End‐to‐End Convolutional Neural Networks Training on Stable Diffusion Deepfake Imagery for Precise Early Weed Detection

The integration of optical sensor systems with advancements in artificial intelligence for image processing presents a promising avenue for the broader implementation of site‐specific weed management (SSWM). Convolutional neural networks (CNNs) have significantly advanced the field of weed detection. However, their effectiveness remains contingent upon the availability of extensive, manually annotated image datasets, which require considerable time and effort. This study introduces an innovative methodology to overcome these limitations by training detection models exclusively with artificial images generated using stable diffusion (SD), a state‐of‐the‐art generative artificial intelligence model. The research was conducted in commercial tomato fields naturally infested with three weed species: Solanum nigrum L., Portulaca oleracea L. and Setaria verticillata L. An artificial dataset comprising 9000 artificially generated images was developed to train four CNN‐based object detection models: YOLOv8, YOLOv9, YOLO‐NAS and RetinaNet. The performance of these models was subsequently validated against real‐world weed images. The results demonstrated high detection accuracy, with YOLOv8 achieving a mean Average Precision (mAP) of 94.3%, YOLO‐NAS reaching 95.5%, YOLOv9 attaining 95.2% and RetinaNet obtaining a mAP of 86.3%. The high quality and variability of the artificial images produced via SD demonstrate the potential of generative models to accelerate traditional data acquisition. This approach provides a scalable and efficient pathway for rapidly deploying weed detection models and adapting them to new crop‐weed scenarios. Notably, it offers a means to significantly reduce the effort and resources required for collecting and annotating large real‐world datasets, without compromising detection precision.