Medicinal Plant Parts Identification and Classification using Deep Learning Based on Multi Label Categories

  • Misganaw Aguate Widneh Lecturer at DTU
  • Abebe Tesfahun Workneh Scientific director at Debremakos Institute of technology
  • Amlakie Aschale Alemu Lecturer at DTU
Keywords: Medicinal Plant Parts, Deep Learning, Multi-label, Convolutional neural network, Fine-tuned model

Abstract

Plants have been used as direct medicinal sources since ancient times as well as today. However, researchers and pharmacists are facing difficulties to identify medicinal plant parts before starting ingredient extraction in the laboratory. This study was conducted to identify the medicinal plant part based on multi-label categories by employing a sigmoid classifier as the last layer of Convolutional Neural Network (CNN). The study employed supervised learning approach in which the true values were predefined initially for the classifier using data annotation phase. Hence, leaf images of the plants were taken as an identity for the rest of the plant parts. The system was designed based on transfer learning by adopting (fine tune) the pre-trained models that employ CNN and trained using Image Net. High-resolution cameras for data acquisition and google Colab for the experiment (training and testing) were used. Mobile Net performed best with an accuracy of 93% for training sets and 92% for testing sets. When the models were evaluated using F1_score, it performed 94%. Without batch normalization at fully connected layer, this model scored 84%. So, Mobile Net obtained higher performance, and suitable to classify the medicinal plant body part. It was also taken as the fastest model to train because Mobile Net used depth wise separable convolution method that reduces scalar multiplication through convolution. By observing the results obtained from the presence and absence of batch normalization, this study deduced that batch normalization is advantageous to obtain good classification performances of the models.

Author Biographies

Misganaw Aguate Widneh, Lecturer at DTU

Lecturer in Electrical and Computer Engineering departemnt of Debre Tabor University

MSc. in Computer Engineering

Abebe Tesfahun Workneh, Scientific director at Debremakos Institute of technology

Scientific Director for Debre Markos Institute of Technology and Lecturer in Electrical and Computer Engineering

Phd. in Computer Engineering

Amlakie Aschale Alemu, Lecturer at DTU

Lecturer in Electrical and Computer Engineering departemnt of Debre Tabor University

MSc. in Computer Engineering

Published
2021-09-03
How to Cite
Widneh, M., Workneh, A., & Alemu, A. (2021). Medicinal Plant Parts Identification and Classification using Deep Learning Based on Multi Label Categories. Ethiopian Journal of Sciences and Sustainable Development, 8(2), 96-108. https://doi.org/10.20372/ejssdastu:v8.i2.2021.380
Section
Articles