Synthesizing Training Data for Intelligent Weed Control Systems Using Generative AI

Deep Learning already plays a pivotal role in technical systems performing various crop protection tasks, including weed detection, disease diagnosis, and pest monitoring. However, the efficacy of such data-driven models heavily relies on large and high-quality datasets, which are often scarce and costly to acquire in agricultural contexts. To address the overarching challenge of data scarcity, augmentation techniques have emerged as a popular strategy to expand training data amount and variation. Traditional data augmentation methods, however, often fall short in reliably replicating real-world conditions and also lack diversity in the augmented images, hindering robust model training. In this paper, we introduce a novel methodology for synthetic image generation designed specifically for object detection tasks in the agricultural context of weed control. We propose a pipeline architecture for synthetic image generation that incorporates a foundation model called Segment Anything Model (SAM), which allows for zero-shot transfer to new domains, along with the recent generative AI-based Stable Diffusion Model. Our methodology aims to produce synthetic training images that accurately capture characteristic weed and background features while replicating the authentic style and variability inherent in real-world images with high fidelity. In view of the integration of our approach into intelligent technical systems, such a pipeline paves the way for continual self-improvement of the perception modules when put into a self-reflection loop. First experiments on real weed image data from a current research project reveal our method's capability to reconstruct the innate features of real-world weed infested scenes from an outdoor experimental setting.