Autonomous crop height estimation and navigation using an unmanned ground vehicle in row-based farmlands (Conference Presentation)

Tianshu Xu; Harnaik Dhami; Song Li; Pratap Tokekar

doi:10.1117/12.2558324

27 April 2020 Autonomous crop height estimation and navigation using an unmanned ground vehicle in row-based farmlands (Conference Presentation)

Tianshu Xu, Harnaik Dhami, Song Li, Pratap Tokekar

Author Affiliations +

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 114140I (2020) https://doi.org/10.1117/12.2558324
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

We present techniques to autonomously measure crop heights in a farmland using two 2D-LiDAR mounted on an Unmanned Ground Vehicle (UGV). Knowing the height of crops is crucial for monitoring overall plant health and growth cycles. Therefore, measuring plant height is a major task in high-throughput phenotyping and is a commonly used trait in plant breeding. Conventional high-throughput height estimations rely on sensors mounted on Unmanned Aerial Vehicles, whose accuracy can be affected by the downwash due to propellers or due to distant lower resolution measurements. To achieve automated height estimation using UGV, we developed an autonomous robotic platform for high throughput phenotyping for genome wide association analysis. We develop a versatile sensing platform mounted on robots to collect large scales of data autonomously from fields. The key to our approach is autonomous row navigation capabilities that enable the robot to scan a row-based farmland without manual input. We adapt methodologies for navigable gap identification and plant heights extracting from 2D LiDAR point clouds. The key steps in our algorithm are random sample consensus (RANSAC), robot motion control, and crop height estimation. We performed a series of experiments in controlled indoor environment and natural farmland environment. Our algorithm was able to make the robot run autonomously in farmland field, and estimate the plant heights within +/- 6.57% in a dataset collected by this platform.

Conference Presentation

Citation Download Citation

Tianshu Xu, Harnaik Dhami, Song Li, and Pratap Tokekar "Autonomous crop height estimation and navigation using an unmanned ground vehicle in row-based farmlands (Conference Presentation)", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 114140I (27 April 2020); https://doi.org/10.1117/12.2558324

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KEYWORDS

Unmanned ground vehicles

Robots

Clouds

LIDAR

Motion controllers

Motion estimation

Robotics

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