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Object Detection (OD) is a computer vision technology that can locate andclassify objects in images and videos, which has the potential to significantlyimprove efficiency in precision agriculture. To simplify OD applicationprocess, we developed Ladder - a software that provides users with a friendlygraphic user interface (GUI) that allows for efficient labelling of trainingdatasets, training OD models, and deploying the trained model. Ladder wasdesigned with an interactive recurrent framework that leverages predictionsfrom a pre-trained OD model as the initial image labeling. After adding humanlabels, the newly labeled images can be added into the training data to retrainthe OD model. With the same GUI, users can also deploy well-trained OD modelsby loading the model weight file to detect new images. We used Ladder todevelop 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 todirectly evaluate different severity levels of wheat stripe rust in fieldimages, 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 precisionagriculture and crop breeding.

Ladder: A software to label images, detect objects and deploy models recurrently for object detection