Research Article A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms Stephen Opoku Oppong , 1 Frimpong Twum , 2 James Ben Hayfron-Acquah , 2 and Yaw Marfo Missah 2 1 Department of ICT Education, University of Education, Winneba, Ghana 2 Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana Correspondence should be addressed to Stephen Opoku Oppong; sooppong@uew.edu.gh Received 24 April 2022; Revised 16 August 2022; Accepted 5 September 2022; Published 27 September 2022 Academic Editor: Kuruva Lakshmanna Copyright © 2022 Stephen Opoku Oppong et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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. e 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. e 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. 1. Introduction Computer vision is a broad term that describes the computer performing the function of an eye by using different mathematical algorithms on a digital image. is area is concerned with the automated processing of images from the real world, extracting features, and interpreting infor- mation in real-time based on the user’s requirements [1, 2]. e fundamental task in computer vision is image recog- nition [3]. Human vision is unique and superior in that it detects and discriminates against the objects around it with ease. It can perceive 3-D structures with perfection and also categorize them efficiently [4]. Computer vision is modelled after the human visual system which nonetheless is superior in detection, identification, and discrimination objects. With human vision being such a complex mechanism, computer vision can be thought of as an approximation of it [5]. Plant taxonomy is the science that aims in detecting, recognizing, describing, characterizing, and naming plants. Chemotaxonomic, anatomical, and morphological classifi- cations are some of the techniques adopted for this science [6]. In comparison to chemotaxonomy, morphological and anatomical classifications are viewed as more traditional [7]. e key aspects that play vital roles in plant taxonomy are; plant identification which deals with the determination of an unknown plant in relation to a previously collected specimen and plant classification which places a known plant in a category based on its shared characteristics with other plants. Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 1189509, 21 pages https://doi.org/10.1155/2022/1189509