As the demand for data in precision agriculture in-creases to enhance productivity and sustainability, the acquisition of this data becomes critical. Currently, data acquisition processes often rely on manual methods or stationary platforms, which limit scalability and flexibility. Although autonomous vehicles present innovative solutions, they face challenges such as high costs and limited adoption rates, especially among small-scale farms. This paper introduces a cost-effective, mobile robotic platform for au-tonomous data collection in agricultural environments. Moreover, our platform provides scalability, accuracy, and affordability, democratizing access to precision agriculture technologies. The platform utilizes a standard camera mounted on its arm, guided by ArUco markers -digital markers designed for high precision-strategically placed within the monitoring environment. These markers give the robot high-level commands necessary for an effective data acquisition campaign. Our preliminary results show that leveraging open-source robotic projects and off-the-shelf components allows the proposed autonomous robotic system to adapt to various crops and terrains effectively in both simulated and real scenarios. Thus, it offers a viable alternative to more costly data acquisition solutions.

Towards Cost-Effective Robotic Solution for Agricultural Data Acquisition

Hueller, Jhonny;Vecchio, Massimo;Pincheira, Miguel;Shamsfakhr, Farhad;Antonelli, Fabio
2024-01-01

Abstract

As the demand for data in precision agriculture in-creases to enhance productivity and sustainability, the acquisition of this data becomes critical. Currently, data acquisition processes often rely on manual methods or stationary platforms, which limit scalability and flexibility. Although autonomous vehicles present innovative solutions, they face challenges such as high costs and limited adoption rates, especially among small-scale farms. This paper introduces a cost-effective, mobile robotic platform for au-tonomous data collection in agricultural environments. Moreover, our platform provides scalability, accuracy, and affordability, democratizing access to precision agriculture technologies. The platform utilizes a standard camera mounted on its arm, guided by ArUco markers -digital markers designed for high precision-strategically placed within the monitoring environment. These markers give the robot high-level commands necessary for an effective data acquisition campaign. Our preliminary results show that leveraging open-source robotic projects and off-the-shelf components allows the proposed autonomous robotic system to adapt to various crops and terrains effectively in both simulated and real scenarios. Thus, it offers a viable alternative to more costly data acquisition solutions.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/351007
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