Cooperative Agricultural Operations of Aerial and Ground Unmanned Vehicles

Mammarella, Martina; Comba, Lorenzo; Biglia, Alessandro; Dabbene, Fabrizio; Gay, Paolo

doi:10.1109/metroagrifor50201.2020.9277573

Cited by 21 publications

(7 citation statements)

References 26 publications

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“…Some studies developed on the basis of the SfM method have provided interesting correlation results between vine canopy volume and pruning weight (R 2 range 0.56-0.71) [18]. The 3D models provide a detailed view of the vineyard rows and therefore these results can be used as input for autonomous driving of unmanned ground vehicles [227,228], and can provide useful information to carry out operations such as pruning in an automated form by bud detection [43].…”

Section: Vineyard Canopy Geometry Based On the Point Cloudmentioning

confidence: 99%

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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The potential of precision viticulture has been highlighted since the first studies performed in the context of viticulture, but especially in the last decade there have been excellent results have been achieved in terms of innovation and simple application. The deployment of new sensors for vineyard monitoring is set to increase in the coming years, enabling large amounts of information to be obtained. However, the large number of sensors developed and the great amount of data that can be collected are not always easy to manage, as it requires cross-sectoral expertise. The preliminary section of the review presents the scenario of precision viticulture, highlighting its potential and possible applications. This review illustrates the types of sensors and their operating principles. Remote platforms such as satellites, unmanned aerial vehicles (UAV) and proximal platforms are also presented. Some supervised and unsupervised algorithms used for object-based image segmentation and classification (OBIA) are then discussed, as well as a description of some vegetation indices (VI) used in viticulture. Photogrammetric algorithms for 3D canopy modelling using dense point clouds are illustrated. Finally, some machine learning and deep learning algorithms are illustrated for processing and interpreting big data to understand the vineyard agronomic and physiological status. This review shows that to perform accurate vineyard surveys and evaluations, it is important to select the appropriate sensor or platform, so the algorithms used in post-processing depend on the type of data collected. Several aspects discussed are fundamental to the understanding and implementation of vineyard variability monitoring techniques. However, it is evident that in the future, artificial intelligence and new equipment will become increasingly relevant for the detection and management of spatial variability through an autonomous approach.

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Section: Vineyard Canopy Geometry Based On the Point Cloudmentioning

confidence: 99%

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Ferro

Catania

2023

Horticulturae

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“…Smart farming is based on the information provided by sensors placed on an agricultural field (Ahmed et al, 2016 ); Machine Learning (ML) models could learn patterns to support the farmers' decision-making (Mammarella et al, 2020 ; Shorewala et al, 2021 ). These sensors joined with a microcontroller sending data constantly, are considered part of the IoT.…”

Section: Background and Related Workmentioning

confidence: 99%

Mobile robotics in smart farming: current trends and applications

Yépez-Ponce,

Salcedo,

Rosero-Montalvo

et al. 2023

Front. Artif. Intell.

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In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

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“…However, the authors did not address the collision-avoidance in the run-time problem [23]. In a similar context, also in the hilly vineyards of Italy, Mammarella et al [24] describes cooperation between aerial and ground robots, exploiting the DWA based on a receding-horizon scheme to perform local path planning.…”

Section: Related Workmentioning

confidence: 99%

Collision Avoidance Considering Iterative Bézier Based Approach for Steep Slope Terrains

Santos

Valente

et al. 2022

IEEE Access

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The Agri-Food production requirements needs a more efficient and autonomous processes, and robotics will play a significant role in this process. Deploying agricultural robots on the farm is still a challenging task. Particularly in slope terrains, where it is crucial to avoid obstacles and dangerous steep slope zones. Path planning solutions may fail under several circumstances, as the appearance of a new obstacle. This work proposes a novel open-source solution called AgRobPP-CA to autonomously perform obstacle avoidance during robot navigation. AgRobPP-CA works in real-time for local obstacle avoidance, allowing small deviations, avoiding unexpected obstacles or dangerous steep slope zones, which could impose a fall of the robot. Our results demonstrated that AgRobPP-CA is capable of avoiding obstacles and high slopes in different vineyard scenarios, with low computation requirements.

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Cooperative Agricultural Operations of Aerial and Ground Unmanned Vehicles

Cited by 21 publications

References 26 publications

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Technologies and Innovative Methods for Precision Viticulture: A Comprehensive Review

Mobile robotics in smart farming: current trends and applications

Collision Avoidance Considering Iterative Bézier Based Approach for Steep Slope Terrains

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