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Proceedings Paper

Machine learning approaches to automate weed detection by UAV based sensors
Author(s): Aaron Etienne; Dharmendra Saraswat
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Paper Abstract

Applying machine learning methods and analysis on remotely sensed color, multispectral, and thermal imagery has been recognized as a potentially cost-effective approach for detecting the location of various weed species in-field. This detection approach has the potential to be an important first step for broader Site-Specific Weed Management procedures (SSWM). The objective of this research was to create a method for automating the detection of weeds in corn and soybean fields, at different stages of the growing season. Sensors based on an unmanned aerial vehicle were used to capture imagery used for this research. We focused on identifying four common weed types present in Midwestern fields. This research involved: 1) collecting color, multispectral, and thermal imagery from UAV based sensors in corn and soybean fields throughout the 2018 growing season, 2) creating individual normalized differential vegetation index (NDVI) images from the near-infrared (NIR) and red multispectral bands 3) applying image thresholding and smoothing techniques on the NDVI imagery , 4) manually drawing bounding boxes and hand labelling vegetation blobs from the processed imagery using color images as the ground truth, 5) developing a training set of these processed, labeled images that represent weeds at different crop growth stages. Preliminary results of these methods show promise in creating an affordable first step to target herbicide application.

Paper Details

Date Published: 14 May 2019
PDF: 14 pages
Proc. SPIE 11008, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, 110080R (14 May 2019); doi: 10.1117/12.2520536
Show Author Affiliations
Aaron Etienne, Purdue Univ. (United States)
Dharmendra Saraswat, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 11008:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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