Lizhi Wang

This paper introduces DeepStand, a deep learning method for high-throughput corn stand counting using images, outperforming existing methods.

DeepCorn uses semi-supervised deep learning to accurately count corn kernels in-field, improving yield estimation and data collection for agriculture.

The paper introduces a look-ahead Monte Carlo simulation method to improve parental selection in trait introgression, showing it achieves higher success probability than existing approaches in maize data.

This paper proposes two MILP models for corn planting and harvest scheduling that consider storage capacity and growing degree units to ensure consistent weekly harvest quantities.