WoodBR-Cross is a new public dataset of 4,875 wood surface images grouped into 38 taxonomic classes and linked to 303 distinct trees, with a species-level subset of 3,406 images across 20 classes and 214 trees. The benchmark introduces three tasks that compare duplicate-aware image partitions, species subset partitions, and individual-tree-disjoint partitions, ensuring no tree appears in both training and test sets within a fold. Experiments with ResNet‑50, ConvNeXt‑Tiny, and ViT‑B/16 show that ConvNeXt‑Tiny and frozen DINOv2 features achieve macro‑F1 scores of 0.8621 and 0.9000, respectively, on the tree‑level disjoint split, providing realistic estimates of performance on unseen trees.
By Daniel Casanova, Alexandre Bahia Gontijo, Arnaldo Candido Junior, Silvana Nisgoski, Pedro Luiz de Paula Filho
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
By David-Alexandre Duclos, William Guimont-Martin, Gabriel Jeanson, Arthur Larochelle-Tremblay, Martine Lapointe, Th\'eo Defosse, Fr\'ed\'eric Moore, Philippe Nolet, Fran\c{c}ois Pomerleau, Philippe Gigu\`ere
FOR-species20K is the largest public LiDAR benchmark for tree‑species classification, combining 25 datasets across 33 species. A multi‑resolution, multi‑view ConvNeXtV2 model raised the hidden‑test accuracy from 79.5 % to 85.0 %, placing first at submission. However, cross‑source experiments show that this score does not reflect true source‑disjoint generalization, with performance dropping dramatically when species are held out by source.
By Jae Joong Lee, Bedrich Benes
The paper presents a deep learning approach for detecting woody clearing using bitemporal Sentinel‑2 imagery from New South Wales, Australia. By introducing a loss‑scaling coefficient, the authors align the model’s objective with end‑user metrics, boosting precision and recall. They further demonstrate zero‑shot transfer to woody regrowth and segmentation tasks, achieving significant error reductions and high F1 scores through image augmentation and generation techniques.
By Kal Backman, Jared Wood, Adam Roff
The study presents a new approach to detect Christmas tree plantations in high‑resolution aerial imagery, treating the task as a rare‑target semantic segmentation problem. It introduces a Hard Negative Mining strategy that significantly improves precision‑recall performance, achieving an IoU of 0.733 and an F1‑score of 0.846 on a 2020 test set. Temporal transfer experiments demonstrate the model’s ability to generalize across years, while large‑scale validation highlights the challenge posed by the plantations’ small spatial footprint.
By Francesca Razzano, Emanuele Dalsasso, Adrien Baysse-Lain\'e, Silvia Liberata Ullo, Gilda Schirinzi, Jocelyn Chanussot
arXiv:2606.20223v2 Announce Type: replace
Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Am...
By Hugo Magaldi, Theau d'Audiffret, Etienne Francois Akomo-Okoue, Bala Amarasekaran, Naomi Anderson, Claire Auger, Noemie Cappelle, Daniel Cornelis, Raphael Cornette, Tobias Deschner, Gabriel Dubus, Davy Fonteyn, Rosa M. Garriga, Jennifer Hatlauf, Innocent Kasekendi, Raymond Katumba, Aram Kazandjian, Alfred Ngomanda, Stephan Ntie, Simone Pika, Xavier Rufray, Harold Rugonge, John Justice Tibesigwa, Peter van Lunteren, Hadrien Vanthomme, Joeri A. Zwerts, Sabrina Krief
arXiv:2609.26549v1 Announce Type: new
Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...
By Thomas Pitts, Kunqi Li, Bin Liang
arXiv:2608. 11759v1 Announce Type: cross Abstract: Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data.
By Giancarlo Sportelli, Nicola Belcari, Roberta Pace, Umberto Bernardo, Sharmin Sultana, Alessandra Toncelli, Matteo Giaccone
A100 is a hybrid CNN‑Transformer vision foundation model trained on 3 million unlabeled agricultural images using self‑supervised techniques (DINO, iBOT, KoLeo). It achieves superior crop classification (95.37 % with a frozen backbone) and phenotyping accuracy, and after fine‑tuning reaches 99.90 % on 32‑class crop identification and 99.02 % on 44‑class disease detection. With only 1 % of labeled data, A100 attains 98.01 % plant classification accuracy, outperforming existing baselines and other agricultural foundation models by significant margins.
By Mayur Pratim Das, Ajai Dubey, Aksshay Mathew P, Sana Afreen, Rishi Kumar Soni, Deepak Meena, Pratibha Kumari, Mukesh Saini
The study benchmarks Vision Transformers (ViTs) against convolutional neural networks (CNNs) for fine‑grained orchid genus identification in New Guinea’s species‑rich, data‑poor flora. Using a two‑stage system that first predicts genus and then retrieves similar species images, the authors fine‑tuned four pretrained backbones on 16,701 photographs from 120 genera and 1,350 species. The self‑supervised ViT DINOv2 achieved the highest genus accuracy (macro top‑1 66.9 %) and outperformed both CNNs and a domain‑matched pretrained ViT, demonstrating strong species retrieval and open‑set detection capabilities.
By Reza Saputra, Diah Harnoni Apriyanti, Andr\'e Schuiteman, Kurt Metzger, Ashley Field, Katharina Nargar, William Edwards
MYOPIA is a benchmark that evaluates tree-based reasoning in vision‑language models (VLMs) using four human‑refined decision trees for GTSRB, MNIST, CIFAR‑10, and MSCOCO. The study shows that tree traversal consistently underperforms zero‑shot prompting across GPT‑4o, Qwen‑VL Max, and LLaMA‑3.2 11B Vision, with significant gaps between class‑attribute recall and image grounding. Diagnostics reveal that early incorrect decisions and answer extraction losses drive this performance drop, and node‑level analysis links split entropy to lower accuracy.
By Islam Mesabah, Sary Elmansoury, Gerrit Gro{\ss}mann, Peter Neigel, Sebastian J. Vollmer
arXiv:2609.10469v1 Announce Type: new
Abstract: Automated plant disease diagnosis is increasingly deployed on farmer-held devices in regions where agronomic expertise is scarce and network connectivi...
By Md. Abdullah Mandal, Saad Ahmed, Md. Khalid Syfullah