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

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.