Fast Location and Recognition of Green Apple Based on RGB-D Image

Abstract

In the process of green apple harvesting or yield estimation, affected by the factors, such as fruit color, light, and orchard environment, the accurate recognition and fast location of the target fruit brings tremendous challenges to the vision system. In this article, we improve a density peak cluster segmentation algorithm for RGB images with the help of a gradient field of depth images to locate and recognize target fruit. Specifically, the image depth information is adopted to analyze the gradient field of the target image. The vorticity center and two-dimensional plane projection are constructed to realize the accurate center location. Next, an optimized density peak clustering algorithm is applied to segment the target image, where a kernel density estimation is utilized to optimize the segmentation algorithm, and a double sort algorithm is applied to efficiently obtain the accurate segmentation area of the target image. Finally, the segmentation area with the circle center is the target fruit area, and the maximum value method is employed to determine the radius. The above two results are merged to achieve the contour fitting of the target fruits. The novel method is designed without iteration, classifier, and several samples, which has greatly improved operating efficiency. The experimental results show that the presented method significantly improves accuracy and efficiency. Meanwhile, this new method deserves further promotion.

Performance evaluation of newly released cameras for fruit detection and localization in complex kiwifruit orchard environments

“…Machine vision systems have been extensively used in sustainable production process of modern agriculture, which enables detection and localization of target fruits, such as automatic fruit harvesting, orchard yield prediction, orchard yield mapping, and fruit growth monitoring (Fu, Feng, et al, 2020; Sun et al, 2022). Detection and localization of target fruits are certainly important components during the process of automatic fruit harvesting.…”

Detection and Positioning of Camellia oleifera Fruit Based on LBP Image Texture Matching and Binocular Stereo Vision

“…The RGB-D camera is widely used because of its mature technology, perfect program interface, and easy to obtain depth information [14][15][16][17][18]. Lin et al used the RGB-D sensor to detect guava fruit and estimate its pose in real outdoor conditions [19].…”