UAV cotton flower counting dataset

Many perennial plants make important contributions to agroeconomies and agroecosystems, but have complex architecture and/or long flowering duration that hinders measurement and selection. Iteratively tracking productivity over a long flowering/fruiting season may permit the identification of genetic factors conferring different reproductive strategies that might be successful in different environments, ranging from rapid early maturation that avoids stresses, to late maturation that utilizes the full seasonal duration to maximize productivity. In cotton, a perennial plant that is generally cultivated as an annual crop, we apply aerial imagery and deep learning methods to novel and stable genetic stocks, identifying genetic factors influencing the duration and rate of fruiting. While these factors may have different relationships with crop productivity and quality in different environments, their determination adds potentially important information to breeding decisions. With transfer l..., Data were collected twice a week from 2021-08-09 through 2021-11-05. Some sessions were skipped due to inclement weather conditions, resulting in a total of 23 sessions.  Data collection was halted after the first overnight freeze, after which most of the plants showed a significant drop in the number of flowers produced. Images of the field were collected using a Matrice 100 drone (DJI, Shenzhen, China) fitted with a custom mount and equipped with a Lumix G7 camera (Panasonic Corporation of North America, Newark, N.J., USA) and a 17 mm lens. The drone was flown at a height of 15 meters, resulting in a GSD of 0.23 cm/px. In a few cases, technical issues with the Matrice 100 data required the substitution of equivalent data from a DJI Phantom 4 Pro v2 drone. The images were geo-referenced using a total of six ground control points distributed throughout the field, with their exact positions measured using a Realtime-Kinematic (RTK) GPS., , # UAV Cotton Flower Counting Dataset

[https://doi.org/10.5061/dryad.5qfttdzhb](https://doi.org/10.5061/dryad.5qfttdzhb)

## Description of the data and file structure

These data were collected with a UAV at a cotton breeding field in Watkinsville, Georgia in 2021. The field was scanned twice weekly, and the data was analyzed using an automated pipeline:

* The raw images were stitched together into an orthophoto
* Individual plot-level crops were extracted
* An object detector was used to detect flowers in the plot images
* The flower counts were analyzed in order to produce various phenotyping metrics

This dataset contains the raw, annotated images used to train the object detector. In addition to the data we collected in 2021, this dataset also includes some additional data, including UAV images from previous years (2016, 2018) as well as some plot-level images collected from a tractor. All images contain bounding box annotations for each flower.

We have also included a model trai...