Merge pull request #920 from henrykironde/publist

Update citation info
weecology · Feb 11, 2025 · 5b70d7e · 5b70d7e
2 parents 0ac5f6f + e7e0d11
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diff --git a/docs/user_guide/14_cite.rst b/docs/user_guide/14_cite.rst
@@ -13,13 +13,29 @@ Software
    updated <https://doc.arcgis.com/en/pretrained-models/latest/imagery/introduction-to-tree-detection.htm>`__
 -  `Agisoft - Automatic detection of objects on
    orthomosaics <https://agisoft.freshdesk.com/support/solutions/articles/31000162552-automatic-detection-of-objects-on-orthomosaic>`__
+-  `TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models <https://www.sciencedirect.com/science/article/pii/S235271102500038X>`__, `GitHub <https://github.com/afruizh/TreeEyed>`__
 
 Research
 --------
 
+2025
+~~~~
+
+Xu, T., Wang, T., & Skidmore, A. K. (2025). Fusing aerial photographs and
+airborne LiDAR data to improve the accuracy of detecting individual trees
+in urban and peri-urban areas. Urban Forestry & Urban Greening, 105, 128696.
+https://doi.org/10.1016/j.ufug.2025.128696
+
+
 2024
 ~~~~
 
+Kurbanov, R. K., Dalevich, A. N., Dorokhov, A. S., Zakharova, N. I.,
+Rebouh, N. Y., Kucher, D. E., Litvinov, M. A., & Ali, A. M. (2024).
+Monitoring of Heracleum sosnowskyi Manden Using UAV Multisensors: Case Study
+in Moscow Region, Russia. Agronomy, 14(10), 2451.
+https://doi.org/10.3390/agronomy14102451
+
 Afsar, M.M., Bakhshi, A.D., Hussain, E. et al. A deep learning-based
 framework for object recognition in ecological environments with dense
 focal loss and occlusion. Neural Comput & Applic (2024).
@@ -177,4 +193,4 @@ Sensing 14 (2021): 3927-3936.
 Weinstein, B. G., S. Marconi, S. Bohlman, A. Zare, and E. White. 2019.
 Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised
 Deep Learning Neural Networks. Remote Sensing 11:1309.
-https://doi.org/10.3390/rs11111309
+https://doi.org/10.3390/rs11111309
diff --git a/fetch_google_scholar.py b/fetch_google_scholar.py
@@ -6,9 +6,13 @@
 
 
 def get_citation_count(doi):
-    search_query = scholarly.search_pubs(doi)
-    paper = next(search_query, None)
-    return paper['num_citations'] if paper else None
+    try:
+        search_query = scholarly.search_pubs(doi)
+        paper = next(search_query, None)
+        return paper['num_citations'] if paper else None
+    except Exception as e:
+        print(f"Fetch google scholar did not run as expected: {e}")
+        return None
 
 
 citation_count = get_citation_count(DOI)
@@ -25,4 +29,4 @@ def get_citation_count(doi):
 
     print(f"Updated citation count: {citation_count}")
 else:
-    print("Failed to fetch citation count")
+    print("Skipping citation count update")