Data-Centric Digital Agriculture: A Perspective
Ribana Roscher Lukas Roth Cyrill Stachniss Achim Walter
In response to the increasing global demand for food, feed, fiber, and fuel,
digital agriculture is rapidly evolving to meet these demands while reducing
environmental impact. This evolution involves incorporating data science,
machine learning, sensor technologies, robotics, and new management strategies
to establish a more sustainable agricultural framework. So far, machine
learning research in digital agriculture has predominantly focused on
model-centric approaches, focusing on model design and evaluation. These
efforts aim to optimize model accuracy and efficiency, often treating data as a
static benchmark. Despite the availability of agricultural data and
methodological advancements, a saturation point has been reached, with many
established machine learning methods achieving comparable levels of accuracy
and facing similar limitations. To fully realize the potential of digital
agriculture, it is crucial to have a comprehensive understanding of the role of
data in the field and to adopt data-centric machine learning. This involves
developing strategies to acquire and curate valuable data and implementing
effective learning and evaluation strategies that utilize the intrinsic value
of data. This approach has the potential to create accurate, generalizable, and
adaptable machine learning methods that effectively and sustainably address
agricultural tasks such as yield prediction, weed detection, and early disease
identification