A Study on the Environmental-Based Turning Characteristics of Multi-Purpose Agricultural Robots

Ji-Won Lee; Minsu Kang; Huichang Park; Yongjun Cho; Jangseok Oh; Min-Gyu Kim; Kap-Ho Seo; Min-Ro Park

doi:10.7746/jkros.2021.16.4.319

Concept and Realization of a Novel Test Method Using a Dynamic Test Stand for Detecting Persons by Sensor Systems on Autonomous Agricultural Robotics

Sensors ◽

10.3390/s21072315 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2315

Author(s):

Christian Meltebrink ◽

Tom Ströer ◽

Benjamin Wegmann ◽

Cornelia Weltzien ◽

Arno Ruckelshausen

Keyword(s):

Environmental Conditions ◽

Dynamic Test ◽

Test Methods ◽

Test Stand ◽

Human Detection ◽

Test Method ◽

Sensor Systems ◽

Functional Safety ◽

Agricultural Robots ◽

Agricultural Robotics

As an essential part for the development of autonomous agricultural robotics, the functional safety of autonomous agricultural machines is largely based on the functionality and robustness of non-contact sensor systems for human protection. This article presents a new step in the development of autonomous agricultural machine with a concept and the realization of a novel test method using a dynamic test stand on an agricultural farm in outdoor areas. With this test method, commercially available sensor systems are tested in a long-term test around the clock for 365 days a year and 24 h a day on a dynamic test stand in continuous outdoor use. A test over a longer period of time is needed to test as much as possible all occurring environmental conditions. This test is determined by the naturally occurring environmental conditions. This fact corresponds to the reality of unpredictable/determinable environmental conditions in the field and makes the test method and test stand so unique. The focus of the developed test methods is on creating own real environment detection areas (REDAs) for each sensor system, which can be used to compare and evaluate the autonomous human detection of the sensor systems for the functional safety of autonomous agricultural robots with a humanoid test target. Sensor manufacturers from industry and the automotive sector provide their sensor systems to have their sensors tested in cooperation with the TÜV.

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Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Agronomy ◽

10.3390/agronomy11040646 ◽

2021 ◽

Vol 11 (4) ◽

pp. 646

Author(s):

Bini Darwin ◽

Pamela Dharmaraj ◽

Shajin Prince ◽

Daniela Elena Popescu ◽

Duraisamy Jude Hemanth

Keyword(s):

Deep Learning ◽

Crop Yield ◽

Precision Agriculture ◽

Soil Parameters ◽

Learning Models ◽

Weed Detection ◽

Agricultural Industry ◽

Agricultural Robots ◽

Detection Techniques ◽

Learning Techniques

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

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Bringing the Farmer Perspective to Agricultural Robots

Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '15 ◽

10.1145/2702613.2732894 ◽

2015 ◽

Cited By ~ 7

Author(s):

Fiona Redhead ◽

Stephen Snow ◽

Dhaval Vyas ◽

Owen Bawden ◽

Ray Russell ◽

...

Keyword(s):

Agricultural Robots

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Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments

Sensors ◽

10.3390/s19194236 ◽

2019 ◽

Vol 19 (19) ◽

pp. 4236

Author(s):

Woosik Lee ◽

Hyojoo Cho ◽

Seungho Hyeong ◽

Woojin Chung

Keyword(s):

Motion Estimation ◽

Autonomous Vehicles ◽

Autonomous Navigation ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

Navigation Satellite ◽

Key Technology ◽

Satellite Systems ◽

Agricultural Robots ◽

Global Navigation Satellite

Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.

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RESEARCH ON REMOTE OPERATING SYSTEM OF PICKING ROBOT BASED ON BIG DATA AND WIFI

INMATEH Agricultural Engineering ◽

10.35633/inmateh-64-44 ◽

2021 ◽

pp. 447-456

Author(s):

Beibei Sun

Keyword(s):

Operating System ◽

Big Data ◽

Remote Monitoring ◽

Agricultural Production ◽

Recognition System ◽

Main Mode ◽

Agricultural Robots ◽

Remote Monitoring System ◽

Modern Agriculture ◽

And Control

Agricultural mechanization has become the main mode of agricultural production and represents the development direction of modern agriculture. The amount of data generated in the agricultural production process is extremely huge, so it is necessary to introduce the concept and analysis method of big data. Combining agricultural robots with big data can improve the performance and application effect of robots. This paper combines big data, WLAN technology and robot technology to realize man-machine remote cooperation platform. This gives full play to the advantages that people are good at object recognition and robots are good at execution, and improves the fruit picking efficiency. The target fruit positioning and recognition system aided by machine vision is adopted to realize the accurate positioning of the fruit to be picked. Design of LFM control signal fitting based on big data clustering. In order to verify the feasibility of the scheme, taking the tomato picking robot as an example, the communication error and control accuracy using big data and WIFI (Wireless Fidelity) technology were tested, and the positioning and navigation efficiency with and without remote monitoring system was compared. Test results show that using big data and WIFI remote monitoring technology can effectively improve the efficiency and accuracy of positioning and navigation of remote operating system, which is of great significance for the design of automatic control system of picking robot.

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Artificial Intelligence-Driven Autonomous Robot for Precision Agriculture

Acta Technologica Agriculturae ◽

10.2478/ata-2021-0008 ◽

2021 ◽

Vol 24 (1) ◽

pp. 48-54

Author(s):

Ivan Beloev ◽

Diyana Kinaneva ◽

Georgi Georgiev ◽

Georgi Hristov ◽

Plamen Zahariev

Keyword(s):

Artificial Intelligence ◽

Precision Agriculture ◽

Communication Systems ◽

Robotic Systems ◽

Small Scale ◽

Construction Process ◽

End User ◽

Agricultural Robots ◽

Agricultural Enterprises ◽

Human Labour

AbstractIn the recent years, robotic systems became more advanced and more accessible. This has led to their slow, but stable integration and use in different processes and applications, including in the agricultural domain. Nowadays, agricultural robots are developed with the aim to replace the human labour in the otherwise exhausting, time-consuming or dangerous activities. Agricultural robotic systems provide many advantages, which can differ based on the type of the robot and its sensors, actuators and communication systems. This paper presents the design, the construction process, the main characteristics and the evaluation of a prototype of a small-scale agricultural robot that can be used for some of the simplest activities in agricultural enterprises. The robot is designed as an end-user autonomous mobile system, which is capable of self-localization and can map or inspect a specific farming area. The decision-making capabilities of the robot are based on artificial intelligence (AI) algorithms, which allow it to perform specific actions in accordance to the situation and the surrounding environment. The presented prototype is in its early development and evaluation stages and the paper concludes with discussions on the possible further improvements of the platform.

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