مشخصات مقاله | |
ترجمه عنوان مقاله | تشخیص خودکار گوجه فرنگی رسیده واحد بر روی گیاهان با ترکیب شبکه عصبی پیچشی مبتنی بر منطقه (R-CNN) سریعتر و مجموعه فازی شهودی |
عنوان انگلیسی مقاله | Automatic Detection of Single Ripe Tomato on Plant Combining Faster R-CNN and Intuitionistic Fuzzy Set |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 14 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه IEEE |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
4.641 در سال 2018 |
شاخص H_index | 56 در سال 2019 |
شاخص SJR | 0.609 در سال 2018 |
شناسه ISSN | 2169-3536 |
شاخص Quartile (چارک) | Q2 در سال 2018 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | مهندسی کشاورزی، مهندسی کامپیوتر، مهندسی فناوری اطلاعات |
گرایش های مرتبط | علوم باغبانی، هوش مصنوعی، شبکه های کامپیوتری |
نوع ارائه مقاله |
ژورنال |
مجله / کنفرانس | دسترسی – IEEE Access |
دانشگاه | School of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China |
کلمات کلیدی | تشخیص گوجه فرنگی، یادگیری عمیق، تفریق پس زمینه، نظریه مجموعه فازی شهودی، تقسیم بندی کانتور |
کلمات کلیدی انگلیسی | Tomato detection, deep learning, background subtraction, intuitionistic fuzzy set theory (IFS), contour segmentation |
شناسه دیجیتال – doi |
https://doi.org/10.1109/ACCESS.2019.2949343 |
کد محصول | E13904 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Materials and Methods III. Results IV. Discussion and Conclusion V. Conclusion Authors Figures References |
بخشی از متن مقاله: |
Abstract
Fast and accurate detection of ripe tomatoes on plant, which replaces manual labor with a robotic vision-based harvesting system, is a challenging task. Tomatoes in adjacent positions are easily mistaken as a single tomato by image recognition methods. In this study, a ripe tomato detection method that combines deep learning with edge contour detection is proposed. Our approach efficiently separates target tomatoes from overlapping tomatoes to detect individual fruits. This approach yields several improvements. First, deep learning requires less time and extracts deeper features than traditional methods for assessing candidate ripe tomato regions. Second, we use Gaussian density function of H and S in the HSV color space to help segment tomato regions from the background, followed by erosion and dilation on the tomato body to separate adjacent tomatoes and remove peripheral subpixels from all detected ripe tomatoes. Third, an adaptive threshold intuitionistic fuzzy set (IFS) method was developed to identify the tomato’s edge, and it performs well in detecting blurred edges in overlapping regions. To improve the efficiency and stability of edge detection under natural conditions, we adopted an illumination adjustment algorithm for the tomato image before edge detection. As samples, we collected images showing tomatoes that were separated, adjacent, overlapped, and even shaded by leaves. The widths and heights of these tomato samples were calculated and analyzed to evaluate the detection performance of the proposed method. The root mean square error (RMSE) results for tomato width and height using the proposed method are 2.996 pixels and 3.306 pixels, respectively. The mean relative error percent (MRE%) values for horizontal and vertical center position shift are 0.261% and 1.179%, respectively. These results demonstrate that the proposed method improves tomato detection accuracy and that it can be further applied in the harvesting process of agricultural robots. Introduction Tomatoes are one of the most important and popular fruit crops. Tomatoes offer humans many essential and beneficial nutrients such as antioxidants and vitamins C and A. As tomato demand increases, tomatoes are increasingly grown in greenhouses. However, manual harvesting is time consuming and costly, and as China’s labor costs rise, the adoption of agricultural automation processes is inevitable. Such processes are of great significance for reducing agriculture labor costs and improving a country’s industrial structure. Therefore, it is necessary to develop automatic tomato pickers. Although most agricultural robots— fruit harvesting systems in particular—use computer vision to detect fruit targets, accurate fruit detection is a challenging research topic. It is difficult to develop a vision system that functions as intelligently as a human and can easily identify fruit, especially in the presence of overlapping fruits or large leaf occlusions. The performance of the robot’s visual system directly affects tomato picking and operational safety. Improving the recognition rate of the visual system can increase the locating accuracy of the robot arm. In this study, we mainly aimed to identify ripe tomatoes based on a vision system. Systems designed to count or harvest fruit require accurate detection schemes that can overcome challenges such as naturally occurring changes in illumination, shape, pose, color and viewpoint. |