Robotic Green Asparagus Selective Harvesting

Leu, Adrian; Razavi, M H; Langstädtler, Lasse; Ristić-Durrant, Danijela; Raffel, H.; Schenck, Christian; Gräser, Axel; Kuhfuß, Bernd

doi:10.1109/tmech.2017.2735861

Cited by 40 publications

(24 citation statements)

References 10 publications

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“…To identify asparagus ready to be harvested in real-time, the robot has an RGB-D camera that provides the planting data for its vision module, consisting of the following tasks: point cloud generator, camera calibration (via Template Point Cloud (TPC) and Model Point Cloud (MPC)) and online asparagus tracking (RANSAC and Euclidean clustering methods). As a result of the work, the German robot achieved an average locomotion speed of 0.2 m/s and a harvest cycle of 2 s per robotic arm, resulting in 90% of successful harvests [77].…”

Section: Wheels With Actuatormentioning

confidence: 97%

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Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R&D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.

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Section: Wheels With Actuatormentioning

confidence: 97%

“…In 2017, researchers Leu et al developed a green asparagus harvesting robot (named GARotics) [77]. Asparagus must be harvested when they reach a height of between 15.24 and 20.32 cm, otherwise, they will not be accepted by the market.…”

Section: Wheels With Actuatormentioning

confidence: 99%

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

2021

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“…Lots of today's agricultural robots emphasize the harvesting of the fruits from the trees. Examples include [12], [13] or in other forms of the crops such as [14], [15]. Their vision systems deploy traditional computer vision algorithms to recognize and locate the fruits' locations for grasping by the robotic arms.…”

Section: Machine Vision In Agricultural Roboticsmentioning

confidence: 99%

Rubber Tree Tapping Position Detection on Trunk-Covered RGB-D Images for Automation Platform

Wongtanawijit

Khaorapapong

2020

ECTI-CIT

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Recently, an autonomous vehicle with 3D sensors for the rubber tree (Hevea brasiliensis) plantation navigation has been presented. Therefore, we present a machine vision for detecting the tapping position and the rubber juice collecting cup in the images, which can be deployed onto an autonomous platform. Firstly, we show an RGB-D image acquisition technique using artificial lights for capturing a rubber tree in the low-light. Then, we present two tapping position detection algorithms which are the color-feature based with sliding window algorithm and the novel deep object detector. To perform the detection on our custom dataset, we build a Faster-RCNN with the pre-trained MobileNetV2 using the fine-tuning technique. The results show that the deep detector outperforms our conventional detector which gives 0.92 average precision on our dataset.

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“…Many robots have been developed to work in harvesting for different fruits and vegetable. For example, Hemming et al (2014) designed a robot for sweet -pepper, van Henten et al (2002) proposed a robot for harvesting cucumbers and Leu et al (2017) designed a robot for the harvesting of green asparagus. Feng et al (2015), proposed a robot for picking tomato within a greenhouse that uses an installed rail to travel between the tomato vines.…”

Section: Introductionmentioning

confidence: 99%

Precision agriculture ’19

2019

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The harvesting of soft fruits and vegetables is a labour-intensive process, often representing more than 50% of the total costs for the producer. In this paper, a harvesting robot is proposed for tomato picking. The robotic solution is developed to address the needs of the tomato producers in the Shanghai region in China, one that faces population growth and therefore a higher demand on food supply. The robotic system presented here consists of a variable-stiffness manipulator arm, a soft robot gripper, and different types of sensors that are used to identify and locate in 3D and pick the tomatoes. The implemented variable compliance enables the robot manipulator to work in a semistructured environment without damage to itself, the crop, or the surrounding. In this paper, the hardware and the software of the robot is described in detail. Early results from the first testing of a proof-of-concept on fresh tomatoes placed on artificial stems in Shanghai are presented, as well as picking UK tomato varieties in greenhouse conditions.

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Robotic Green Asparagus Selective Harvesting

Cited by 40 publications

References 10 publications

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead

Rubber Tree Tapping Position Detection on Trunk-Covered RGB-D Images for Automation Platform

Precision agriculture ’19

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