Robot for weed species plant‐specific management

Bawden, Owen; Kulk, Jason; Russell, Raymond; McCool, Chris; English, Andrew; Dayoub, Feras; Lehnert, Christopher; Pérez, Tristán

doi:10.1002/rob.21727

Cited by 122 publications

(88 citation statements)

References 35 publications

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“…The AgBot II shown in Figure 1.7d and presented by Bawden et al (2017) is a robot platform for weed control, set up with dierential drive front wheels and two rear castor wheels. The design emphasizes modularity and ease of on-site assembly of the system.…”

Section: Leaf Classicationmentioning

confidence: 99%

“…This implies that we have not attempted to design a modular and versatile robot -rather a simple, robust, maintainable and cost efficient system for DoD weed control in row crops. A common cost-effective robot design is using differential drive front wheels and rear castor wheels as the robot presented by Bawden et al (2017). If you were to reduce to one castor wheel, Figure 5, the conventional design is to center the rear wheel.…”

Section: Robot Designmentioning

confidence: 99%

“…This has been an important road-block for robotic weed control, as we have not had satisfactory performance of classication when weeds overlap. There has been much eort invested in improving these algorithms This comes at the cost of having 8 motors for steering and drive.The AgBot II shown in Figure 1.7d and presented by Bawden et al (2017) is a robot platform for weed control, set up with dierential drive front wheels and two rear castor wheels. The design emphasizes modularity and ease of on-site assembly of the system.…”

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confidence: 99%

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Robotic in-row weed control in vegetables

Utstumo

Urdal

Brevik

et al. 2018

Computers and Electronics in Agriculture

104

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Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes the systematic cultivation techniques of row crops to navigate and operate in the eld. By the use of machine vision it separates seeded vegetable crops from weed, and treat each weed within the row with individual herbicide droplets, without aecting the crop. This Dropon-Demand (DoD) method allow the use of non-selective herbicides and signicant reductions in herbicide use.This thesis presents six research papers concerning the development of the DoD system and the mobile robot. The robot is tailored to its purpose with cost, maintainability, ecient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benet of reduced weight, complexity and cost. The DoD system for herbicide application has been developed within and in connection with this project. The inuence of liquid properties viscosity and surface tension on the formation and stability of droplets has been tested in lab trials. A control circuit for synchronized control of solenoid valves was developed and tested.Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 µg glyphosate or 0.15 µg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for leaf retention, as the common herbicide glyphosate had no eect unless mixed with suitable additives. The trials document the DoD eect on weed species not previously described in literature, and with an alternative herbicide to glyphosate, iodosulfuron. A eld trial with the robot was performed in a carrot eld, and all the weeds were eectively controlled with the DoD system.iii Abstract The robot and DoD system represent an important contribution to the range of systems presented witin Precision Agriculture for in-row weed control -a movement which as a whole represent a paradigm shift to the environmental impact and health risks of weed control, while providing valuable new tools to the producers. My advisor, Prof. Jan Tommy Gravdahl has the ability to delve deeply into a wide array of technical elds. He has been a solid and consistent support, which has been vital for me to bridge the divide between the industrial and academic worlds. I will strive for the opportunities to work more together.Friendships have grown both at NTNU, UMN and Adigo which will outlast this thesis. I thank you, my family, colleagues and friends for the support to complete this journey. And not the least, I thank my wife Camilla. This introduction will describe the background for the project and its motivation, and the context and relationship between each paper is described in Section 1.4. The conclusion in Chapter 3 will tie together our ndings and describe our perspective on the path forwards for Drop on De...

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Section: Leaf Classicationmentioning

confidence: 99%

Section: Robot Designmentioning

confidence: 99%

mentioning

confidence: 99%

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Robotic in-row weed control in vegetables

Utstumo

Urdal

Brevik

et al. 2018

Computers and Electronics in Agriculture

104

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“…The Deepfield Bonirob (Lottes, Hörferlin, Sander, & Stachniss, ; Ruckelshausen et al, ) detected crop plants with various of sensors (near‐infrared [NIR], RGB cameras, or time‐of‐flight [TOF] cameras), and treated weeds individually with a stamping tube. The AgBotII (Bawden et al, ) detected and classified weeds with a color camera, and control weeds with spot sprayers or hoeing tools.…”

Section: Introductionmentioning

confidence: 99%

Automated crop plant detection based on the fusion of color and depth images for robotic weed control

Gai

Tang

Steward

2019

Journal of Field Robotics

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Robotic weeding enables weed control near or within crop rows automatically, precisely and effectively. A computer‐vision system was developed for detecting crop plants at different growth stages for robotic weed control. Fusion of color images and depth images was investigated as a means of enhancing the detection accuracy of crop plants under conditions of high weed population. In‐field images of broccoli and lettuce were acquired 3–27 days after transplanting with a Kinect v2 sensor. The image processing pipeline included data preprocessing, vegetation pixel segmentation, plant extraction, feature extraction, feature‐based localization refinement, and crop plant classification. For the detection of broccoli and lettuce, the color‐depth fusion algorithm produced high true‐positive detection rates (91.7% and 90.8%, respectively) and low average false discovery rates (1.1% and 4.0%, respectively). Mean absolute localization errors of the crop plant stems were 26.8 and 7.4 mm for broccoli and lettuce, respectively. The fusion of color and depth was proved beneficial to the segmentation of crop plants from background, which improved the average segmentation success rates from 87.2% (depth‐based) and 76.4% (color‐based) to 96.6% for broccoli, and from 74.2% (depth‐based) and 81.2% (color‐based) to 92.4% for lettuce, respectively. The fusion‐based algorithm had reduced performance in detecting crop plants at early growth stages.

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“…In addition, a combination of chemical and mechanical methods are also utilized to destroy the weeds [16]. Some modular weeding mechanisms are also implemented [17].…”

Section: Introductionmentioning

confidence: 99%

Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme

Chang

Lin

2018

Robotics

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This paper proposes a scheme that combines computer vision and multi-tasking processes to develop a small-scale smart agricultural machine that can automatically weed and perform variable rate irrigation within a cultivated field. Image processing methods such as HSV (hue (H), saturation (S), value (V)) color conversion, estimation of thresholds during the image binary segmentation process, and morphology operator procedures are used to confirm the position of the plant and weeds, and those results are used to perform weeding and watering operations. Furthermore, the data on the wet distribution area of surface soil (WDAS) and the moisture content of the deep soil is provided to a fuzzy logic controller, which drives pumps to perform variable rate irrigation and to achieve water savings. The proposed system has been implemented in small machines and the experimental results show that the system can classify plant and weeds in real time with an average classification rate of 90% or higher. This allows the machine to do weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%.

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Robot for weed species plant‐specific management

Cited by 122 publications

References 35 publications

Robotic in-row weed control in vegetables

Robotic in-row weed control in vegetables

Automated crop plant detection based on the fusion of color and depth images for robotic weed control

Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme

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