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: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
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
The paper evaluates how well Vision Transformers (ViTs) can handle token merging techniques—specifically ToMe and Mutual Pair Merging—across wheat phenotyping tasks such as growth-stage classification, wheat-head detection, and wheat-organ segmentation. It benchmarks task quality, throughput, token count, and GPU memory usage, including tests on a Raspberry Pi 5. Results show that classification is highly tolerant to token merging, whereas detection and segmentation suffer due to factors like repeated instances, thin organs, dense boundaries, and runtime overhead, and that optimized attention backends can negate apparent speed gains.
By Simon Rav\'e, Pejman Rasti, David Rousseau
CropCop is a closed‑set plant‑health recognition system covering 120 operational classes, built from a rigorously audited dataset of 109,107 images after removing 3,233 duplicate relationships. The model, based on a fine‑tuned DINOv3 ConvNeXt‑Tiny, achieves 98.51% accuracy and 96.87% macro‑F1 on a locked internal test, while a quantised MobileNetV4 variant reaches 98.46% accuracy and 96.23% macro‑F1 in a 22.60 MiB runtime artifact. Validation‑only post‑training quantisation and a compact ExecuTorch/XNNPACK PTE ensure high fidelity between the trained model and its deployed form, with minimal decision changes between the INT8 graph and the final artifact.
By Rana Muhammad Ahmed, Sabahat Abbas
arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.
By Junxiong Zhou, Xuechen Li, Chonghao Qiu, Lang Qiao, Xiaowei Jia, Qi Yang, Chishan Zhang, Leikun Yin, Nanshan You, Vipin Kumar, David Mulla, Ce Yang, Zhenong Jin, Licheng Liu
arXiv:2608.21254v1 Announce Type: cross
Abstract: Accurate agricultural weed detection in real-world field conditions is essential for precision agriculture, enabling targeted intervention and reduci...
By Nikhilesh Prabhakar, Pranuthi Tenali, Wilfredo Abudeye Fernandez, Shekhar Borah, Athresh Karanam, Erik Blasch, Prabha Sundaravadivel, Sriraam Natarajan
arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
By Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor
arXiv:2608.30088v1 Announce Type: new
Abstract: Accurate detection of tomato growth stages is essential for stage-specific greenhouse management and precision agriculture. In Bhutan, greenhouse culti...
By Sherab Gocha, Sou Nobukawa
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
arXiv:2608.20663v1 Announce Type: new
Abstract: Public datasets for agricultural disease detection are usually judged fit for use from reported metrics, which say nothing about whether the annotation...
By Pushuo Wang (Shenyang Institute of Technology)