Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

Sensors

Hong Y. Jeon¹  Lei F. Tian² 

[1] United State Department of Agriculture—Agricultural Research Service, Application Technology Research Unit, 1680 Madison Ave, Wooster, OH 44691, USA; E-Mail:;Agricultural and Biological Engineering Department, University of Illinois, 1304 W. Pennsylvania Ave., Urbana, IL 61801, USA; E-Mail:

关键词: field crop;    machine vision;    outdoor illumination;    weed identification;   

DOI  :  10.3390/s110606270

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.