1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2025
TasselNetV4: A vision foundation model for cross-scene, cross-scale, and cross-species plant counting
Xiaonan Hu, Xuebing Li, Jinyu Xu +8
Accurate plant counting provides valuable information for agriculture such as crop yield prediction, plant density assessment, and phenotype quantification. Vision-based approaches…
cs.CV2025★ 1 cited
FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data
Bing Han, Chen Zhu, Dong Han +22
Vision-driven field monitoring is central to digital agriculture, yet models built on general-domain pretrained backbones often fail to generalize across tasks, owing to the intera…
eess.IV2025
Global Rice Multi-Class Segmentation Dataset (RiceSEG): A Comprehensive and Diverse High-Resolution RGB-Annotated Images for the Development and Benchmarking of Rice Segmentation Algorithms
Junchi Zhou, Haozhou Wang, Yoichiro Kato +21
Developing computer vision-based rice phenotyping techniques is crucial for precision field management and accelerating breeding, thereby continuously advancing rice production. Am…