196 citations · 427 across the 9 of their papers we have counts for
9 papers
An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions
Saba Moeinizade, Hieu Pham, Ye Han +2
For a global breeding organization, identifying the next generation of superior crops is vital for its success. Recognizing new genetic varieties requires years of in-field testing…
WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting
Saeed Khaki, Nima Safaei, Hieu Pham +1
For a globally recognized planting breeding organization, manually-recorded field observation data is crucial for plant breeding decision making. However, certain phenotypic traits…
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning
Saeed Khaki, Hieu Pham, Lizhi Wang
Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth…
Image-Based Sorghum Head Counting When You Only Look Once
Lawrence Mosley, Hieu Pham, Yogesh Bansal +1
Modern trends in digital agriculture have seen a shift towards artificial intelligence for crop quality assessment and yield estimation. In this work, we document how a parameter t…
DeepCorn: A Semi-Supervised Deep Learning Method for High-Throughput Image-Based Corn Kernel Counting and Yield Estimation
Saeed Khaki, Hieu Pham, Ye Han +3
The success of modern farming and plant breeding relies on accurate and efficient collection of data. For a commercial organization that manages large amounts of crops, collecting…
Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting
Saeed Khaki, Hieu Pham, Ye Han +3
Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well dev…
Meta Pseudo Labels
Hieu Pham, Zihang Dai, Qizhe Xie +2
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet, which is 1.6% better than the existing st…
Optimizing Data Usage via Differentiable Rewards
Xinyi Wang, Hieu Pham, Paul Michel +3
To acquire a new skill, humans learn better and faster if a tutor, based on their current knowledge level, informs them of how much attention they should pay to particular content…
Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
Mohsen Shahhosseini, Guiping Hu, Hieu Pham
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensemblin…