US 2022/0019795 A1 · PREDICTING HORTICULTURAL YIELD FOR A FIELD LOCATION USING MULTI-BAND AERIAL IMAGERY

Abstract

Embodiments of the disclosed technologies are capable of inputting, to a machine-learned classifier that has been created using a set of neural network-based models, multi-band digital image data that represents an aerial view of an agricultural field location containing an horticultural product; outputting, by the classifier, annotated image data, the annotated image data comprising annotation data indicative of individual instances of the horticultural product in the agricultural field location; using the annotated image data, computing a first predicted yield for the agricultural field location; adjusting the first predicted yield by a scaling factor to produce a second predicted yield for the agricultural field location.

Claims20

Dependent claims

A computer-implemented method comprising:

inputting, to a machine-learned classifier that has been created using a set of neural network-based models, multi-band digital image data that represents an aerial view of an agricultural field location containing a horticultural product, each neural network-based model of the set of neural network-based models comprising a plurality of layers, each layer of the plurality of layers comprising a plurality of nodes and a plurality of weights associated with the plurality of nodes;
outputting, by the classifier, annotated image data, the annotated image data comprising annotation data identifying individual instances of the horticultural product in the agricultural field location;
using the annotated image data, computing a first predicted yield for the agricultural field location.

A system comprising:

one or more processors;
a memory storing instructions which, when executed by the one or more processors, cause performance of:
inputting, to a machine-learned classifier that has been created using a set of neural network-based models, multi-band digital image data that represents an aerial view of an agricultural field location containing a horticultural product, each neural network-based model of the set of neural network-based models comprising a plurality of layers, each layer of the plurality of layers comprising a plurality of nodes and a plurality of weights associated with the plurality of nodes;
outputting, by the classifier, annotated image data, the annotated image data comprising annotation data identifying individual instances of the horticultural product in the agricultural field location;
using the annotated image data, computing a first predicted yield for the agricultural field location.

2 more elements

One or more non-transitory computer-readable media storing instructions which, when executed, cause performance of:

inputting, to a machine-learned classifier that has been created using a set of neural network-based models, multi-band digital image data that represents an aerial view of an agricultural field location containing a horticultural product, each neural network-based model of the set of neural network-based models comprising a plurality of layers, each layer of the plurality of layers comprising a plurality of nodes and a plurality of weights associated with the plurality of nodes;
outputting, by the classifier, annotated image data, the annotated image data comprising annotation data identifying individual instances of the horticultural product in the agricultural field location;
using the annotated image data, computing a first predicted yield for the agricultural field location.

Dependencies are inferred from the claim text.

Predicting horticultural yield for a field location using multi-band aerial imagery

Life of this patent

Predicting horticultural yield for a field location using multi-band aerial imagery

Life of this patent

Abstract

Claims20