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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nur Nabilah Abu Mangshor
Universiti Teknologi MARA
Electrical & Electronics Engineering

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Herbal plant recognition using deep convolutional neural network Izwan Asraf Md Zin; Zaidah Ibrahim; Dino Isa; Sharifah Aliman; Nurbaity Sabri; Nur Nabilah Abu Mangshor
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (552.13 KB) | DOI: 10.11591/eei.v9i5.2250

Abstract

This paper investigates the application of deep convolutional neural network (CNN) for herbal plant recognition through leaf identification. Traditional plant identification is often time-consuming due to varieties as well as similarities possessed within the plant species. This study shows that a deep CNN model can be created and enhanced using multiple parameters to boost recognition accuracy performance. This study also shows the significant effects of the multi-layer model on small sample sizes to achieve reasonable performance. Furthermore, data augmentation provides more significant benefits on the overall performance. Simple augmentations such as resize, flip and rotate will increase accuracy significantly by creating invariance and preventing the model from learning irrelevant features. A new dataset of the leaves of various herbal plants found in Malaysia has been constructed and the experimental results achieved 99% accuracy.
Pre-trained classification of scalp conditions using image processing Shafaf Ibrahim; Zarith Azuren Noor Azmy; Nur Nabilah Abu Mangshor; Nurbaity Sabri; Ahmad Firdaus Ahmad Fadzil; Zaaba Ahmad
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp138-144

Abstract

Scalp problems may occur due to the miscellaneous factor, which includes genetics, stress, abuse and hair products. The conventional technique for scalp and hair treatment involves high operational cost and complicated diagnosis. Besides, it is becoming progressively important for the payer to investigate the value of new treatment selection in the management of a specific scalp problem. As they are generally expensive and inconvenient, there is an increasing need for an affordable and convenient way of monitoring scalp conditions. Thus, this paper presents a study of pre-trained classification of scalp conditions using image processing techniques. Initially, the scalp image went through the pre-processing such as image enhancement and greyscale conversion. Next, three features of color, texture, and shape were extracted from each input image, and stored in a region of interest (ROI) table. The knowledge of the values of the pre-trained features is used as a reference in the classification process subsequently. A technique of support vector machine (SVM) is employed to classify the three types of scalp conditions which are alopecia areata (AA), dandruff and normal. A total of 120 images of the scalp conditions were tested. The classification of scalp conditions indicated a good performance of 85% accuracy. It is expected that the outcome of this study may automatically classify the scalp condition, and may assist the user on a selection of suitable treatment available.
Leaf Recognition using Texture Features for Herbal Plant Identification Zaidah Ibrahim; Nurbaity Sabri; Nur Nabilah Abu Mangshor
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp152-156

Abstract

This research investigates the application of texture features for leaf recognition for herbal plant identification.  Malaysia is rich with herbal plants but not many people can identify them and know about their uses.   Preservation of the knowledge of these herb plants is important since it enables the general public to gain useful knowledge which they can apply whenever necessary.  Leaf image is chosen for plant recognition since it is available and visible all the time.   Unlike flowers that are not always available or roots that are not visible and not easy to obtain, leaf is the most abundant type of data available in botanical reference collections.  A comparative study has been conducted among three popular texture features that are Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Speeded-Up Robust Features (SURF) with multiclass Support Vector Machine (SVM) classifier.  A new leaf dataset has been constructed from ten different herb plants.  Experimental results using the new constructed dataset and Flavia, an existing dataset, indicate that HOG and LBP produce similar leaf recognition performance and they are better than SURF.
Co-Authors Ahmad Firdaus Ahmad Fadzil Dino Isa Izwan Asraf Md Zin Nurbaity Sabri Shafaf Ibrahim Sharifah Aliman Zaaba Ahmad Zaidah Ibrahim Zarith Azuren Noor Azmy