This dataset consists of four sets of flower images, from three different species: apple, peach, and pear, and accompanying ground truth images. The images were acquired under a range of imaging conditions. These datasets support work in an accompanying paper that demonstrates a flower identification algorithm that is robust to uncontrolled environments and applicable to different flower species. While this data is primarily provided to support that paper, other researchers interested in flower detection may also use the dataset to develop new algorithms. Flower detection is a problem of interest in orchard crops because it is related to management of fruit load. Funding provided through ARS Integrated Orchard Management and Automation for Deciduous Tree Fruit Crops.
- ZIPPear
- ZIPAppleB images
- ZIPAppleA images
- ZIPAppleA labels
- ZIPAppleB labels
- TXTTraining image names from Apple A dataset
- ZIPPeach
- TXTValidation image names from Apple A dataset
- ZIPPeach labels
- ZIPPear labels
- ZIPApple A Labeled images from training set