A Novel Computer Vision Model for Medicinal Plant Identification Using Log-Gabor Filters and Deep Learning Algorithms

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. This 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. The 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, DenseNet201, 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. The 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.

Developing a Neural Network-based Model for Identifying Medicinal Plant Leaves using Image Recognition Techniques

“…AlexNet requires RGB images that are 256X256 in size, so if they are not already 256X256, all training and test images must be resized. [27] results…”

“…The error rate of ResNet is lower than that of humans after training and implementing on the ImageNet dataset. [27] Alexnet AlexNet was the first deep convolutional neural network (CNN) to be widely used for image classification tasks. It was first introduced to the public at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, where it demonstrated the effectiveness of deep CNNs for image classification.…”

A Review on Detection of Medical Plant Images

“…The Centre for Plant Medicine Research (CPMR) in Akuapem Akropong, Ghana, has created a medicinal plant leaf dataset for the study. The NIKON D3500 camera is used to capture the photographs, which have the dimensions 4000 3 and are in the uncompressed JPEG format in YCbCr colour [17]. Images are captured on the anterior surfaces of the leaflets of medicinal plants.…”

Gabor filter-based statistical features for ADHD detection

“…Since the EEG signal is characterized by non-stationary behavior and a diverse range of time-frequency components, using Gabor filters can be an advantage for discovering the signal's descriptive features. In recent years, researchers have prominently used Gabor filters in image processing ( Hu et al, 2020 ), and computer vision-based applications ( Oppong et al, 2022 ). In addition, Gabor filter-based features have been found to be effective in signal classification tasks ( Kumar et al, 2015 ) and even integrated into deep learning models ( Barshooi and Amirkhani, 2022 ; Hammouche et al, 2022 ; Khalifa et al, 2022 ; Oppong et al, 2022 ).…”

“…In recent years, researchers have prominently used Gabor filters in image processing ( Hu et al, 2020 ), and computer vision-based applications ( Oppong et al, 2022 ). In addition, Gabor filter-based features have been found to be effective in signal classification tasks ( Kumar et al, 2015 ) and even integrated into deep learning models ( Barshooi and Amirkhani, 2022 ; Hammouche et al, 2022 ; Khalifa et al, 2022 ; Oppong et al, 2022 ). Despite these advantages, the potential of the Gabor filter has not been explored for ADHD detection.…”