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128 results on '"Whiting, Matthew D."'

1. Robotic Pollination of Apples in Commercial Orchards

Author

Sapkota, Ranjan, Ahmed, Dawood, Khanal, Salik Ram, Bhattarai, Uddhav, Mo, Changki, Whiting, Matthew D., and Karkee, Manoj

Subjects
Computer Science - Robotics
Abstract

This research presents a novel, robotic pollination system designed for targeted pollination of apple flowers in modern fruiting wall orchards. Developed in response to the challenges of global colony collapse disorder, climate change, and the need for sustainable alternatives to traditional pollinators, the system utilizes a commercial manipulator, a vision system, and a spray nozzle for pollen application. Initial tests in April 2022 pollinated 56% of the target flower clusters with at least one fruit with a cycle time of 6.5 s. Significant improvements were made in 2023, with the system accurately detecting 91% of available flowers and pollinating 84% of target flowers with a reduced cycle time of 4.8 s. This system showed potential for precision artificial pollination that can also minimize the need for labor-intensive field operations such as flower and fruitlet thinning., Comment: 2 Page, 1 figure

Published
2023

2. Design, Modeling, and Control of a Low-Cost and Rapid Response Soft-Growing Manipulator for Orchard Operations

Author

Dorosh, Ryan, Allen, Justin, He, Zixuan, Ninatanta, Christopher, Coleman, Jack, Spieker, Jack, Tuck, Ethan, Kurtz, Jordan, Zhang, Qin, Whiting, Matthew D., Luo, Jiecai, Karkee, Manoj, and Luo, Ming

Subjects
Computer Science - Robotics
Abstract

Tree fruit growers around the world are facing labor shortages for critical operations, including harvest and pruning. There is a great interest in developing robotic solutions for these labor-intensive tasks, but current efforts have been prohibitively costly, slow, or require a reconfiguration of the orchard in order to function. In this paper, we introduce an alternative approach to robotics using a novel and low-cost soft-growing robotic platform. Our platform features the ability to extend up to 1.2 m linearly at a maximum speed of 0.27 m/s. The soft-growing robotic arm can operate with a terminal payload of up to 1.4 kg (4.4 N), more than sufficient for carrying an apple. This platform decouples linear and steering motions to simplify path planning and the controller design for targeting. We anticipate our platform being relatively simple to maintain compared to rigid robotic arms. Herein we also describe and experimentally verify the platform's kinematic model, including the prediction of the relationship between the steering angle and the angular positions of the three steering motors. Information from the model enables the position controller to guide the end effector to the targeted positions faster and with higher stability than without this information. Overall, our research show promise for using soft-growing robotic platforms in orchard operations., Comment: International Conference on Intelligent Robots and Systems (IROS) 2023

Published
2023

12. Design, Modeling, and Control of a Low-Cost and Rapid Response Soft-Growing Manipulator for Orchard Operations

Author

Dorosh, Ryan, primary, Allen, Justin, additional, He, Zixuan, additional, Ninatanta, Christopher, additional, Coleman, Jack, additional, Spieker, Jack, additional, Tuck, Ethan, additional, Kurtz, Jordan, additional, Zhang, Qin, additional, Whiting, Matthew D., additional, Luo, Jiecai, additional, Karkee, Manoj, additional, and Luo, Ming, additional

Published
2023
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34. Computer vision‐based tree trunk and branch identification and shaking points detection in Dense‐Foliage canopy for automated harvesting of apples.

Author

Zhang, Xin, Karkee, Manoj, Zhang, Qin, and Whiting, Matthew D.

Subjects
TREE trunks, TREE branches, CONVOLUTIONAL neural networks, COMPUTER vision, APPLES, SEASONAL employment
Abstract

Fresh market apples are one of the high‐value crops in the United States. Washington alone has produced two‐thirds of the annual national production in the past 10 years. However, the availability of seasonal labor is increasingly uncertain. Shake‐and‐catch automated harvesting solutions have, therefore, become attractive for addressing this challenge. One of the significant challenges in applying this harvesting system is effectively positioning the end‐effector at appropriate excitation locations. A computer vision system was used for automatically identifying appropriate locations. Convolutional neural networks (CNNs) were utilized to identify the tree trunks and branches for supporting the automated excitation locations determination. Three CNN architectures were employed: Deeplab v3+ ResNet‐18, VGG‐16, and VGG‐19. Four pixel classes were predefined as branches, trunks, apples, and leaves to segment the canopies trained to simple, narrow, accessible, and productive tree architectures with varying foliage density. Results on Fuji cultivar showed that ResNet‐18 outperformed the VGGs in identifying branches and trunks based on all three evaluation measures: per‐class accuracy (PcA), intersection over union (IoU), and boundary‐F1 score (BFScore). ResNet‐18 achieved a PcA of 97%, IoU of 0.69, and BFScore of 0.89. The ResNet‐18 was further evaluated for its robustness with other test canopy images. When applied this method to one of the highest density cultivars of Scifresh, results showed it can achieve IoUs of 0.41 and 0.62 and BFScores of 0.71 and 0.86 for branches and trunks. Such identification result helped to get a 72% of auto‐determined shaking points being the "good" category identified by human experts. [ABSTRACT FROM AUTHOR]

Published
2021
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46. Kiona™ Sweet Cherry

Author

Oraguzie, Nnadozie C., primary, Ophardt, David, additional, Whiting, Matthew D., additional, Lang, Gregory A., additional, and Long, Lynn E., additional

Published
2010
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