activity
20192021
most citedConvolutional Neural Networks for Image-based Corn Kernel Detection and Counting

78 citations · 80 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV2021

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…

cs.CV20212 cited

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…

cs.CV2020

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…

cs.CV2020

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…

cs.CV202078 cited

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…

cs.LG2020

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…