End-to-end Learning for Autonomous Crop Row-following

Bakken, Marianne; Moore, Richard J. D.; From, Pål Johan

doi:10.1016/j.ifacol.2019.12.505

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…Bakken et al [46] worked on almost the same idea as [45] in building a model, but they design their robot to works in the agriculture section (crop row-following). In the test results, they also referred to the accuracy of the presented model.…”

Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

Vision-Based Mobile Robot Controllers: A Scientific Review

Abdulazeez¹

2021

TURCOMAT

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Today, there are different types of self-controlled robots. Some of them had critical effects on our lives like industrial and medical robots. Others are for military usages such as drones and the pets robots just for entertainment. The crucial differences between this kind of robot and the controlled ones are their ability to move on their own and make decisions based on their observations of the world around them. Mobile robots must have a data source used as an input dataset and processed to change their behavior; for instance, moving, stopping, rotating, or doing any required action based on the information gathered from the surrounding environment. Different types of sensors were used to feed robots controllers with data. Such data source could be ultrasonic sensor, laser sensor, torque sensor, or vision sensor. Robots integrated with cameras were becoming an essential field of study. They recently attracted significant attention from researchers it has been commonly used in many sectors of healthcare, manufacturing, and many other services. The robot needs a controller with a powerful mechanism of realization to deal with such incoming data. The world of mobile robot controllers is discussed in this paper, and the latest trends were reviewed. This review aims to provide a general understanding of robot controllers and navigation methods developed over the last few years.

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Section: Figure 4 Planning Procedures 4 Related Work For Controlling Methodsmentioning

confidence: 99%

Vision-Based Mobile Robot Controllers: A Scientific Review

Abdulazeez¹

2021

TURCOMAT

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“…Stereo vision can provide depth information, which can help detect crop rows with different illumination conditions and weed pressure than a single camera [96]. Besides the traditional machine vision techniques, the deep learning methods can obtain directly the crop row's orientation from raw images [97].…”

Section: Vision-based Navigationmentioning

confidence: 99%

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

2022

Plant Phenomics

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Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.

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“…In an attempt to use the structure of a field, Barawid et al [14] investigated 2D LiDAR based navigation systems for orchards and a similar strategy was used in a simulated environment by [15] for traversing row-crop fields. While these approaches enable side-applications such as obstacle avoidance, frame drift in self-similar environments can cause issues [16], including crop damage.…”

Section: Related Workmentioning

confidence: 99%

Towards Autonomous Visual Navigation in Arable Fields

Ahmadi¹,

Halstead²,

McCool³

2021

Preprint

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Autonomous navigation of a robot in agricultural fields is essential for every task from crop monitoring through to weed management and fertilizer application. Many current approaches rely on accurate GPS, however, such technology is expensive and also prone to failure (e.g. through lack of coverage). As such, navigation through sensors that can interpret their environment (such as cameras) is important to achieve the goal of autonomy in agriculture.In this paper we introduce a purely vision based navigation scheme which is able to reliably guide the robot through rowcrop fields. Independent of any global localization or mapping, this approach is able to accurately follow the crop-rows and switch between the rows, only using on-board cameras. With the help of a novel crop-row detection and a novel crop-row switching technique, our navigation scheme can be deployed in a wide range of fields with different canopy types in various growth stages. We have extensively tested our approach in five different fields under various illumination conditions using our agricultural robotic platform (BonnBot-I). And our evaluations show that we have achieved a navigation accuracy of 3.82cm over five different crop fields.

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End-to-end Learning for Autonomous Crop Row-following

Cited by 22 publications

References 8 publications

Vision-Based Mobile Robot Controllers: A Scientific Review

Vision-Based Mobile Robot Controllers: A Scientific Review

A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots

Towards Autonomous Visual Navigation in Arable Fields

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