From neural networks to deep learning: Tracing the AI evolution in plant ecology - Mehalhal, Akkacha; Ainad Tabet, Mustapha (Ecological Questions, 2026)
The integration of artificial intelligence (AI) with plant ecology provides a powerful supplementary toolkit for analyzing complex environmental data, yet the structure of this rapidly growing field remains unmapped. This study employs a comprehensive bibliometric analysis of 1055 Scopus documents (1968-2025) to chart its conceptual, intellectual, and social landscape. Our analysis reveals a distinct three-phase evolution from foundational Artificial Neural Networks (ANNs) to a broad machine learning expansion, and a current surge in deep learning for applications like precision agriculture. The field’s conceptual core is built on predictive modeling, particularly for species distribution, and advanced image analytics. We highlight that this evolution has been largely driven by the increasing availability of open-source programming libraries (e.g., in R and Python) and accessible software platforms, rather than just the theoretical advancement of AI itself. The global collaboration network is multi-polar, with the USA and China acting as prominent hubs. This research maps the trajectory of AI applications in ecology and emphasizes the emerging frontier of Explainable AI (XAI), which is essential for moving beyond "black-box" predictions toward interpretable ecological insights and fundamental scientific discovery