Trends in Development of UAV-UGV Cooperation Approaches in Precision Agriculture

Vu, Quyen; Raković, Mirko; Delić, Vlado; Ronzhin, Andrey

doi:10.1007/978-3-319-99582-3_22

Cited by 39 publications

(19 citation statements)

References 26 publications

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“…Both UAV and UGV by itself have been successfully employed in plant phenotyping tasks. The coordination between UAV and UGV enables a new breakthrough application of UAV/UGV cooperative systems to achieve a common goal more effectively and efficiently (Arbanas et al, 2018;Vu et al, 2018). Both vehicles in this cooperative team share complementarities according to their capabilities that allow them to operate in the same field and work together to fulfill phenotyping missions.…”

Section: Swarm Robot Is a New Frontier To Efficiently Accomplish Complex Phenotyping Tasksmentioning

confidence: 99%

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Atefi

Pitla

et al. 2021

Front. Plant Sci.

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Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era.

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Section: Swarm Robot Is a New Frontier To Efficiently Accomplish Complex Phenotyping Tasksmentioning

confidence: 99%

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Atefi

Pitla

et al. 2021

Front. Plant Sci.

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“…The research suggests a trend toward cooperative UGV and UAV systems for improved coverage and resolution. In this model, the UGV performs complimentary data acquisition and acts as a mobile charger for the UAV, delivering it to deployment locations as seen in Figure 12 [ 189 , 193 , 194 , 195 ].…”

Section: Remote Sensor Platformsmentioning

confidence: 99%

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

Fahey

Pham

Gardi

et al. 2020

Sensors

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In agriculture, early detection of plant stresses is advantageous in preventing crop yield losses. Remote sensors are increasingly being utilized for crop health monitoring, offering non-destructive, spatialized detection and the quantification of plant diseases at various levels of measurement. Advances in sensor technologies have promoted the development of novel techniques for precision agriculture. As in situ techniques are surpassed by multispectral imaging, refinement of hyperspectral imaging and the promising emergence of light detection and ranging (LIDAR), remote sensing will define the future of biotic and abiotic plant stress detection, crop yield estimation and product quality. The added value of LIDAR-based systems stems from their greater flexibility in capturing data, high rate of data delivery and suitability for a high level of automation while overcoming the shortcomings of passive systems limited by atmospheric conditions, changes in light, viewing angle and canopy structure. In particular, a multi-sensor systems approach and associated data fusion techniques (i.e., blending LIDAR with existing electro-optical sensors) offer increased accuracy in plant disease detection by focusing on traditional optimal estimation and the adoption of artificial intelligence techniques for spatially and temporally distributed big data. When applied across different platforms (handheld, ground-based, airborne, ground/aerial robotic vehicles or satellites), these electro-optical sensors offer new avenues to predict and react to plant stress and disease. This review examines the key sensor characteristics, platform integration options and data analysis techniques recently proposed in the field of precision agriculture and highlights the key challenges and benefits of each concept towards informing future research in this very important and rapidly growing field.

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“…Usually, the UAV flies over the field of interest collecting the aerial images. The flight time ranges from several minutes to hours, depending on the type of UAV and the area to be covered, among other factors [6]. When using short-endurance UAV, several take-offs and landings may be needed.…”

Section: Introductionmentioning

confidence: 99%

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

et al. 2019

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Unmanned aerial systems (UAS) are becoming a common tool for aerial sensing applications. Nevertheless, sensed data need further processing before becoming useful information. This processing requires large computing power and time before delivery. In this paper, we present a parallel architecture that includes an unmanned aerial vehicle (UAV), a small embedded computer on board, a communication link to the Internet, and a cloud service with the aim to provide useful real-time information directly to the end-users. The potential of parallelism as a solution in remote sensing has not been addressed for a distributed architecture that includes the UAV processors. The architecture is demonstrated for a specific problem: the counting of olive trees in a crop field where the trees are regularly spaced from each other. During the flight, the embedded computer is able to process individual images on board the UAV and provide the total count. The tree counting algorithm obtains an F 1 score of 99 . 09 % for a sequence of ten images with 332 olive trees. The detected trees are geolocated and can be visualized on the Internet seconds after the take-off of the flight, with no further processing required. This is a use case to demonstrate near real-time results obtained from UAS usage. Other more complex UAS applications, such as tree inventories, search and rescue, fire detection, or stock breeding, can potentially benefit from this architecture and obtain faster outcomes, accessible while the UAV is still on flight.

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Trends in Development of UAV-UGV Cooperation Approaches in Precision Agriculture

Cited by 39 publications

References 26 publications

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops

On-the-Fly Olive Tree Counting Using a UAS and Cloud Services

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