Ms. Angelin R. Favorito Profile

Angelin R. Favorito

at Univ of Memphis

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Proceedings Article | 7 June 2024 Presentation + Paper

A low cost, semi-autonomous phenotyping cart for late growth stages of tall crops

Joseph Perry, Shoaf Robinson, Angelin Favorito, Stephanie Ward, Md Rafi Ur Rahman, Thomas Watson, Eddie Jacobs

Proceedings Volume 13053, 1305308 (2024) https://doi.org/10.1117/12.3013889

KEYWORDS: LIDAR, Sensors, Design, Agriculture, RGB color model, Image segmentation, Vegetation, Data modeling, Cameras, Batteries

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With an increasing focus on precision agriculture to maximize crop yields and minimize ecological impacts, remote sensing for agriculture has required the deployment of more advanced sensors and processing algorithms. Traditionally, unmanned aerial systems (UAS’s) have been the primary choice for phenotyping crops, but these systems are limited in endurance, power, payload, and legality. Medium to large, unmanned ground vehicles (UGV’s), however, are not hampered by these limitations. Previous research in the application of phenotyping UGV’s for tall crops has been focused on either small systems or very large gantry systems. Described here is a medium sized, low-cost, adjustable UGV that provides a solution by demonstrating the capability to image tall crops into late growth states. It incorporates a sliding mechanism to allow for a greater range in height than previous phenotyping UGV’s with the same payload capacity.The UGV’s capabilities are analyzed theoretically and practically, including its structural rigidity, handling, and endurance. An overview of parts and assembly is presented to facilitate replication and proliferation of the vehicle. Additionally, the vehicle is primarily fabricated using off-the-shelf components. The few custom components are used based on common materials and simple geometries and can be replicated with standard metalworking equipment. To further reduce costs, a dual RTKGNSS system is utilized to control the UGV in a semi-autonomous fashion. A Future goal is to use the gathered datasets to produce an algorithm for fully autonomous capabilities.

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