RootQuantV2 adapts a frozen DINOv3 Vision Transformer to predict root length and surface area directly from minirhizotron images, eliminating the need for manually traced masks. By training only 11.9 M parameters (3.78 % of the model), it achieves R² values of 0.950 for length and 0.930 for area, improving RMSE by 24.3 % and 20.7 % over the previous RootQuant CNN approach. The method repurposes existing numeric archives of root traits for high‑throughput, automated phenotyping in field‑grown crops.
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...
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."
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 presents a unified compression framework for Vision Transformers aimed at on‑device plant disease detection in resource‑constrained agricultural settings. It combines Hessian‑Balanced Adaptive Block Pruning, quantization, and attention‑based knowledge distillation, evaluating each component separately before integrating the best performers into a deployment pipeline. On a chilli disease dataset, the compressed models achieve accuracy comparable to the FP32 baseline while reducing model size by 74‑98 %, and the full pipeline attains a 54.5× size reduction to 6.01 MB with 95.13 % accuracy.
arXiv:2608. 16661v1 Announce Type: cross Abstract: Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spectra as one-dimensional sequences, which limits how they capture long-range inter-band dependencies.
The paper investigates patch‑wise supervision for detecting AI‑generated images, proposing a shared backbone that classifies explicit crops with individual losses and averages patch probabilities only during inference. This approach eliminates the need for handcrafted residual filtering or learned image‑level fusion modules. Experiments across single‑patch selection, multiple generator collections, and four CNN and Transformer backbones show that patch‑wise variants outperform whole‑image counterparts on the GenImage dataset, while also exploring factors such as supervision granularity, source resolution, crop size, and inference coverage.
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...
Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spectra as one-dimensional sequences, which limits how they capture long-range inter-band dependencies. We asked whether transforming 1D spectra into 2D image representations improves multi-trait prediction with convolutional neural networks (CNN).
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...
arXiv:2505.18930v2 Announce Type: replace-cross Abstract: Early weed identification is crucial for effective management and control, and researchers, agronomists, and technology developers are increa...
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.
arXiv:2607. 14509v1 Announce Type: cross Abstract: This paper describes DS@GT ARC's third-place solution to the PlantCLEF 2026 challenge on multi-species plant identification in vegetation quadrat images, where systems must predict every species present in high-resolution (~3000 x 3000 pixel) plot photographs while training only on single-label images of individual plants.