Saeed Khaki

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

YieldNet predicts corn and soybean yields using remote sensing data with a deep learning model that shares weights between crops and uses a new loss function, achieving accurate predictions months before harvest.

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