Campus Units

Agricultural and Biosystems Engineering

Document Type

Conference Proceeding

Publication Version

Published Version

Publication Date

2016

DOI

10.13031/aim.20162460814

Conference Title

2016 ASABE Annual International Meeting

Conference Date

July 17–20, 2016

City

Orlando, FL, United States

Abstract

Weed management is vitally important in crop production systems. However, conventional herbicide based weed control can lead to negative environmental impacts. Manual weed control is laborious and impractical for large scale production. Robotic weed control offers a possibility of controlling weeds precisely, particularly for weeds growing near or within crop rows. A computer vision system was developed based on Kinect V2 sensor, using the fusion of two-dimensional textural data and three-dimensional spatial data to recognize and localized crop plants different growth stages. Images were acquired of different plant species such as broccoli, lettuce and corn at different growth stages. A database system was developed to organize these images. Several feature extraction algorithms were developed which addressed the problems of canopy occlusion and damaged leaves. With our proposed algorithms, different features were extracted and used to train plant and background classifiers. Finally, the efficiency and accuracy of the proposed classification methods were demonstrated and validated by experiments.

Comments

This paper is from 2016 ASABE Annual International Meeting, Paper No. 162460814, pages 1-15 (doi: 10.13031/aim.20162460814). St. Joseph, Mich.: ASABE.. Posted with permission.

Copyright Owner

American Society of Agricultural and Biological Engineers

Language

en

File Format

application/pdf

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