A feasibility study of an harmless tiltrotor for smart farming applications

Mascarello, Laura Novaro; Quagliotti, Fulvia; Ristorto, Gianluca

doi:10.1109/icuas.2017.7991374

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…The UAV flight mission is represented by a snake-path identified through a series of waypoints over a 200X150 m rectangularshape area. The Ground Sampling Distance (GSD) requirement is a function of the flight altitude, as described in [16], and defines the grid width. In particular, in compliance with the recommendations of the multispectral camera manufacturer, for an altitude of 100 m, the grid size has been set to 20 m and is defined including a 10% of both overlap and sidelap requirements, identified in Figures 4 by light-yellow/green strips.…”

Section: Simulation and Hil Resultsmentioning

confidence: 99%

A Tube-based Robust MPC for a Fixed-wing UAV: an Application for Precision Farming

Mammarella¹,

Capello²

2018

Preprint

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The techniques of precision agriculture include the possibility to execute crop monitoring tasks through the application of Unmanned Aerial Vehicles (UAVs). These platforms are flexible, easy to use and low-cost, and they are the best candidates for improving the farm efficiency and productivity. In this research, a guidance algorithm and a robust control system are combined to guarantee the robustness of the system to additive noise (i.e. wind disturbance) and uncertainties (i.e. model parameter variations). A small fixed-wing UAV with an autonomy of about 1 hour is proposed as case study, to reduce the cost of monitoring and increasing the stability performance of the system. A waypoint-grid on a paddy field is verified by hardwarein-the loop tests. The control scheme provides good results with a low computational effort, guaranteeing the repeatability of the monitoring and reduction of the costs.

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Section: Simulation and Hil Resultsmentioning

confidence: 99%

A Tube-based Robust MPC for a Fixed-wing UAV: an Application for Precision Farming

Mammarella¹,

Capello²

2018

Preprint

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“…The grid-width is given by the camera performance with respect to the flight altitude. For 100 m, the grid size has been set to 20 m, including a 10% of both overlap and sidelap requirements, following the sensor manufacture guidelines described in [26]. Moreover, the coverage area includes also a 10 m band to allow the UAV stabilization after each turn.…”

Section: B Paddy Field Scenariomentioning

confidence: 99%

Sample-Based SMPC for Tracking Control of Fixed-Wing UAV

Mammarella

Capello

Dabbene

et al. 2018

IEEE Control Syst. Lett.

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In this paper, a guidance and tracking control strategy for fixed-wing Unmanned Aerial Vehicle (UAV) autopilots is presented. The proposed control exploits recent results on sample-based stochastic Model Predictive Control, which allow coping in a computationally efficient way with both parametric uncertainty and additive random noise. Different application scenarios are discussed, and the implementability of the proposed approach are demonstrated through software-in-the-loop simulations. The capability of guaranteeing probabilistic robust satisfaction of the constraint specifications represents a key-feature of the proposed scheme, allowing real-time tracking of the designed trajectory with guarantees in terms of maximal deviation with respect to the planned one. The presented simulations show the effectiveness of the proposed control scheme. *This work was not supported by any organization 1 M. Mammarella is with the

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“…Using drones, it is possible to track and detect objects for autonomous farming without human intervention. It can be used to monitor a farm in a far more efficient and cost-effective manner [21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

Gao,

Bambrah,

Parihar

et al. 2024

Agriculture

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With the development of artificial intelligence, the intelligence of agriculture has become a trend. Intelligent monitoring of agricultural activities is an important part of it. However, due to difficulties in achieving a balance between quality and cost, the goal of improving the economic benefits of agricultural activities has not reached the expected level. Farm supervision requires intensive human effort and may not produce satisfactory results. In order to achieve intelligent monitoring of agricultural activities and improve economic benefits, this paper proposes a solution that combines unmanned aerial vehicles (UAVs) with deep learning models. The proposed solution aims to detect and classify objects using UAVs in the agricultural industry, thereby achieving independent agriculture without human intervention. To achieve this, a highly reliable target detection and tracking system is developed using Unmanned Aerial Vehicles. The use of deep learning methods allows the system to effectively solve the target detection and tracking problem. The model utilizes data collected from DJI Mirage 4 unmanned aerial vehicles to detect, track, and classify different types of targets. The performance evaluation of the proposed method shows promising results. By combining UAV technology and deep learning models, this paper provides a cost-effective solution for intelligent monitoring of agricultural activities. The proposed method offers the potential to improve the economic benefits of farming while reducing the need for intensive hum.

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A feasibility study of an harmless tiltrotor for smart farming applications

Cited by 7 publications

References 2 publications

A Tube-based Robust MPC for a Fixed-wing UAV: an Application for Precision Farming

A Tube-based Robust MPC for a Fixed-wing UAV: an Application for Precision Farming

Sample-Based SMPC for Tracking Control of Fixed-Wing UAV

Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms

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