Measuring and modeling apple trees using time-of-flight data for automation of dormant pruning applications

Chattopadhyay, Somrita; Akbar, Shayan A.; Elfiky, Noha M.; Medeiros, Henry; Kak, Avinash C.

doi:10.1109/wacv.2016.7477596

Cited by 24 publications

(12 citation statements)

References 25 publications

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“…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

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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.

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Section: Discussionmentioning

confidence: 99%

Modeling Dormant Fruit Trees for Agricultural Automation

Medeiros

Kim

Sun

et al. 2016

Journal of Field Robotics

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“…Most effort for machine vision towards branch detection for robotic pruning was on grape vines due to its more uniform and organized canopy architecture. For example, some researchers used a single 2D camera and image processing techniques for identification of pruning points in grapevines [64,67,83,84]. Furthermore, a stereo vision system based 3D machine vision system was used and cutting points on the branches were determined with remaining certain length of branches by segmenting the branches and measuring the length of branches [85].…”

Section: Branch Detection/reconstruction For Automated Pruningmentioning

confidence: 99%

“…The results showed that the algorithm removed 85% of long branches, and 69% of overlapping branches. A few other studies have also been conducted on tree modeling using different vision sensing for automatic pruning, such as Kinect 2 [68], RGBD [71], depth image [72], and time-of-flight data [67]. Tabb and her collaborator focused on developing a 3D reconstruction of fruit trees ( Figure 6, reproduced from [69]) for automatic pruning with identifying the branch parameters such as length, diameter, angle, etc.…”

Section: Branch Detection/reconstruction For Automated Pruningmentioning

confidence: 99%

Sensing and Automation in Pruning of Apple Trees: A Review

Schupp

2018

Agronomy

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Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Meanwhile, worker safety is another issue in the manual pruning. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. Identifying tree branches/canopies with sensors as well as automated operating pruning activity are the important components in the automated pruning system. This paper reviews the research and development of sensing and automated systems for branch pruning in apple production. Tree training systems, pruning strategies, 3D structure reconstruction of tree branches, and practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed.

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“…More recent methods that rely on time-of-flight (ToF) RGBD sensors, have been showing promising results [10], [11], [12], [13], [14]. Although these methods overcome some limitations of lidar-based systems such as the inability to utilize texture, their resolution is also a function of distance and hence it can be difficult to apply them to larger structures.…”

Section: Related Workmentioning

confidence: 99%

A robotic vision system to measure tree traits

Tabb¹,

Medeiros

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-The autonomous measurement of tree traits, such as branching structure, branch diameters, branch lengths, and branch angles, is required for tasks such as robotic pruning of trees as well as structural phenotyping. We propose a robotic vision system called the Robotic System for Tree Shape Estimation (RoTSE) to determine tree traits in field settings. The process is composed of the following stages: image acquisition with a mobile robot unit, segmentation, reconstruction, curve skeletonization, conversion to a graph representation, and then computation of traits. Quantitative and qualitative results on apple trees are shown in terms of accuracy, computation time, and robustness. Compared to ground truth measurements, the RoTSE produced the following estimates: branch diameter (mean-squared error 0.99 mm), branch length (mean-squared error 45.64 mm), and branch angle (mean-squared error 10.36 degrees). The average run time was 8.47 minutes when the voxel resolution was 3 mm 3 .

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Measuring and modeling apple trees using time-of-flight data for automation of dormant pruning applications

Cited by 24 publications

References 25 publications

Modeling Dormant Fruit Trees for Agricultural Automation

Modeling Dormant Fruit Trees for Agricultural Automation

Sensing and Automation in Pruning of Apple Trees: A Review

A robotic vision system to measure tree traits

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