Walnut Detection Through Deep Learning Enhanced by Multispectral Synthetic Images
This work addresses a domain-specific problem for walnut orchard management, with incremental improvements in detection methods.
The study tackled the challenge of accurately detecting walnuts in orchards due to similar appearances with leaves, by using YOLOv5 trained on a dataset enhanced with synthetic RGB and NIR images, resulting in clear improvements in detection efficiency.
The accurate identification of walnuts within orchards brings forth a plethora of advantages, profoundly amplifying the efficiency and productivity of walnut orchard management. Nevertheless, the unique characteristics of walnut trees, characterized by their closely resembling shapes, colors, and textures between the walnuts and leaves, present a formidable challenge in precisely distinguishing between them during the annotation process. In this study, we present a novel approach to improve walnut detection efficiency, utilizing YOLOv5 trained on an enriched image set that incorporates both real and synthetic RGB and NIR images. Our analysis comparing results from our original and augmented datasets shows clear improvements in detection when using the synthetic images.