Color image segmentation with genetic algorithm for in-field weed sensing

Transactions of the American Society of Agricultural Engineers

Volumn 43, Issue 4, 2000, Pages 1019-1027

  Tang, L ^a   Tian, L ^a   Steward, B L ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Color image segmentation; Genetic algorithm; Lighting condition; Weed sensing

Indexed keywords

COLOR IMAGE PROCESSING; COMPUTER VISION; GENETIC ALGORITHMS; IMAGE SEGMENTATION; IMAGE SENSORS;

COLOR IMAGE SENSING; HUE SATURATION INTENSITY (HSI); IN FIELD WEED SENSING;

WEED CONTROL;

EID: 0034232984     PISSN: 00012351     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (20)

1. Unsupervised image segmentation using a distributed genetic algorithm
2. Unsupervised segmentation of Markov random field modeled textured images using selectionist relaxation
3. Optimal mutation rates in genetic search
4. Dynamic model to detect weeds in cereals under actual fields conditions
5. Adaptive image segmentation using genetic and hybrid search method
6. Analysis of the behavior of a class of genetic adaptive systems
7. A survey on image segmentation
8. Sizing populations for serial and parallel genetic algorithms
9. Real-coded genetic algorithms, virtual alphabets, and blocking
10. The diversification role of crossover in the genetic algorithms
11. Real-time weed detection in outdoor field conditions
12. A computer vision system for characterizing corn growth and development
13. Environmentally adaptive segmentation algorithm for outdoor image segmentation
14. Outdoor field machine vision identification of tomato seedlings for automated weed control
15. Color indices for weed identification under various soil, residue, and lighting conditions