Software and Hardware Architectures in Cooperative Aerial and Ground Robots for Agricultural Disease Detection

Menendez-Aponte, Pablo; García, Celsa; Freese, Douglas; Defterli, Sinem; Xu, Yunjun

doi:10.1109/cts.2016.0070

Cited by 13 publications

(5 citation statements)

References 16 publications

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“…In [74], the team consisted of a UAV and a UGV for disease detection in a strawberry field. The role of the UAV was to inspect the entire crop and to mark suspect regions.…”

Section: Ugv and Teams (Uav/ugv)mentioning

confidence: 99%

An Overview of Cooperative Robotics in Agriculture

et al. 2021

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Agricultural robotics has been a popular subject in recent years from an academic as well as a commercial point of view. This is because agricultural robotics addresses critical issues such as seasonal shortages in manual labor, e.g., during harvest, as well as the increasing concern regarding environmentally friendly practices. On one hand, several individual agricultural robots have already been developed for specific tasks (e.g., for monitoring, spraying, harvesting, transport, etc.) with varying degrees of effectiveness. On the other hand, the use of cooperative teams of agricultural robots in farming tasks is not as widespread; yet, it is an emerging trend. This paper presents a comprehensive overview of the work carried out so far in the area of cooperative agricultural robotics and identifies the state-of-the-art. This paper also outlines challenges to be addressed in fully automating agricultural production; the latter is promising for sustaining an increasingly vast human population, especially in cases of pandemics such as the recent COVID-19 pandemic.

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“…In [74], the team consisted of a UAV and a UGV for disease detection in a strawberry field. The role of the UAV was to inspect the entire crop and to mark suspect regions.…”

Section: Ugv and Teams (Uav/ugv)mentioning

confidence: 99%

An Overview of Cooperative Robotics in Agriculture

et al. 2021

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“…These parameters are either important factors of the robot performance or constraints affecting the controller design, as will be discussed in the next section. Note that the tire radius is measured different from the value listed in Menendez-Aponte et al (2016). A wide FOV is necessary to avoid the scenario that the marker is outside of the camera view when the robot is moving to the marker while the robot is not turning towards it.…”

Section: Robot Platformmentioning

confidence: 99%

“…The robot platform designed to scout strawberry fields is shown in Figure 2. A detailed discussion about this robot platform can be found in Menendez‐Aponte et al (2016). The robot is an all‐wheel‐drive, skid‐steering vehicle, equipped with two electric motors to drive the wheels, a laptop computer running the navigation and control software, a front‐facing camera for cross‐bed navigation, and eight bottom‐mounted ultrasonic sensors for over‐bed navigation.…”

Section: Research Backgroundmentioning

confidence: 99%

Minimum‐time row transition control of a vision‐guided agricultural robot

2021

Journal of Field Robotics

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This paper presents a vision‐based, subspace optimal controller aiming to improve the row transition performance of an agricultural robot in a strawberry field. The contribution of this paper is twofold. First, only RGB cameras, instead of complicated sensor suites, are used for cross‐bed navigation and row alignment. Second, a real‐time adaptive dynamic programming‐based algorithm is designed for an optimal row transition. The conditions for row alignment are derived in an augmented pixel coordinate frame. Based on these conditions, a simple motion rule is utilized to reduce the search space dimension so that the proposed algorithm can be implemented in real‐time. Additionally, the inverse‐dynamics policy of the algorithm is updated using vision feedback at each control step to adapt to uncertainties. The proposed controller is tested in both simulations and field experiments. In a simulation comparison, the minimum‐time solution achieved using the proposed algorithm is 44.7 s, which is very close to that of a benchmark algorithm (44.4 s). However, the CPU time required by the proposed algorithm is only 4.3% of time needed by the benchmark algorithm. Twenty field experiments using the presented design were all successful in row transition, with a mean final alignment error of 0.5 cm.

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“…At this stage of development, this approach should be considered for remote sensing applications [51], rather than for robotic disease management, where the robots recognize and control the disease. Disease detection and control in open-fields (not only in well-defined environments) could be achieved by utilizing aerial (UAV) and ground robots (Unmanned Ground Vehicles, UGVs) [52]. In this study [52], UAVs were used to collect spectral images and analyze them to identify "critical" regions.…”

Section: Closed or Open Spaces: Challenges For Robotic Plant Managementmentioning

confidence: 99%

“…Disease detection and control in open-fields (not only in well-defined environments) could be achieved by utilizing aerial (UAV) and ground robots (Unmanned Ground Vehicles, UGVs) [52]. In this study [52], UAVs were used to collect spectral images and analyze them to identify "critical" regions. This information was transferred wirelessly to the UGV, which navigated to the "critical" areas to analyze and collect leaf samples.…”

Section: Closed or Open Spaces: Challenges For Robotic Plant Managementmentioning

confidence: 99%

iPathology: Robotic Applications and Management of Plants and Plant Diseases

2017

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The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

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Software and Hardware Architectures in Cooperative Aerial and Ground Robots for Agricultural Disease Detection

Cited by 13 publications

References 16 publications

An Overview of Cooperative Robotics in Agriculture

An Overview of Cooperative Robotics in Agriculture

Minimum‐time row transition control of a vision‐guided agricultural robot

iPathology: Robotic Applications and Management of Plants and Plant Diseases

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