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 paper introduces an automated pipeline that reconstructs editable 3D procedural models of field‑grown maize directly from raw 3D point clouds, eliminating the need for manual tuning or species‑specific training data. It uses a vision‑language model to annotate leaf midlines in rendered views, then applies deterministic geometric algorithms and differentiable NURBS fitting to generate accurate plant descriptors and refine leaf surfaces. The method achieves a median Chamfer distance of 5.4 mm on 100 diverse maize plants and recovers 99.4% of reference leaves with high overlap, outperforming previous semi‑automated approaches.
By Mozhgan Hadadi, Talukder Z. Jubery, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
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
AT‑ViT is a dual‑branch Vision Transformer that processes both raw herbarium scans and their segmentation masks through a multi‑scale, multi‑view cross‑attention fusion. It uses a mask‑guided patch weighting scheme to emphasize plant regions and suppress background artifacts, thereby encouraging plant‑centric representations. In trait classification tasks such as leaf base shape and thorns, AT‑ViT consistently outperforms baselines, improves spatial attention grounding (IoU_p +15.66 to +18.03 pp, IoU_b –27.92 to –31.02 pp), and shows greater robustness to synthetic background perturbations, surpassing ResNet101 by up to +32.32 accuracy points and CrossViT by up to +5.07 points.
whyItMatters":"The model addresses shortcut learning caused by background cues in herbarium images, leading to more accurate and interpretable plant trait recognition."
By Amani Sedrat, Takieddine Chehhat, Youcef Sklab, Hanane Ariouat, Abderrazak Sebaa, Eric Chenin, Jean-Daniel Zucker, Edi Profiti
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...
By Shuai Xiang, James Burridge, Shouyang Liu, Hao Lu, Tokihiro Fukatsu, Yinqiang Zheng, Wei Guo
arXiv:2608.28216v1 Announce Type: new
Abstract: Locating a specific object instance in a cluttered scene using a single reference image and a short description, and reporting when that instance is ab...
By Kishor Datta Gupta, Ahmed Rafi Hasan, Md. Mahfuzur Rahman, Md. Sadman Haque, Mohd Ariful Haque
PlantC2USeg is a deep transfer‑learning framework that uses cross‑scale consistency learning and an information‑restricted decoder to improve plant point cloud segmentation. It achieves state‑of‑the‑art performance on Soybean3D and ShapeNet Part, and demonstrates strong few‑shot generalization across species and sensing conditions. The method reduces the need for large annotated datasets and lowers adaptation overhead for new plant species.
By Yu Tian, Xintong Jiang, Jan Franklin Adamowski, Shiv O. Prasher, Shangpeng Sun
AgroGround is a large‑scale dataset that links plant disease and agricultural target labels to image regions, enabling models to both recognize and localize these targets. It was created by automatically converting eight agricultural VQA datasets into 794,850 grounded instruction examples, including healthy images for negative supervision. Fine‑tuning a shared vision‑language model on these instructions improves recognition, joint accuracy, and healthy‑image abstention, outperforming its annotation teacher on both the AgroGround benchmark and the external PlantSeg test set.
By Abdulla Alshehhi, Zongyan Han, Rao Muhammad Anwer
The paper presents a benchmark to test whether vision‑language models can produce plant simulation configurations from images using in‑context learning. It focuses on cowpea plot reconstruction, requiring the models to output structured JSON that includes field and plant details. Open‑source multimodal models from the Gemma 4 and Qwen3.5 families are evaluated on synthetic and real drone datasets, using five in‑context methods, and the results show that while VLMs can generate valid JSON and estimate key agronomic metrics, their performance varies and often lags behind dataset baselines.
By Heesup Yun, Isaac Kazuo Uyehara, Earl Ranario, Lars Lundqvist, Christine H. Diepenbrock, Brian N. Bailey, J. Mason Earles
The paper introduces a multibranch ensemble head that pairs low- and high-capacity branches for Vision Foundation Models, sampling one branch during training and averaging both at inference. This lightweight design adds negligible parameters and inference overhead while improving accuracy-efficiency trade-offs on data-scarce tasks. Applied to cucumber phenological stage classification, the DINOv2 ViT-S with the multibranch head achieves a Macro F1 of 0.978 and Cohen's Kappa of 0.971, matching larger models but with only 12.2 GFlops and 22.3 M parameters, enabling deployment on smartphones and edge devices.
By Kostiantyn Khabarlak
arXiv:2608. 00870v1 Announce Type: cross Abstract: Panoptic crop mapping requires both delineating individual agricultural parcels and assigning a crop type to each parcel from satellite image time series.
By Xuechen Li
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.
By Shanghua Liu, Majharulislam Babor, Christoph Verduyn, Breght Vandenberghe, Bruno Betoni Parodi, Cornelia Weltzien, Marina M. -C. H\"ohne