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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Engineering, Science and Information Technology
Fauzi, Alfharizky
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Herbal plant leaves classification for traditional medicine using convolutional neural network Fauzi, Alfharizky; Soerowirdjo, Busono; Haryatmi, Emy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3322-3329

Abstract

The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.
Herbal Plant Leaves Classification Using Convolutional Neural Network Models: A Literature Review Fauzi, Alfharizky; Haryatmi, Emy; Riyadi, Tri Agus; Murniyati, Murniyati
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.723

Abstract

Plants are essential to human beings because plants are considered most as foods. Plants can be used for food ingredients, medical purposes, and industrial applications. People inspect plants using traditional methods, such as using the naked eye, which can be time-consuming and expensive. Therefore, the effectiveness and high quality of automated crop identification classification systems are needed for adequate crop protection. This study aims to identify and classify nine plant species using different datasets, focusing on transfer learning from models trained on plant leaf datasets. Most research has shown that increasing the dataset size would significantly improve classification accuracy. The accuracy of the first test using the modified N1 classification model was 99.45%. In the second experiment, the accuracy of the N2 model was 99.65%. The accuracy of the N3 model, despite being slightly less accurate than AlexNet, was 99.55%, and it performed better, while the accuracy of AlexNet was 99.73%. Compared to the AlexNet model, the proposed model performed better and required less training time. The N1 model reduced the training time by 34.58%, the N2 model by 18.25%, and the N3 model by 20.23%. The N1 and N3 resulted in the same size, namely 14.8MB, and the compactness was 92.67%. The size of the N2 model was 29.7MB, and the compactness was 85.29% compactness. The proposed models provide more accuracy and efficiency in classifying plant leaves and can be used as a standalone mobile application that benefits farmers.
Co-Authors Busono Soerowirdjo Emy Haryatmi Murniyati, Murniyati Riyadi, Tri Agus