论文信息 - Crop and weed species recognition based on hyperspectral sensing and active learning

Crop and weed species recognition based on hyperspectral sensing and active learning

In this paper, a new active learning method is proposed, which distinguishes between crop and weed based on their different spectral reflectances. A method comprising novel detection and incremental class augmentation was implemented. Novel detection was based on one class classifiers. Under field conditions, crop and weed species can be recognised successfully. Best results for the active learning were obtained for the one-class Mixture of Gaussians (MOG) and Self Organising Map (SOM) classifiers. The MOG and SOM performance in crop recognition was 100% and 96% respectively while the recognition for different weed species ranged from 31-98% (MOG) and 43-94% (SOM).

[1] John S. Gero. A CURIOUS DESIGN AGENT A Computational Model of Novelty-Seeking Behaviour in Design , 2001 .

[2] Michael Brady,et al. Novelty detection for the identification of masses in mammograms , 1995 .

[3] Esko Herrala,et al. Imaging spectrometer for process industry applications , 1994, Other Conferences.

[4] Cuizhen Wang,et al. Detecting Invasive Sericea Lespedeza (Lespedeza cuneata) in Mid-Missouri Pastureland Using Hyperspectral Imagery , 2008, Environmental management.

[5] H. Ramon,et al. A Weed Species Spectral Detector Based on Neural Networks , 2002, Precision Agriculture.

[6] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[7] Vincenzo Crupi,et al. Neural-Network-Based System for Novel Fault Detection in Rotating Machinery , 2004 .

[8] Bart De Ketelaere,et al. A neural network based plant classifier , 2001 .

[9] T. Borregaard,et al. Crop–weed Discrimination by Line Imaging Spectroscopy , 2000 .

[10] H. Søgaard. Weed Classification by Active Shape Models , 2005 .