11 citations · 24 across the 5 of their papers we have counts for
5 papers
PointRAFT: 3D deep learning for high-throughput prediction of potato tuber weight from partial point clouds
Pieter M. Blok, Haozhou Wang, Hyun Kwon Suh +3
Potato yield is a key indicator for optimizing cultivation practices in agriculture. Potato yield can be estimated on harvesters using RGB-D cameras, which capture three-dimensiona…
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…
High-throughput 3D shape completion of potato tubers on a harvester
Pieter M. Blok, Federico Magistri, Cyrill Stachniss +3
Potato yield is an important metric for farmers to further optimize their cultivation practices. Potato yield can be estimated on a harvester using an RGB-D camera that can estimat…
DODA: Adapting Object Detectors to Dynamic Agricultural Environments in Real-Time with Diffusion
Shuai Xiang, Pieter M. Blok, James Burridge +2
Object detection has wide applications in agriculture, but domain shifts of diverse environments limit the broader use of the trained models. Existing domain adaptation methods usu…
Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods
Etienne David, Mario Serouart, Daniel Smith +32
The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4,700 RGB images acquired from various acquisition platforms…