Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Computer Engineering
Multispecies Fruit Flower Detection Using A Refined Semantic Segmentation Network, Philipe A. Dias, Amy Tabb, Henry P. Medeiros
Multispecies Fruit Flower Detection Using A Refined Semantic Segmentation Network, Philipe A. Dias, Amy Tabb, Henry P. Medeiros
Electrical and Computer Engineering Faculty Research and Publications
In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance …
Apple Flower Detection Using Deep Convolutional Networks, Philipe A. Dias, Amy Tabb, Henry P. Medeiros
Apple Flower Detection Using Deep Convolutional Networks, Philipe A. Dias, Amy Tabb, Henry P. Medeiros
Electrical and Computer Engineering Faculty Research and Publications
To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network …