Implementing Inception v3, VGG-16 and VGG-19 Architectures of CNN for Medicinal Plant leaves Identification and Disease Detection

Abstract

Medicinal plants have been a valuable source of healing and healthcare for centuries, serving as a crucial component of traditional medicine systems worldwide. The identification of these plants is a fundamental step in harnessing their therapeutic potential and ensuring their sustainable use. Accurate identification of these plants and the early detection of diseases affecting their leaves are crucial for ensuring a consistent supply of high-quality medicinal resources. In this paper, three types of CNN architectures of deep learning are used to develop robust classification models for distinguishing between healthy and diseased medicinal plant leaves. Inception v3 is known for its versatility in handling various input image sizes without the need for extensive pre-processing, while VGG-19 exhibits high accuracy, robust feature extraction, and suitability for complex image patterns. VGG-16, known for its simplicity and ease of training, provided competitive results, particularly where computational resources were limited. These networks have been pre-trained on large-scale image datasets and fine-tuned using them. These different approaches provide a varied solution to this problem by comparing their accuracy levels, feasibility and to select which one suits the best in this study.