Computer Vision Techniques for Automated Detection of Wildflower Growth Stages

This paper presents a new methodology that utilizes computer vision techniques for the automatic detection and classification of stages in wildflower growth. Wild plant development monitoring is essential for ecological studies and biodiversity preservation as well as for environmental management. The approach makes use of novel state-of-the-art computer vision algorithms for the processing of high-resolution images of wildflowers against diverse background scenes. A labeled dataset of wildflower species, at various growth stages, will aid in supervised learning. The system identifies and accurately classifies the growth stage using CNNs and image segmentation algorithms to perform feature extraction. This paper works under computer vision in ecology, thereby simplifying the monitoring of the development processes of wild plant growth while conserving biodiversity.