  Weed management plays an important role in many modern agricultural
applications. Conventional weed control methods mainly rely on chemical
herbicides or hand weeding, which are often cost-ineffective, environmentally
unfriendly, or even posing a threat to food safety and human health. Recently,
automated/robotic weeding using machine vision systems has seen increased
research attention with its potential for precise and individualized weed
treatment. However, dedicated, large-scale, and labeled weed image datasets are
required to develop robust and effective weed identification systems but they
are often difficult and expensive to obtain. To address this issue, data
augmentation approaches, such as generative adversarial networks (GANs), have
been explored to generate highly realistic images for agricultural
applications. Yet, despite some progress, those approaches are often
complicated to train or have difficulties preserving fine details in images. In
this paper, we present the first work of applying diffusion probabilistic
models (also known as diffusion models) to generate high-quality synthetic weed
images based on transfer learning. Comprehensive experimental results show that
the developed approach consistently outperforms several state-of-the-art GAN
models, representing the best trade-off between sample fidelity and diversity
and highest FID score on a common weed dataset, CottonWeedID15. In addition,
the expanding dataset with synthetic weed images can apparently boost model
performance on four deep learning (DL) models for the weed classification
tasks. Furthermore, the DL models trained on CottonWeedID15 dataset with only
10% of real images and 90% of synthetic weed images achieve a testing accuracy
of over 94%, showing high-quality of the generated weed samples. The codes of
this study are made publicly available at
https://github.com/DongChen06/DMWeeds.
