Advanced computer vision and statistical methods were employed for identifying living plants from soil/residue background for two species of grasses (Shattercane, Green Foxtail) and two broadleaf species (Velvetleaf, Red Root Pigweed) weeds. The excess green index method was used as a contrast enhancement for specifically identifying plant from soil regions. Excess green classified plant and soil regions correctly over the entire three-week observation period with high accuracies (99% plus). Plant and soil binary images were derived from excess green images and provided edge boundaries. These boundaries were used with corresponding gray scale images to extract four classical textural features for plants and soil: angular second moment, inertia, entropy, and local homogeneity. These features were derived from the co-occurrence matrix. Stepwise and canonical discriminant analyses were used to test the classification performance of the texture and excess green features. Discrimination models of local homogeneity, inertia, and angular second moment were found to classify grass and broadleaf categories of plants, with classification accuracies of 93 and 85%, respectively. Classification accuracies of individual species only ranged from 30 to 77%. Soil classification accuracies were also high for textural feature algorithms (97%). The time required to produce tokensets ranged from 15 to 20 s on a UNIX computer system. Additional time required for the system to reach a plant/soil classification ranged from 5 to 10 s. This translated into an overall system response time of 20 to 30 s, with the preprocessing step constituting the major part of the system response time.
|Original language||English (US)|
|Number of pages||9|
|Journal||Transactions of the American Society of Agricultural Engineers|
|State||Published - Jul 1998|
All Science Journal Classification (ASJC) codes
- Agricultural and Biological Sciences (miscellaneous)