The Robotanist: A ground-based agricultural robot for high-throughput crop phenotyping

Mueller-Sim, Tim; Jenkins, Merritt; Abel, Justin R.; Kantor, George

doi:10.1109/icra.2017.7989418

Cited by 104 publications

(65 citation statements)

References 7 publications

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“…Some mobile robots are task‐specific, meaning that they are specially designed for one particular application. Several task‐specific mobile bases can be found in literature including the sweet pepper‐harvesting robot (Lehnert, English, McCool, Tow, & Perez, ) and robots for phenotyping (Mueller‐Sim, Jenkins, Abel, & Kantor, ). Task‐specific mobile bases can also be found in various commercial projects, for example, the weeding robots created by companies like ecoRobotix and Franklin Robotics, and harvesting robots being developed by companies like AGROBOT or Harvest CROO Robotics.…”

Section: Related Workmentioning

confidence: 99%

“…Some mobile robots are task-specific, meaning that they are specially designed for one particular application. Several task-specific mobile bases can be found in literature including the sweet pepper-harvesting robot (Lehnert, English, McCool, Tow, & Perez, 2017) and robots for phenotyping (Mueller-Sim, Jenkins, Abel, & Kantor, 2017 Most agricultural robots rely on a mobile base, that is, specifically designed for one type of environment. A mobile base designed for driving in tractor-sized tracks in open fields, for example, will normally not fit in a greenhouse.…”

Section: Mobile Platform and Navigationmentioning

confidence: 99%

See 1 more Smart Citation

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

Xiong

Grimstad

et al. 2019

Journal of Field Robotics

273

133

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This paper presents an autonomous robot capable of picking strawberries continuously in polytunnels. Robotic harvesting in cluttered and unstructured environment remains a challenge. A novel obstacle‐separation algorithm was proposed to enable the harvesting system to pick strawberries that are located in clusters. The algorithm uses the gripper to push aside surrounding leaves, strawberries, and other obstacles. We present the theoretical method to generate pushing paths based on the surrounding obstacles. In addition to manipulation, an improved vision system is more resilient to lighting variations, which was developed based on the modeling of color against light intensity. Further, a low‐cost dual‐arm system was developed with an optimized harvesting sequence that increases its efficiency and minimizes the risk of collision. Improvements were also made to the existing gripper to enable the robot to pick directly into a market punnet, thereby eliminating the need for repacking. During tests on a strawberry farm, the robots first‐attempt success rate for picking partially surrounded or isolated strawberries ranged from 50% to 97.1%, depending on the growth situations. Upon an additional attempt, the pick success rate increased to a range of 75–100%. In the field tests, the system was not able to pick a target that was entirely surrounded by obstacles. This failure was attributed to limitations in the vision system as well as insufficient dexterity in the grippers. However, the picking speed improved upon previous systems, taking just 6.1 s for manipulation operation in the one‐arm mode and 4.6 s in the two‐arm mode.

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Section: Related Workmentioning

confidence: 99%

Section: Mobile Platform and Navigationmentioning

confidence: 99%

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

Xiong

Grimstad

et al. 2019

Journal of Field Robotics

273

133

View full text Add to dashboard Cite

show abstract

“…Navigation lines were generated based on the stem locations. As listed in Table 5, the representative autonomous robot platforms include "Vinobot" [166] , "BoniRob" [167,168] and "Robotanist" [169] . The advantage of the robot platforms is that they are able to collect plant information during the day and night.…”

Section: Vehicle-based Phenotyping Platformmentioning

confidence: 99%

“…In addition, the size of the robot platforms is small. [163] Color digital camera, Range camera, Laser sensor, Hyperspectral camera, Light curtain, GPS receiver, Rotary encoder Plant height, Plant coverage, Tiller density, Nitrogen University of Arizona [160] Sonar sensor, Multispectral camera, Crop Circle ACS-470, Infrared radiometer, GPS receiver Plant height, Chlorophyll, NDVI, Canopy temperature Kansas State University [161] Laser sensor, Crop Circle ACS-470, GreenSeeker, thermometer, Ultrasonic sensor, GPS receiver Plant height, Canopy termperature, NDVI High Resolution Plant Phenomics Centre [164] Color digital camera, Lidar sensor, GreenSeeker, Hyperspectral camera, Thermometer, Thermography, GPS receiver, Wheel encoder Plant height, NDVI, LA/LAD, Biomass, Canopy temperature LemnaTec [165] Color digital camera, Laser sensor, Hyperspectral camera, Thermography, Fluorescence sensor, CO 2 sensor, radiometer Plant height, NDVI, Nitrogen, Plant coverage, Water stress Blue River Technology [162] Color digital camera, Lidar sensor, Multispectral sensor, Thermography, pyranometer, GPS receiver Plant height, NDVI, Leaf angle, LAI, Canopy temperature [166] Stereo camera, Lidar sensor, GPS receiver, RFID reader, pyranometer, Temperature sensor, Humidity sensor Plant height, LAI BoniRob [167,168] Color digital camera, Range camera, Lidar sensor, Hyperspectral camera, Light curtain, GPS receiver, Rotary encoder Plant height, Plant coverage, Spectral reflection, Biomass, Stem thickness, Spacing in the row Robotanist [169] Color digital camera, Stereo vision, Range camera, Lidar sensor, GPS receiver, Attitude and heading reference system Leaf angle, Plant greenness…”

Section: Vehicle-based Phenotyping Platformmentioning

confidence: 99%

Sensors for measuring plant phenotyping: A review

Qiu¹,

Wei²,

Zhang³

et al. 2018

International Journal of Agricultural and Biological Engineering

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Food crisis is a matter of prime importance because it becomes more severe as the global population grows. Among the solutions to this crisis, breeding is deemed one of the most effective ways. However, traditional phenotyping in breeding is time consuming and laborious, and the database is insufficient to meet the requirements of plant breeders, which hinders the development of breeding. Accordingly, innovations in phenotyping are urgent to solve this bottleneck. The morphometric and physiological parameters of plant are particularly interested to breeders. Numerous sensors have been employed and novel algorithms have been proposed to collect data on such parameters. This paper presents a brief review on the parameter measurement for phenotyping to describe its development in recent years. Some parameters that have been measured in phenotyping are introduced and discussed, including plant height, leaf parameters, in-plant space, chlorophyll, water stress, and biomass. And the measurement methods of each parameter with different sensors were classified and compared. Some comprehensive measurement platforms were also summarized, which are able to measure several parameters simultaneously. Besides, some deficiencies of phenotyping should be addressed, and novel methods should be proposed to reduce cost, improve efficiency, and promote phenotyping in the future.

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“…Some agricultural robots are designed to be used for solving several tasks, like the AgBot II (Bawden et al (2014)), which is a differential drive robotic carrier that can be equipped with various implements. Other robots are specialized in solving one type of task, like the Robotanist (Mueller-Sim et al (2017)), a robot for high-throughput crop phenotyping. Various robots have also been developed for harvesting (Lehnert et al (2017) Feng et al (2018) and pruning (Botterill et al (2016)), while yet other robots have been developed for in-farm transportation, such as the Bin-Dog (Ye et al (2017)), a robotic platform for bin management in orchards.…”

Section: Introductionmentioning

confidence: 99%

Software Components of the Thorvald II Modular Robot

Grimstad¹,

From²

2018

MIC

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In this paper, we present the key software components of the Thorvald II mobile robotic platform. Thorvald II is a modular system developed by the authors for creating robots of arbitrary shapes and sizes, primarily for the agricultural domain. Several robots have been built and are currently operating on farms and universities at various locations in Europe. Robots may take many different forms, and may be configured for differential drive, Ackermann steering, all-wheel drive, all-wheel steering with any number of wheels etc. The software therefore needs to be configuration agnostic. In this paper we present an architecture that allows for simple setup of never-seen-before robot configurations. The presented software is organized in a collection of ROS packages, made available to the reader. These packages allow a user to create her or his own robot configurations and simulate these robots in Gazebo using a provided plugin. Although the presented packages were created to be used with Thorvald robots, they may also be useful for people who are looking to develop their own robot and are interested in testing various robot configurations in simulation before settling on a specific design. To create a robot, the user lists modules with key parameters in one single configuration file and gives this as an input to the robot at startup. Example configuration files are provided within the packages. In this paper, we discuss key aspects of the ROS packages and provide directions on where to find updated information on how to install and use these.

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The Robotanist: A ground-based agricultural robot for high-throughput crop phenotyping

Cited by 104 publications

References 7 publications

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

An autonomous strawberry‐harvesting robot: Design, development, integration, and field evaluation

Sensors for measuring plant phenotyping: A review

Software Components of the Thorvald II Modular Robot

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