Grape clusters manual#

Over the past 3 decades, the rapid advancement of digital image processing techniques has enabled applications of machine vision in agriculture and other fields.

Grape clusters manual#

Traditional manual harvesting can no longer meet the basic needs of the grape industry, and several prototypes for commercial grape-harvesting robots have been developed. With an aging population and reduced agricultural labor force in China, it is urgent to develop automated grape-harvesting robots capable of working in the field ( Lin et al., 2019). Grape harvesting is a labor-intensive and time-consuming work ( Luo et al., 2016). Grapes have become one of the most globally popular fruits because of their desired taste and rich nutrition. The demonstrated performance of the developed method indicates that it can be applied to grape-harvesting robots. The detection and point cloud segmentation for each grape took approximately 1.7 s. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union ( IOU) are 89.53, 95.33, and 82.00%, respectively.

A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose.

The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots.