Author(s): Videira, João ; Gaspar, Pedro Dinis ; Soares, Vasco N. G. J. ; Caldeira, João M. L. P.
Date: 2023
Persistent ID: http://hdl.handle.net/10400.6/14227
Origin: uBibliorum
Subject(s): Wild flowers; Deep learning; Development stages; Computer vision; Machine learning
Wild flowers and plants play an important role in protecting biodiversity and providing various ecosystem services. However, some of them are endangered or threatened and are entitled to preservation and protection. This study represents a first step to develop a computer vision system and a supporting mobile app for detecting and monitoring the development stages of wild flowers and plants, aiming to contribute to their preservation. It first introduces the related concepts. Then, surveys related work and categorizes existing solutions presenting their key features, strengths, and limitations. The most promising solutions and techniques are identified. Insights on open issues and research directions in the topic are also provided. This paper paves the way to a wider adoption of recent results in computer vision techniques in this field and for the proposal of a mobile application that uses YOLO convolutional neural networks to detect the stages of development of wild flowers and plants.
