Sensors 2011, 11(6), 6270-6283; doi:10.3390/s110606270
Article

Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

1 United State Department of Agriculture—Agricultural Research Service, Application Technology Research Unit, 1680 Madison Ave, Wooster, OH 44691, USA 2 Agricultural and Biological Engineering Department, University of Illinois, 1304 W. Pennsylvania Ave., Urbana, IL 61801, USA
* Author to whom correspondence should be addressed.
Received: 18 April 2011; in revised form: 18 May 2011 / Accepted: 7 June 2011 / Published: 10 June 2011
(This article belongs to the Special Issue Sensors in Agriculture and Forestry)
PDF Full-text Download PDF Full-Text [646 KB, uploaded 10 June 2011 14:31 CEST]
Abstract: An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA).
Keywords: field crop; machine vision; outdoor illumination; weed identification

Article Statistics

Load and display the download statistics.

Citations to this Article

Cite This Article

MDPI and ACS Style

Jeon, H.Y.; Tian, L.F.; Zhu, H. Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination. Sensors 2011, 11, 6270-6283.

AMA Style

Jeon HY, Tian LF, Zhu H. Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination. Sensors. 2011; 11(6):6270-6283.

Chicago/Turabian Style

Jeon, Hong Y.; Tian, Lei F.; Zhu, Heping. 2011. "Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination." Sensors 11, no. 6: 6270-6283.

Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert