no code implementations • 16 May 2023 • Enyu Cai, Jiaqi Guo, Changye Yang, Edward J. Delp
In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning.
no code implementations • 8 May 2022 • Enyu Cai, Zhankun Luo, Sriram Baireddy, Jiaqi Guo, Changye Yang, Edward J. Delp
The number of panicles (or heads) of Sorghum plants is an important phenotypic trait for plant development and grain yield estimation.
no code implementations • 1 Sep 2021 • Changye Yang, Sriram Baireddy, Enyu Cai, Melba Crawford, Edward J. Delp
Unmanned Aerial Vehicles (UAVs) have become popular for use in plant phenotyping of field based crops, such as maize and sorghum, due to their ability to acquire high resolution data over field trials.
no code implementations • 15 Jul 2021 • Enyu Cai, Sriram Baireddy, Changye Yang, Melba Crawford, Edward J. Delp
Flowering time (time to flower after planting) is important for estimating plant development and grain yield for many crops including sorghum.
no code implementations • 27 May 2021 • Changye Yang, Sriram Baireddy, Enyu Cai, Valerian Meline, Denise Caldwell, Anjali S. Iyer-Pascuzzi, Edward J. Delp
In particular, we want to design a metric for wilting based on images acquired of the plant.
no code implementations • 29 Apr 2020 • Enyu Cai, Sriram Baireddy, Changye Yang, Melba Crawford, Edward J. Delp
In this paper, we propose a method for estimating plant centers by transferring an existing model to a new scenario using limited ground truth data.
no code implementations • 24 Jan 2020 • Changye Yang, Sriram Baireddy, Yuhao Chen, Enyu Cai, Denise Caldwell, Valérian Méline, Anjali S. Iyer-Pascuzzi, Edward J. Delp
Analysis of the shape of plants can potentially be used to accurately quantify the degree of wilting.