Deere + Company

Mineral Earth Sciences LLC

Zachary Beaver

Kangkang Wang

Ming Zheng

Alexander Ngai

Tiffany Cai

Chuan Du

Implementations are described herein for performing depth estimation in the agricultural domain, including generating synthetic training data. In various implementations, one or more three-dimensional synthetic plants may be generated in in a three-dimensional space, wherein the one or more three-dimensional synthetic plants include homogenous and densely-distributed synthetic plant parts. The plurality of three-dimensional synthetic plants may be projected onto two-dimensional planes from first and second perspectives in the three-dimensional space to form a pair of synthetic stereoscopic images. The first and second synthetic stereoscopic images of the pair may be annotated to create a mapping between the individual synthetic plant parts across the first synthetic stereoscopic images. A feature matching machine learning model may be trained based on the mapping.

Sparse and/or dense depth estimation from stereoscopic imaging

Implementations are described herein for estimating aggregate trait values for agricultural plots. In various implementations, a plurality of images depicting crops in an agricultural plot may be processed using a feature extraction portion of aggregate trait estimation model to generate a corresponding plurality of embeddings. The plurality of embeddings may be combined to obtain a unified representation of the crops in the agricultural plot. Using a prediction portion of the aggregate trait estimation model, the unified representation of the crops may be processed to estimate, as a direct output of the aggregate trait estimation model, an aggregate trait value of the crops in the agricultural plot. Data indicative of the estimated aggregate trait value of the crops in the agricultural plot may be output at a computing device.

Aggregate trait estimation for agricultural plots

Implementations are described herein for performing sparse depth estimation in the agricultural domain. In various implementations, one or more spatial aspects of a first visual annotation of a first candidate plant trait instance depicted in a first stereo image may be compared to one or more spatial aspects of a second visual annotation of a second candidate plant trait instance depicted in a second stereo image. Based on the comparison, it may be determined that the first and second candidate plant trait instances are a match for the same plant trait instance across the first and second stereo images. A disparity may be determined between the first and second candidate plant trait instances. Based on the disparity, a depth associated with the same plant trait instance may be estimated and used for various purposes.

Growing community of inventors

San Francisco, CA, United States of America

Zachary Beaver