Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics

McCool, Chris; Beattie, James A.; Firn, Jennifer; Lehnert, Christopher; Kulk, Jason; Bawden, Owen; Russell, Raymond; Pérez, Tristán

doi:10.1109/lra.2018.2794619

Cited by 46 publications

(29 citation statements)

References 8 publications

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“…Systems that approach this level of autonomy are rare and typically address well-defined problems at the frontiers of research. Examples include robotic harvesting (Bac, vanHenten, Hemming, & Edan, 2014), pruning (Botterill et al, 2017), pollination (Williams et al, 2019), and weeding (McCool et al, 2018). To develop these systems and make them economically viable, further research and development is required.…”

Section: Introductionmentioning

confidence: 99%

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

Bender

Whelan

Sukkarieh

2019

Journal of Field Robotics

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This article presents an agricultural data set collected by Ladybird, an autonomous field robot designed at the Australian Centre for Field Robotics. The data set contains weekly scans of cauliflower and broccoli (Brassica oleracea) covering a 10 week growth cycle from transplant to harvest. The data set includes ground truth; physical characteristics of the crop; environmental data collected by a weather station and a soil sensor network; and scans of the crop performed by Ladybird, which include stereo color, thermal and hyperspectral imagery. The layout of the farm and data collection methodology are described. A description of Ladybird's capabilities and sensors are provided. Benchmark results are provided to illustrate the contents of the data set and how it can be processed. The hyperspectral data are compiled into hypercubes and the pixels are classified into crop, weed, or soil. An object detector is applied to the color imagery to locate the crop. Our intention in releasing the data set is to facilitate robotics and machine learning research activity in agriculture. The data set can be downloaded from https://doi.org/10.25910/5c941d0c8bccb.

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Section: Introductionmentioning

confidence: 99%

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

Bender

Whelan

Sukkarieh

2019

Journal of Field Robotics

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“…Following this concept, in addition to detecting weeds, the Australian AgBot II robot mechanically performs the removal of weeds from crops, through the action of three types of tools: an arrow-shaped hoe, a toothed tool and a cutting tool. The AgBot II uses techniques such as Local Binary Pattern (LBP) and Covariance Feature in its image processing stage, collected by the RGB camera, to identify weeds [40].…”

Section: Robotic Applications In Agriculture For Plant Treatmentmentioning

confidence: 99%

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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“…Over the years, mobile platforms have been developed for a range of agricultural applications, from weeding (McCool et al, ), to high throughput phenotyping (Vijayarangan et al, ), to transportation (Ye et al, ). Some mobile robots are task‐specific, meaning that they are specially designed for one particular application.…”

Section: Related Workmentioning

confidence: 99%

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

Xiong

Grimstad

et al. 2019

Journal of Field Robotics

292

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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Efficacy of Mechanical Weeding Tools: a study into alternative weed management strategies enabled by robotics

Cited by 46 publications

References 8 publications

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

A high‐resolution, multimodal data set for agricultural robotics: A Ladybird's‐eye view of Brassica

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

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

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