Identification of Traditional Medicinal Plant Leaves Using an effective Deep Learning model and Self-Curated Dataset

TL;DR

This paper presents a custom deep learning CNN model that accurately identifies medicinal plant leaves, reducing reliance on human experts and aiding traditional medicine practices.

Contribution

The authors developed a novel CNN architecture and validated it on multiple datasets, including a self-curated one, achieving high accuracy in plant identification.

Findings

Achieved up to 99.7% accuracy on the self-curated dataset.

Validated model effectiveness across three different datasets.

Demonstrated potential to automate medicinal plant identification.

Abstract

Medicinal plants have been a key component in producing traditional and modern medicines, especially in the field of Ayurveda, an ancient Indian medical system. Producing these medicines and collecting and extracting the right plant is a crucial step due to the visually similar nature of some plants. The extraction of these plants from nonmedicinal plants requires human expert intervention. To solve the issue of accurate plant identification and reduce the need for a human expert in the collection process; employing computer vision methods will be efficient and beneficial. In this paper, we have proposed a model that solves such issues. The proposed model is a custom convolutional neural network (CNN) architecture with 6 convolution layers, max-pooling layers, and dense layers. The model was tested on three different datasets named Indian Medicinal Leaves Image Dataset,MED117 Medicinal Plant Leaf Dataset, and the self-curated dataset by the authors. The proposed model achieved respective accuracies of 99.5%, 98.4%, and 99.7% using various optimizers including Adam, RMSprop, and SGD with momentum.