Robotic weed control using automated weed and crop classification

Wu, Xiaolong; Aravecchia, Stéphanie; Lottes, Philipp; Stachniss, Cyrill; Pradalier, Cédric

doi:10.1002/rob.21938

Cited by 125 publications

(52 citation statements)

References 60 publications

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“…The NH scenario is particularly relevant for farms with (or planning to convert to) an herbicide-free production. In these farms weeds are often controlled by tillage applications, although precision farming techniques such as robotic weed control are gaining more popularity over the years 42 . Several EU farms are expected to present a BAU-NH intermediate situation, using chemical and mechanical weed control methods.…”

Section: Discussionmentioning

confidence: 99%

Environmental and human health at risk – scenarios to achieve the EU’s 50% pesticide reduction goals.

Silva

Yang

Fleskens

et al. 2021

Preprint

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The recently released Farm to Fork Strategy sets, for the first time ever, pesticide reduction goals at the EU level: 50% reduction in overall use and risk of chemical pesticides and 50% use reduction of more hazardous pesticides. However, little guidance is provided to achieve these targets. In this study, we compiled the characteristics and recommended application rates of 230 EU approved, synthetic, open-field use active substances and explored the potential of eight pesticide reduction scenarios (defined based on application rates, pesticide type, persistence, and hazard) to achieve the reduction goals. Our approach revealed that all 230 substances are potentially harmful to humans or ecosystems, and that only severe pesticide use restrictions such as full conversion to organic farming or allowing only low hazard substances will result in 50% reductions. Our results emphasis the need of an EC action plan on how to achieve and maintain the aimed reduction levels.

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Section: Discussionmentioning

confidence: 99%

Environmental and human health at risk – scenarios to achieve the EU’s 50% pesticide reduction goals.

Silva

Yang

Fleskens

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…The BoniRob robot (as shown in Figure 6r) is more complete, as it performs the tasks of detecting (through cameras and ultrasonic sensors) and removing weeds, not only using mechanical tools, but also spraying herbicide [54].…”

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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“…Several phenotyping systems [1,16,17] have been developed for greenhouse environments and are also available commercially [18,19]. Other systems such as [20][21][22][23][24] have been designed to acquire plant data directly from the fields or estimate information about plants for management actions [25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

Registration of spatio-temporal point clouds of plants for phenotyping

et al. 2021

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Plant phenotyping is a central task in crop science and plant breeding. It involves measuring plant traits to describe the anatomy and physiology of plants and is used for deriving traits and evaluating plant performance. Traditional methods for phenotyping are often time-consuming operations involving substantial manual labor. The availability of 3D sensor data of plants obtained from laser scanners or modern depth cameras offers the potential to automate several of these phenotyping tasks. This automation can scale up the phenotyping measurements and evaluations that have to be performed to a larger number of plant samples and at a finer spatial and temporal resolution. In this paper, we investigate the problem of registering 3D point clouds of the plants over time and space. This means that we determine correspondences between point clouds of plants taken at different points in time and register them using a new, non-rigid registration approach. This approach has the potential to form the backbone for phenotyping applications aimed at tracking the traits of plants over time. The registration task involves finding data associations between measurements taken at different times while the plants grow and change their appearance, allowing 3D models taken at different points in time to be compared with each other. Registering plants over time is challenging due to its anisotropic growth, changing topology, and non-rigid motion in between the time of the measurements. Thus, we propose a novel approach that first extracts a compact representation of the plant in the form of a skeleton that encodes both topology and semantic information, and then use this skeletal structure to determine correspondences over time and drive the registration process. Through this approach, we can tackle the data association problem for the time-series point cloud data of plants effectively. We tested our approach on different datasets acquired over time and successfully registered the 3D plant point clouds recorded with a laser scanner. We demonstrate that our method allows for developing systems for automated temporal plant-trait analysis by tracking plant traits at an organ level.

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Robotic weed control using automated weed and crop classification

Cited by 125 publications

References 60 publications

Environmental and human health at risk – scenarios to achieve the EU’s 50% pesticide reduction goals.

Environmental and human health at risk – scenarios to achieve the EU’s 50% pesticide reduction goals.

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

Registration of spatio-temporal point clouds of plants for phenotyping

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