Global Wheat Head Dataset 2021: more diversity to improve the benchmarking of wheat head localization methods

no code implementations • 17 May 2021 • Etienne David, Mario Serouart, Daniel Smith, Simon Madec, Kaaviya Velumani, Shouyang Liu, Xu Wang, Francisco Pinto Espinosa, Shahameh Shafiee, Izzat S. A. Tahir, Hisashi Tsujimoto, Shuhei Nasuda, Bangyou Zheng, Norbert Kichgessner, Helge Aasen, Andreas Hund, Pouria Sadhegi-Tehran, Koichi Nagasawa, Goro Ishikawa, Sébastien Dandrifosse, Alexis Carlier, Benoit Mercatoris, Ken Kuroki, Haozhou Wang, Masanori Ishii, Minhajul A. Badhon, Curtis Pozniak, David Shaner LeBauer, Morten Lilimo, Jesse Poland, Scott Chapman, Benoit de Solan, Frédéric Baret, Ian Stavness, Wei Guo

We now release a new version of the Global Wheat Head Detection (GWHD) dataset in 2021, which is bigger, more diverse, and less noisy than the 2020 version.

Benchmarking Head Detection

AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images

no code implementations • 17 Jul 2020 • Jordan Ubbens, Tewodros Ayalew, Steve Shirtliffe, Anique Josuttes, Curtis Pozniak, Ian Stavness

Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques.

Plant Phenotyping

Crop Lodging Prediction from UAV-Acquired Images of Wheat and Canola using a DCNN Augmented with Handcrafted Texture Features

1 code implementation • 18 Jun 2019 • Sara Mardanisamani, Farhad Maleki, Sara Hosseinzadeh Kassani, Sajith Rajapaksa, Hema Duddu, Menglu Wang, Steve Shirtliffe, Seungbum Ryu, Anique Josuttes, Ti Zhang, Sally Vail, Curtis Pozniak, Isobel Parkin, Ian Stavness, Mark Eramian

In this paper, we propose a deep convolutional neural network (DCNN) architecture for lodging classification using five spectral channel orthomosaic images from canola and wheat breeding trials.

General Classification Transfer Learning

DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning

1 code implementation • 30 Sep 2017 • Shubhra Aich, Anique Josuttes, Ilya Ovsyannikov, Keegan Strueby, Imran Ahmed, Hema Sudhakar Duddu, Curtis Pozniak, Steve Shirtliffe, Ian Stavness

In this paper, we investigate estimating emergence and biomass traits from color images and elevation maps of wheat field plots.