3D Skeletonization of Complex Grapevines for Robotic Pruning

Robotic pruning of dormant grapevines is an area of active research in order to promote vine balance and grape quality, but so far robotic efforts have largely focused on planar, simplified vines not representative of commercial vineyards. This paper aims to advance the robotic perception capabilities necessary for pruning in denser and more complex vine structures by extending plant skeletonization techniques. The proposed pipeline generates skeletal grapevine models that have lower reprojection error and higher connectivity than baseline algorithms. We also show how 3D and skeletal information enables prediction accuracy of pruning weight for dense vines surpassing prior work, where pruning weight is an important vine metric influencing pruning site selection.Comment: 6 pages, IROS 2023 Computer Vision for Automatio

MACHINE VISION SYSTEM FOR ROBOTIC APPLE HARVESTING IN FRUITING WALL ORCHARDS

Mechanization in agriculture is often regarded as one of the greatest human achievements in the twentieth century. These technological advancements have led to significant reduction in human effort in the production of bulk agricultural commodities such as corn and wheat. Specialty crop industry such as fresh fruit market, on the other hand, are still dependent upon manual labor for various production operations such as training, pruning, and harvesting. Among these, tree fruit harvesting of high value crops like apples is the most labor intensive and time sensitive task that requires the right number of farm workers at right time. To increase productivity and reduce dependency on seasonal labor, researchers have proposed automated harvesting systems. Because of highly unstructured orchard environment and variable outdoor conditions, these technologies have achieved only limited successes in the past. No commercial viability has been achieved and every apple destined for fresh market is still handpicked. The lack of mechanized harvesting system has the potential to threaten the long-term sustainability of fresh fruit industry in the United States and around the world.This dissertation focuses on the study and evaluation of a machine vision and an integrated robotic system for automated harvesting of apples grown in modern fruiting wall orchards. The machine vision algorithm designed to work in orchard environment accurately detected apples growing individually as well as in heavy clusters under variable natural lighting conditions. A pragmatic approach to harvesting (also called hierarchical approach) showed that 98% of the fruit could be detected with iterative imaging and harvesting of most visible fruits. The integrated robotic system with global camera and custom built seven degrees of freedom manipulator successfully picked 84% of attempted fruit with 6 seconds of average harvest time per fruit. This approach of selective apple harvesting with a global camera system and low-cost manipulator show a huge potential for cost-effective robotic solutions for harvesting fresh market apples. However, several limitations still remain to be addressed for commercialization

APPLE IDENTIFICATION IN FIELD ENVIRONMENT WITH OVER THE ROW MACHINE VISION SYSTEM

Accurate detection and identification of fruits is critically important for the success of developing automated apple harvesting system. Research has been conducted to identify apples in orchard environment with reasonable accuracy when apples are clearly visible or partially occluded. However, only limited work has been carried out to identify fruit in clusters, which is critically important as fruit clusters are common in field conditions. This work focused on accurately identifying partially visible apples and apples in clusters using a machine vision system. An over the row platform with tunnel structure and artificial lighting was used to increase uniformity in imaging environment. Iterative Circular Hough Transform (CHT) was used to detect clearly visible fruit as well as individual fruit in cluster. Partially occluded apples were detected using blob analysis and a clustering algorithm based on Euclidean distance between centroids of blobs was used to merge the parts of an apple divided by occlusion. Potential fruit detected by CHT and blob analysis were passed through a color identification process to decide if they were apples. This algorithm was successfully tested with 60 images of apple trees and resulted with 90% apple identification accuracy. On average, CHT detected 54% of total identified apples whereas blob analysis detected remaining 46% with overall false positive of 1.8% and false negative of 8.2%. The fusion of blob analysis and CHT significantly increased detection accuracy compared to individual methods including that in clusters. The results showed potential for in-field apple identification for automated apple harvesting