Plant Species Classification Using Information Measure and Deep Learning for an Actual Environmental Problem

Abstract

This chapter's major goal is to examine the issue of real-world recognition to enhance species preservation. As it is a popular topic and a crucial one, the authors focus on identifying plant species. The examples are scanned specimens in traditional plant species identification, and the setting is plain. Real-world species recognition, on the other hand, is more difficult. They begin by looking at realistic species recognition and how it differs from traditional plant species recognition. Interdisciplinary teamwork based on the newest breakthroughs in technology and computer science is provided to cope with the difficult challenge. In this research, they offer a unique framework for deep learning as well as an effective data augmentation strategy. They crop the image before everyone is aware in terms of visual attention. Furthermore, they use it as a data augmentation technique. Attention cropping (AC) is the name given to a revolutionary data augmentation technique. To predict species from a significant quantity of information, fully convolutional neural networks (CNN) are constructed.