Automation of dormant pruning in specialty crop production: An adaptive framework for automatic reconstruction and modeling of apple trees

Elfiky, Noha M.; Akbar, Shayan A.; Sun, Jianxin; Park, Johnny; Kak, Avinash C.

doi:10.1109/cvprw.2015.7301298

Cited by 36 publications

(24 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The relatively high resolution of scanning structured light enables over 90% of branches to be identified, although 39% of detected branches are “fragmented” into different parts. Elfiky, Akbar, Sun, Park, & Kak () imaged apple tree branches with a Kinect 2 camera. They fused multiple images into a single point cloud, identified the trunk, then identified the points at which branches leave the trunk in the point cloud.…”

Section: Introductionmentioning

confidence: 99%

A Robot System for Pruning Grape Vines

Botterill

Paulin

Green

et al. 2016

Journal of Field Robotics

167

119

View full text Add to dashboard Cite

This paper describes a robot system for the automatic pruning of grape vines. A mobile platform straddles the row of vines, and it images them with trinocular stereo cameras as it moves. A computer vision system builds a three-dimensional (3D) model of the vines, an artificial intelligence (AI) system decides which canes to prune, and a six degree-of-freedom robot arm makes the required cuts. The system is demonstrated cutting vines in the vineyard. The main contributions of this paper are the computer vision system that builds 3D vine models, and the test of the complete-integrated system. The vine models capture the structure of the plants so that the AI system can decide where to prune, and they are accurate enough that the robot arm can reach the required cuts. Vine models are reconstructed by matching features between images, triangulating feature matches to give a 3D model, then optimizing the model and the robot's trajectory jointly (incremental bundle adjustment). Trajectories are estimated online at 0.25 m/s, and they have errors below 1% when modeling a 96 m row of 59 vines. Pruning each vine requires the robot arm to cut an average of 8.4 canes. A collision-free trajectory for the arm is planned in intervals of 1.5 s/vine with a rapidly exploring random tree motion planner. The total time to prune one vine is 2 min in field trials, which is similar to human pruners, and it could be greatly reduced with a faster arm. Trials also show that the long chain of interdependent components limits reliability. A commercially feasible pruning robot should stop and prune each vine in turn. C 2016 Wiley Periodicals, Inc.

show abstract

Section: Introductionmentioning

confidence: 99%

A Robot System for Pruning Grape Vines

Botterill

Paulin

Green

et al. 2016

Journal of Field Robotics

167

119

View full text Add to dashboard Cite

show abstract

“…Currently, registration is performed automatically between pairs of images acquired by moving the linear slide, and human intervention is necessary to perform registration of the other views in order to provide an appropriate initial guess to the system. We are currently investigating more robust frameworks to perform fully automated registration (Chattopadhyay, Akbar, Elfiky, Medeiros, & Kak, ; Elfiky, Akbar, Sun, Park, & Kak, ). Data segmentation is also a somewhat tedious task that currently must be done manually.…”

Section: Discussionmentioning

confidence: 99%

Modeling Dormant Fruit Trees for Agricultural Automation

Medeiros

Kim

Sun

et al. 2016

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

Dormant pruning of fruit trees is one of the most costly and labor‐intensive activities in specialty crop production. We present a system that solves the first step in the process of automated pruning: accurately measuring and modeling the fruit trees. Our system employs a laser sensor to collect observations of fruit trees from multiple perspectives, and it uses these observations to measure parameters needed for pruning. A split‐and‐merge clustering algorithm divides the collected data into three sets of points: trunk candidates, junction point candidates, and branches. The trunk candidates and junction point candidates are then further refined by a robust fitting algorithm that models as cylinders each segment of the trunk and primary branches. In this work, we focus on measuring the diameters of the primary branches and the trunk, which are important factors in dormant pruning and can be obtained directly from the cylindrical models. We show that the results are qualitatively satisfactory using synthetic and real data. Our experiments with three synthetic and three real apple trees of two different varieties showed that the system is able to identify the primary branches with an average accuracy of 98% and estimate their diameters with an average error of 0.6 cm. Although the current implementation of the system is too slow for large‐scale practical applications (it can measure approximately two trees per hour), our study shows that the proposed approach may serve as a fundamental building block of robotic pruners in the near future.

show abstract

“…Parameters such as diameter and angle between trunk and PB are used to determine pruning points. To obtain these parameters, a 3D representation of the tree is necessary to accurately choose the pruning points [11,18,6].…”

Section: Orchard Pruningmentioning

confidence: 99%

“…Stereo vision cameras rely on constant illumination and other hard-to-control variables [18]. The kinect sensor is relatively cheap and accessible, but accuracy and reliability is questionable [11,6]. The laser options are very expensive when compared to the other two data acquisition options, but their accuracy tops all three [11,18].…”

Section: Data Acquisitionmentioning

confidence: 99%

Automated Pruning of Greenhouse Indeterminate Tomato Plants

Joey

Zhang

Liu

2018

Proceedings of the 2nd International Conference on Vision, Image and Signal Processing

View full text Add to dashboard Cite

Automated Pruning of Greenhouse Indeterminate Tomato Plants Joey Angeja Pruning of indeterminate tomato plants is vital for a profitable yield and it still remains a manual process. There has been research in automated pruning of grapevines, trees, and other plants, but tomato plants have yet to be explored. Wage increases are contributing to the depleting profits of greenhouse tomato farmers. Rises in population are the driving force behind the need for efficient growing techniques. The major contribution of this thesis is a computer vision algorithm for detecting greenhouse tomato pruning points without the use of depth sensors. Given an up-close 2-D image of a tomato stem with the background excluded, the algorithm proposed in this work can detect and mark the tomato suckers.iv ACKNOWLEDGMENTS Thanks to:• My parents, Joe and Liz, for supporting me through everything. Nothing would have been achievable without your love and support.• My sisters, Katrina and Marissa, for unconditional love throughout my life.And for being my source of writer's block relief.• Claudia Morales, you always pushed me to strive for success.• My friends (

show abstract

Automation of dormant pruning in specialty crop production: An adaptive framework for automatic reconstruction and modeling of apple trees

Cited by 36 publications

References 16 publications

A Robot System for Pruning Grape Vines

A Robot System for Pruning Grape Vines

Modeling Dormant Fruit Trees for Agricultural Automation

Automated Pruning of Greenhouse Indeterminate Tomato Plants

Contact Info

Product

Resources

About