Precision agriculture involves the application of advanced technologies to
improve agricultural productivity, efficiency, and profitability while
minimizing waste and environmental impact. Deep learning approaches enable
automated decision-making for many visual tasks. However, in the agricultural
domain, variability in growth stages and environmental conditions, such as
weather and lighting, presents significant challenges to developing deep
learning-based techniques that generalize across different conditions. The
resource-intensive nature of creating extensive annotated datasets that capture
these variabilities further hinders the widespread adoption of these
approaches. To tackle these issues, we introduce a semi-self-supervised domain
adaptation technique based on deep convolutional neural networks with a
probabilistic diffusion process, requiring minimal manual data annotation.
Using only three manually annotated images and a selection of video clips from
wheat fields, we generated a large-scale computationally annotated dataset of
image-mask pairs and a large dataset of unannotated images extracted from video
frames. We developed a two-branch convolutional encoder-decoder model
architecture that uses both synthesized image-mask pairs and unannotated
images, enabling effective adaptation to real images. The proposed model
achieved a Dice score of 80.7\% on an internal test dataset and a Dice score of
64.8\% on an external test set, composed of images from five countries and
spanning 18 domains, indicating its potential to develop generalizable
solutions that could encourage the wider adoption of advanced technologies in
agriculture.

利用深度学习方法，通过半自我监督域适应技术在精确农业领域实现农作物图像的自动化标注，以及在不同环境条件下适应性强的解决方案，以推动先进技术在农业领域的广泛应用。