Landing Ai

Greg Frederick Diamos

Dongyan Wang

Kai Yang

Yiwen Rong

Andrew Yan-Tak Ng

Bo Tan

Beom Sik Kim

Timothy Viatcheslavovich Rosenflanz

Tian Wu

Systems and methods are disclosed herein for detecting impurities of harvested plants in a receptacle of a harvester. In an embodiment, a harvester controller receives, from a camera facing the contents of the receptacle, an image of the contents. The harvester controller applies the image as input to a machine learning model. The harvester controller receives, as output from the machine learning model, an identification of an impurity of the harvested plants. The harvester controller transmits a control signal based on the impurity.

AI-optimized harvester configured to maximize yield and minimize impurities

Systems and methods are disclosed herein for optimizing harvester yield. In an embodiment, a controller receives a pre-harvest image from a front-facing camera of a harvester. The controller inputs the pre-harvest image into a model, and receives as output from the model a predicted harvest yield. The controller receives, from an interior camera of the harvester, a post-harvest image including the plants as harvested. The controller inputs the post-harvest image into a second model and receives, as output, an actual harvest yield of the plants as-harvested. The controller determines that the predicted harvest yield does not match the actual harvest yield, and outputs a control signal.

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Menlo Park, CA, United States of America

Greg Frederick Diamos