  Object Detection (OD) is a computer vision technology that can locate and
classify objects in images and videos, which has the potential to significantly
improve efficiency in precision agriculture. To simplify OD application
process, we developed Ladder - a software that provides users with a friendly
graphic user interface (GUI) that allows for efficient labelling of training
datasets, training OD models, and deploying the trained model. Ladder was
designed with an interactive recurrent framework that leverages predictions
from a pre-trained OD model as the initial image labeling. After adding human
labels, the newly labeled images can be added into the training data to retrain
the OD model. With the same GUI, users can also deploy well-trained OD models
by loading the model weight file to detect new images. We used Ladder to
develop a deep learning model to access wheat stripe rust in RGB (red, green,
blue) images taken by an Unmanned Aerial Vehicle (UAV). Ladder employs OD to
directly evaluate different severity levels of wheat stripe rust in field
images, eliminating the need for photo stitching process for UAVs-based images.
The accuracy for low, medium and high severity scores were 72%, 50% and 80%,
respectively. This case demonstrates how Ladder empowers OD in precision
agriculture and crop breeding.
