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
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
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
The paper introduces a dual‑stream Swin ResNet hybrid architecture that combines CNN and Transformer streams for cross‑grade banana variety classification. It employs KL‑divergence‑based self‑distillation to align the probabilistic outputs of the two streams, achieving a mean accuracy of 81.25% on a dataset of 31,678 images across three varieties and three quality grades. Experiments show the hybrid model outperforms both CNN and Transformer baselines, improving feature transferability, attention localization, and optimization stability.
By Khush Kataruka, Mohammad Muzammila, Zahid Akhtar, Raghavendra Ramachandra, Praveen Kumar Chandaliya
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
The paper introduces AGS-PlantSeg, a few‑shot 3D plant organ segmentation method that uses the frozen Utonia foundation model and Adaptive Granularity Selection (AGS) to dynamically choose optimal spatial granularity for each plant. By extracting tailored geometric features for a lightweight MLP head, AGS-PlantSeg achieves superior cross‑species generalization, reaching an average mIoU of 88.9% and outperforming fixed‑granularity baselines by 2.5 points across PLANesT‑3D, Pheno4D, and Crops3D datasets. The approach requires minimal annotated data yet competes with fully supervised, plant‑specific architectures.