Indian Journal of Science and Technology

Abstract

Objective: The Indian Forest is the primary source of many medicinal herbs. Medicinal plants have long been the subject of extensive inquiry and contemplation due to their significance in human survival. Botanist professionals must classify and identify these plants, which is a hard and time-consuming process. As a result, a vision-based technique may help scientists and the general public to identify herb plants more quickly and accurately. As a result, this study proposes a vision-based smart strategy for recognizing herb plants that involve developing a deep learning (DL) model. Although there is a range of helpful plants, we limit ourselves to only 15 from the Kaggle database. Methods: Despite numerous studies, accurately identifying plant species using automation remains a challenge. In this system, we used 15 distinct Indian plants for experiment purposes. We use a dataset of 82,500 images, with around 5500 images of each species. We use leaf shape, texture, and color as the features, whether physiological or morphological. Four deep learning classifiers named Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and Multilayer Perceptrons (MLPs) are deployed on an optimized medicinal plant leaves dataset. Findings: The web based system can identify species by simply uploading an existing image. The multi-layer perceptron classifier performs well with an accuracy of 99.01%. Convolutional neural networks have an accuracy of 98.3%. Novelty: The novelty of our study lies in the intersection of comparison of advanced deep learning techniques. While earlier research has focused on plant identification issues, our approach contributes to the larger fields of explainable AI, and the construction of reliable models. In real-world applications, the background of leaves may change hence photographs with various backgrounds are employed for instructional purposes. Models trained on various backgrounds and can withstand alterations in the background.

Keywords: Deep learning, Generative Adversarial Networks, Multilayer Perceptrons, Recurrent Neural Networks, Convolutional Neural Network, Machine Vision