Though most herbicide is applied uniformly in agronomic fields, there is strong evidence that weeds are not distributed uniformly within the crop fields. If an effective weed detection system were developed, both economic and environmental benefits would result from its use for site-specific weed management. Past work in this area has focused mainly on either low spatial resolution photo-detectors or off-line machine vision systems. This study was undertaken to develop real-time machine vision weed detection technology for outdoor lighting conditions. The novel environmentally adaptive segmentation algorithm (EASA) was developed with the objective of real-time operation on an on-board computer-based system. The EASA used cluster analysis to group pixels of homogenous color regions of the image together which formed the basis for image segmentation. The performance of several variations of this algorithm was measured by comparing segmented field images produced by the EASA, fixed-color HSI region segmentation, and ISODATA clustering with hand-segmented reference images. The time cost and questionable accuracy of hand-segmented reference images led to exploration of the use of computer-segmented reference images. Sensitivity and background sensitivity were used as performance measures. Significant differences were found between the means of sensitivity, background sensitivity, and overall performance across segmentation schemes. Similar results were obtained with computer-segmented reference images.
|Original language||English (US)|
|Number of pages||13|
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 1999|
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics