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The accurate identification of walnuts within orchards brings forth aplethora of advantages, profoundly amplifying the efficiency and productivityof walnut orchard management. Nevertheless, the unique characteristics ofwalnut trees, characterized by their closely resembling shapes, colors, andtextures between the walnuts and leaves, present a formidable challenge inprecisely distinguishing between them during the annotation process. In thisstudy, we present a novel approach to improve walnut detection efficiency,utilizing YOLOv5 trained on an enriched image set that incorporates both realand synthetic RGB and NIR images. Our analysis comparing results from ouroriginal and augmented datasets shows clear improvements in detection whenusing the synthetic images.

Walnut Detection Through Deep Learning Enhanced by Multispectral Synthetic Images