Developing a Neural Network-based Model for Identifying Medicinal Plant Leaves using Image Recognition Techniques

Abstract

Herbal plants play a vital role in human health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. There has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques to address this. The proper identification of plant methods using computer vision and neural network techniques has been proposed. This approach involves neural network models such as CNN, ALexnet and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve species identification techniques, and protect endangered species.