arXiv:2410.07421v2 Announce Type: replace Abstract: Instance segmentation is a core computer vision task with great practical significance. Recent advances, driven by large-scale benchmark datasets,...
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...
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
arXiv:2609.12350v1 Announce Type: new Abstract: Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that ar...
LeafTrackNet is a deep learning framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network to track individual leaves over time. The authors introduce CanolaTrack, a large benchmark dataset of 5,704 RGB images with 31,840 annotated leaf instances from 184 canola plants. When evaluated without prior fine‑tuning, LeafTrackNet outperforms existing methods on CanolaTrack, KOMATSUNA, and MSU‑PID datasets, achieving HOTA scores of 88.03, 87.33, and 74.20 respectively.
The paper introduces MCLC‑NET, a multimodal continual learning framework for leaf counting that sequentially learns tasks using a memory buffer to retain key samples. It also presents MMLC, a new real‑world dataset containing RGB, depth, and thermal images across different crops and environmental conditions, organized in crop‑wise, time‑wise, and mixed orderings. Experiments show that MCLC‑NET outperforms existing methods on all three task orderings, achieving the lowest average mean squared errors.
arXiv:2609.21059v1 Announce Type: cross Abstract: Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent adv...
arXiv:2602. 10045v2 Announce Type: replace-cross Abstract: Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is no guarantee that a predicted mask is close to the ground truth.
arXiv:2603.27519v4 Announce Type: replace Abstract: Image-based plant phenotyping depends on dense structural understanding of crops, yet pixel-level annotation remains expensive across species, orga...
The paper introduces BalSAM, a model that combines the Segment Anything Model (SAM) with Digital Surface Model (DSM) elevation data to improve tree crown instance segmentation from high‑resolution drone imagery. Experiments across boreal plantations, temperate forests, and tropical forests show that while off‑the‑shelf SAM does not beat a custom Mask R-CNN, fine‑tuning SAM end‑to‑end and incorporating DSM information yield promising results, especially for plantation sites.
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.
arXiv:2504.10201v3 Announce Type: replace Abstract: In this paper, we introduce a synthetic image generator relying on a few simple principles, specifically focusing on geometric modeling, textures,...