Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns
dc.contributor.author | Lease, Basil Andy | |
dc.contributor.supervisor | Lenin Gopal | en_US |
dc.contributor.supervisor | Raymond Chiong | en_US |
dc.contributor.supervisor | Wong Wei Kitt | en_US |
dc.date.accessioned | 2022-03-14T07:34:00Z | |
dc.date.available | 2022-03-14T07:34:00Z | |
dc.date.issued | 2022 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/88106 | |
dc.description.abstract |
This thesis presents a novel pixel-level weed classification through rotation-invariant uniform local binary pattern (LBP) features for precision weed control. Based on two-level optimisation structure; First, Genetic Algorithm (GA) optimisation to select the best rotation-invariant uniform LBP configurations; Second, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Neural Network (NN) ensemble to select the best combinations of voting weights of the predicted outcome for each classifier. The model obtained 87.9% accuracy in CWFID public benchmark. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | PhD | en_US |
curtin.department | Curtin Malaysia | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Curtin Malaysia | en_US |
curtin.contributor.orcid | Lease, Basil Andy [0000-0001-5469-187X] | en_US |