A NovelComputerVisionModel forMedicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms
| dc.contributor.author | Oppong, Stephen Opku | |
| dc.contributor.author | Twum, Frimpong | |
| dc.contributor.author | Acquah-Hayfron, James Benjamin | |
| dc.contributor.author | Missah, Yaw Marfo | |
| dc.date.accessioned | 2023-12-06T13:59:40Z | |
| dc.date.available | 2023-12-06T13:59:40Z | |
| dc.date.issued | 2022 | |
| dc.description | An article published in Computational Intelligence and Neuroscience, Volume 2022, Article ID 1189509, 21 pages https://doi.org/10.1155/2022/1189509 | |
| dc.description.abstract | Computer vision is the science that enables computers and machines to see and perceive image content on a semantic level. It combines concepts, techniques, and ideas from various fields such as digital image processing, pattern matching, artificial intelligence, and computer graphics. A computer vision system is designed to model the human visual system on a functional basis as closely as possible. Deep learning and Convolutional Neural Networks (CNNs) in particular which are biologically inspired have significantly contributed to computer vision studies. is research develops a computer vision system that uses CNNs and handcrafted filters from Log-Gabor filters to identify medicinal plants based on their leaf textural features in an ensemble manner. e system was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana (MyDataset) consisting of forty-nine (49) plant species. Using the concept of transfer learning, ten pretrained networks including Alexnet, GoogLeNet, Den-seNet201, Inceptionv3, Mobilenetv2, Restnet18, Resnet50, Resnet101, vgg16, and vgg19 were used as feature extractors. The DenseNet201 architecture resulted with the best outcome of 87% accuracy and GoogLeNet with 79% preforming the worse averaged across six supervised learning algorithms. e proposed model (OTAMNet), created by fusing a Log-Gabor layer into the transition layers of the DenseNet201 architecture achieved 98% accuracy when tested on MyDataset. OTAMNet was tested on other benchmark datasets; Flavia, Swedish Leaf, MD2020, and the Folio dataset. The Flavia dataset achieved 99%, Swedish Leaf 100%, MD2020 99%, and the Folio dataset 97%. A false-positive rate of less than 0.1% was achieved in all cases. | |
| dc.identifier.citation | Computational Intelligence and Neuroscience ,Volume 2022, Article ID 1189509, 21 pages; https://doi.org/10.1155/2022/1189509 | |
| dc.identifier.uri | https://doi.org/10.1155/2022/1189509 | |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/14668 | |
| dc.publisher | Computational Intelligence and Neuroscience | |
| dc.title | A NovelComputerVisionModel forMedicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms |