Object Detection (OD) is a computer vision technology to locate and classify objects in images and videos, which has the potential to significantly improve efficiency in precision agriculture. However, applying OD in agriculture, such as with crops images from Unmanned Aerial Vehicles (UAVs), is still faced with obstacles including image preprocessing and labeling, model development and deployment. 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 detection models, and deploying the model. Ladder was designed with an interactive recurrent framework that leverages predictions from a pre-trained model as the initial image labeling. After adding human labels, the newly labeled images can be added into the training data to retrain the model. Users can deploy trained detection 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 UAV. Ladder employs the model 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, which remove barriers of applying in-field images with supper high resolution in crop breeding.
