B. Ganapathysubramanian

This paper applies self-supervised learning to maize kernel classification and segmentation, showing improved performance and annotation efficiency compared to supervised methods.

The paper introduces SNAP, a deep learning pipeline for automated soybean nodule quantification with 99% accuracy, enabling efficient analysis of nodule traits and genetic-environmental interactions.

This paper evaluates crowdsourcing for generating high-quality ground truth data for machine learning in plant phenomics, finding that Amazon MTurk workers match expert performance.