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Row Detection BASED Navigation and Guidance for Agricultural Robots and Autonomous Vehicles in Row-Crop Fields: Methods and Applications

AGRONOMY-BASEL (2023)

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023)

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Summary: A method is proposed for crop height measurement using machine vision, which includes establishing a measurement model based on aperture image principle, performing image segmentation and enhancement, strengthening the boundary line between crop region and background, and detecting the horizontal line using the Sobel operator.

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Summary: English Image sensor communication (ISC), also known as optical camera communication, is a form of visible light communication that utilizes image sensors for data reception. ISC has spatial separation properties and robustness to ambient noise, making it suitable for outdoor applications such as intelligent transportation systems (ITSs). This review analyzes the research trends in ISC, particularly in ITSs, and examines various ISC receivers and their applications, as well as the use of ISC in range estimation techniques and simultaneous communication and range estimation. The aim is to provide a comprehensive overview of the role of ISC technology in ITSs and its potential for future development.

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Vision-based navigation and guidance for agricultural autonomous vehicles and robots: A review

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Summary: Autonomous navigation of agricultural robots in agricultural environments is challenging due to their complex and unstructured nature. Machine vision technology has been intensively studied and widely used in the field of agricultural navigation. This paper reviews the research advances in machine vision-based navigation and guidance for agricultural robots, discusses key visual navigation information processing technologies, and focuses on the application of vision-based navigation technology for agricultural robots. The challenges and future development prospects of machine vision in agricultural robot navigation are also discussed.

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Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network

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Robotic Fertilization in Strip Cropping using a CNN Vegetables Detection-Characterization Method

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COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Summary: Crop row following in narrow row cereal crops is challenging, as the separation between plants within a row disappears early on and canopy closure between rows occurs months later. This study proposes an image processing pipeline that uses the shape and orientation of grass leaves to identify and track rows. The method achieves sub-50 mm accuracy and is suitable for agri-robots to drive between real-world wheat crop rows.

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Key technologies of machine vision for weeding robots: A review and benchmark

Yong Li et al.

Summary: This paper systematically reviews the development of machine-vision-based weeding robots in the past 30 years, discussing key technologies such as image preprocessing, image segmentation, navigation line extraction, and weed recognition. The study reveals that due to environmental variation and system complexity, machine-vision-based weeding robots still face many challenges.

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Summary: Clustering is an essential tool in data mining, and there is a need for improved, flexible, and efficient clustering techniques. This study presents a comprehensive review of traditional and state-of-the-art clustering techniques, demonstrating the importance of clustering in various disciplines and fields such as big data, artificial intelligence, and robotics.

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Improved Real-Time Semantic Segmentation Network Model for Crop Vision Navigation Line Detection

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BDS/IMU Integrated Auto-Navigation System of Orchard Spraying Robot

Liang Zhang et al.

Summary: An integrated navigation system was developed to improve the accuracy and reliability of orchard spraying robots. Utilizing an error Kalman filter algorithm and a path-tracking control algorithm, the system successfully corrected the inertial measurement unit error and enabled the robot to smoothly track the pre-set path. Field experiments demonstrated the effectiveness of the proposed auto-navigation system.

APPLIED SCIENCES-BASEL (2022)

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Real-Time Localization and Mapping Utilizing Multi-Sensor Fusion and Visual-IMU-Wheel Odometry for Agricultural Robots in Unstructured, Dynamic and GPS-Denied Greenhouse Environments

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AGRONOMY-BASEL (2022)

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Nan Hu et al.

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Precision Variable-Rate Spraying Robot by Using Single 3D LIDAR in Orchards

Limin Liu et al.

Summary: Automatic navigation (AN) and precision variable-rate spraying (PVS) are important technologies for the safety of pesticide application in orchards and protecting the environment. This study combines a 3D LIDAR sensor and the RANSAC algorithm to achieve AN and PVS of the robot, reducing the volume of pesticide application, air drift, and ground loss.

AGRONOMY-BASEL (2022)

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The fast continuous wavelet transformation (fCWT) for real-time, high-quality, noise-resistant time-frequency analysis

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NATURE COMPUTATIONAL SCIENCE (2022)

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Field estimation of maize plant height at jointing stage using an RGB-D camera

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CROP JOURNAL (2022)

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Review Engineering, Electrical & Electronic

Actuators and Sensors for Application in Agricultural Robots: A Review

Dongbo Xie et al.

Summary: With the rapid development of science and technology in recent years, agricultural robots have gradually replaced humans in completing agricultural operations, leading to increased production efficiency and the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments, such as drive systems, control strategies, and environmental perception. It also highlights the importance of innovative actuator design and development to meet the complex requirements of agricultural operations.

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Detection of Curved Rows and Gaps in Aerial Images of Sugarcane Field Using Image Processing Techniques

Bruno Moraes Rocha et al.

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IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (2022)

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Luis Emmi et al.

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PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS (ICINCO) (2022)

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Saba Rabab et al.

Summary: This study proposed an efficient crop row detection algorithm without the use of templates and most pre-information, showing robust performance in challenging conditions like lighting changes and presence of weeds. Tested on public and Genomic Sub-Selection datasets, and comparing favorably to other methods, it exhibited superior performance.

PRECISION AGRICULTURE (2021)

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Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform

Jiqing Chen et al.

Summary: Accurate extraction of navigation path is crucial for automated navigation of agricultural robots. This paper proposes a new algorithm for fitting navigation path of greenhouse cucumber-picking robots using prediction point Hough transform, which shows better segmentation effect and lower error compared to traditional methods. The proposed algorithm has computation time of 17.92 ms, saving 35.20 ms compared to traditional Hough transform.

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An Outline of Multi-Sensor Fusion Methods for Mobile Agents Indoor Navigation

Yuanhao Qu et al.

Summary: Indoor autonomous navigation is a fundamental function of mobile agents, which refers to their perception and exploration abilities in unknown indoor environments with the help of various sensors. Multi-sensor fusion methods have the potential to enhance the perception and navigation abilities of mobile agents, and involve analyzing single sensor advantages and disadvantages, introducing mainstream technologies, and discussing possible technique trends and challenges.

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Crop Row Segmentation and Detection in Paddy Fields Based on Treble-Classification Otsu and Double-Dimensional Clustering Method

Yue Yu et al.

Summary: Visual navigation is crucial for improving agricultural automation, and extracting a guidance path from agricultural field images is the most important issue in this field. A new crop row segmentation and detection algorithm for complex paddy fields has been proposed, which combines grayscale transformation, clustering, feature extraction, and adaptive clustering methods to achieve robust and accurate results.

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Real time detection of inter-row ryegrass in wheat farms using deep learning

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An image matching optimization algorithm based on pixel shift clustering RANSAC

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Using a depth camera for crop row detection and mapping for under-canopy navigation of agricultural robotic vehicle

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COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Samwel Opiyo et al.

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IEEE SENSORS JOURNAL (2021)

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Timeliness Harvesting Loss of Rice in Cold Region under Different Mechanical Harvesting Methods

Jinwu Wang et al.

Summary: The research investigated the impact of harvest date and mechanical harvesting methods on rice yield losses, finding that the harvest date significantly affected different types of losses. Harvesting around 52-53 days after heading resulted in optimal yield and minimal losses.

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Marianne Bakken et al.

Summary: This study introduces a robot-supervised learning approach that automates label generation for semantic segmentation in crop row detection, showing promising potential for future development in field testing.

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) (2021)

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A Low-cost Robot with Autonomous Recharge and Navigation for Weed Control in Fields with Narrow Row Spacing

Yayun Du et al.

Summary: This paper presents the first fully autonomous robot for weed management in row crops, capable of computer vision based navigation, weed detection, complete field coverage, and automatic recharge. Designed for inter-row weed control in crop fields, it aims to reduce herbicide use and provide precise weed targeting. The compact robot has been field-tested in flaxseed fields in North Dakota to demonstrate its efficacy.

2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (2021)

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Summary: The constant development of agricultural robotics aims to address challenges posed by population growth, urbanization, product competitiveness, environmental preservation, and a lack of qualified labor. This paper reviews the main applications of agricultural robotic systems and highlights the four major areas for future research work in enhancing smart agriculture: locomotion systems, sensors, computer vision algorithms, and communication technologies.

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IFAC PAPERSONLINE (2018)

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