Explainable AI in Grassland Monitoring: Enhancing Model Performance and
  Domain Adaptability
Shanghua Liu Anna Hedström Deepak Hanike Basavegowda Cornelia Weltzien Marina M. -C. Höhne
Grasslands are known for their high biodiversity and ability to provide
multiple ecosystem services. Challenges in automating the identification of
indicator plants are key obstacles to large-scale grassland monitoring. These
challenges stem from the scarcity of extensive datasets, the distributional
shifts between generic and grassland-specific datasets, and the inherent
opacity of deep learning models. This paper delves into the latter two
challenges, with a specific focus on transfer learning and eXplainable
Artificial Intelligence (XAI) approaches to grassland monitoring, highlighting
the novelty of XAI in this domain. We analyze various transfer learning methods
to bridge the distributional gaps between generic and grassland-specific
datasets. Additionally, we showcase how explainable AI techniques can unveil
the model's domain adaptation capabilities, employing quantitative assessments
to evaluate the model's proficiency in accurately centering relevant input
features around the object of interest. This research contributes valuable
insights for enhancing model performance through transfer learning and
measuring domain adaptability with explainable AI, showing significant promise
for broader applications within the agricultural community.