  In this study, we explore the explainability of neural networks in
agriculture and forestry, specifically in fertilizer treatment classification
and wood identification. The opaque nature of these models, often considered
'black boxes', is addressed through an extensive evaluation of state-of-the-art
Attribution Maps (AMs), also known as class activation maps (CAMs) or saliency
maps. Our comprehensive qualitative and quantitative analysis of these AMs
uncovers critical practical limitations. Findings reveal that AMs frequently
fail to consistently highlight crucial features and often misalign with the
features considered important by domain experts. These discrepancies raise
substantial questions about the utility of AMs in understanding the
decision-making process of neural networks. Our study provides critical
insights into the trustworthiness and practicality of AMs within the
agriculture and forestry sectors, thus facilitating a better understanding of
neural networks in these application areas.
