Flower Detection

Flower detection research focuses on automatically identifying and classifying flowers in images, primarily to improve agricultural practices and advance botanical studies. Current approaches leverage deep learning architectures, such as convolutional neural networks (CNNs) and their variations (e.g., YOLOv5), often enhanced by ensemble learning or transfer learning techniques to boost accuracy and robustness across diverse flower species and imaging conditions. These advancements enable applications like precision agriculture (e.g., automated pollination and thinning) and improved plant taxonomy through efficient, large-scale flower identification and breed detection. Furthermore, incorporating novel sensing modalities, such as UV imaging, is improving the accuracy and detail of flower detection.