorithm combined with locality-constrained linear coding (LLC) was used to extract and represent the features of fruit maturity from R, G, and B channels, respectively. In addition, the RGB-DSIFT-LLC features were input into a linear support vector machine (SVM) classifier for identifying the maturity of fruits. By conducting an experimental study on a special dataset, we verified that the proposed method achieves 92.71% detection accuracy and 91.52% maturity classification accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biosystems Engineering - Volume 175, November 2018, Pages 156-167
Journal: Biosystems Engineering - Volume 175, November 2018, Pages 156-167
نویسندگان
Shuqin Tu, Yueju Xue, Chan Zheng, Yu Qi, Hua Wan, Liang Mao,