Systems and methods for validating planting of trees

Patent metadata

Field	Value
Publication number	US-12373849-B2
Application number	US-202217935399-A
Country	US
Kind code	B2
Filing date	Sep 26, 2022
Priority date	Mar 27, 2020
Publication date	Jul 29, 2025
Grant date	Jul 29, 2025

Abstract

Method and system for validating planting of trees. For example, the method includes receiving a first image depicting a tree, receiving a second image depicting an environment where the tree is to be planted, receiving a third image depicting the tree having been planted in the environment, selecting and encoding a first patch of the first image that depicts the tree, selecting and encoding a second patch of the second image that depicts the environment, selecting and encoding a third patch of the third image that depicts both the tree and the environment, and comparing the encoded patches to determine whether the tree has been planted in the environment.

First claim

What is claimed is:

1. A computer-implemented method comprising:
selecting, by a computing device, a first patch of a first image that depicts a part of a tree or a second patch of a second image that depicts a part of an environment, and a third patch of a third image that depicts the part of the tree and the part of the environment;
encoding, by the computing device, the first patch of the first image to generate first encoded tree data or the second patch of the second image to generate first encoded environment data, and the third patch of the third image to generate third encoded tree data or third encoded environment data, wherein the encoding the first patch or the second patch involves learning to generate the first encoded tree data or the first encoded environment data, respectively, based upon a model, wherein the model is an artificial neural network;
analyzing, by the computing device, the first patch or the second patch, wherein the analyzing the first patch or the second patch includes performing one or more of feature extraction or applying pattern recognition;
using, by the computing device, an autoencoder to generate the first encoded tree data or the first encoded environment data, wherein the autoencoder includes a reduction layer for inputs, one or more intermediate layers, and a reconstruction layer for outputs;
comparing, by the computing device, the first encoded tree data with the third encoded tree data or the first encoded environment data with the third encoded environment data to generate one or more comparison data; and
determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison data.

2. The computer-implemented method of claim 1 , wherein the determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison data includes:
validating, by the computing device, that the tree is in the environment in response to the one or more comparison data satisfying one or more predetermined conditions.

3. The computer-implemented method of claim 1 , further comprising:
receiving, by the computing device, the first image depicting the tree;
selecting, by the computing device, the first patch of the first image that depicts the part of the tree;
encoding, by the computing device, the first patch of the first image to generate the first encoded tree data;
encoding, by the computing device, the third patch of the third image to generate the third encoded tree data; and
comparing, by the computing device, the first encoded tree data with the third encoded tree data to generate the one or more comparison data.

4. The computer-implemented method of claim 1 , further comprising:
receiving, by the computing device, the second image depicting the environment;
selecting, by the computing device, the second patch of the second image that depicts the part of the environment;
encoding, by the computing device, the second patch of the second image to generate the first encoded environment data;
encoding, by the computing device, the third patch of the third image to generate the third encoded environment data; and
comparing, by the computing device, the first encoded environment data with the third encoded environment data to generate the one or more comparison data.

5. The computer-implemented method of claim 1 , further comprising:
receiving, by the computing device, the first image depicting the tree;
receiving, by the computing device, the second image depicting the environment;
selecting, by the computing device, the first patch of the first image that depicts the part of the tree;
selecting, by the computing device, the second patch of the second image that depicts the part of the environment;
encoding, by the computing device, the first patch of the first image to generate the first encoded tree data;
encoding, by the computing device, the third patch of the third image to generate the third encoded tree data;
comparing, by the computing device, the first encoded tree data with the third encoded tree data to generate one or more comparison tree data;
encoding, by the computing device, the second patch of the second image to generate the first encoded environment data;
encoding, by the computing device, the third patch of the third image to generate the third encoded environment data;
comparing, by the computing device, the first encoded environment data with the third encoded environment data to generate one or more comparison environment data; and
determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data.

6. The computer-implemented method of claim 5 , wherein the determining, by the computing device, whether the tree is in the environment based at least in part upon the one or more comparison tree data and the one or more comparison environment data includes:
validating, by the computing device, that the tree is in the environment in response to the one or more comparison tree data satisfying one or more predetermined tree conditions and the one or more comparison environment data satisfying one or more predetermined environment conditions.

7. The computer-implemented method of claim 1 , further comprising:
capturing, by the computing device, one or more images associated with whether the tree is in the environment.

8. A computing device comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
select a first patch of a first image that depicts a part of a tree or a second patch of a second image that depicts a part of an environment, and a third patch of a third image that depicts the part of the tree and the part of the environment;
encode the first patch of the first image to generate first encoded tree data or the second patch of the second image to generate first encoded environment data, and the third patch of the third image to generate third encoded tree data or third encoded environment data, wherein encoding the first patch or the second patch involves learning to generate the first encoded tree data or the first encoded environment data, respectively, based upon a model, wherein the model is an artificial neural network;
analyze the first patch or the second patch, wherein analyzing the first patch or the second patch includes performing one or more of feature extraction or applying pattern recognition;
use an autoencoder to generate the first encoded tree data or the first encoded environment data, wherein the autoencoder includes a reduction layer for inputs, one or more intermediate layers, and a reconstruction layer for outputs;
compare the first encoded tree data with the third encoded tree data or the first encoded environment data with the third encoded environment data to generate one or more comparison data; and
determine whether the tree is in the environment based at least in part upon the one or more comparison data.

9. The computing device of claim 8 , wherein the instructions that cause the one or more processors to determine whether the tree is in the environment based at least in part upon the one or more comparison data further cause the one or more processors to:
validate that the tree is in the environment in response to the one or more comparison data satisfying one or more predetermined conditions.

10. The computing device of claim 8 , wherein the instructions, when executed by the one or more processors, further cause the one or more processors to:
receive th

Assignees

• Quanata Llc

Inventors

• Sanchez Kenneth Jason

Classifications

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06Q50/40

  Business processes related to the transportation industry (shipping G06Q10/083) · CPC title

• G07C5/0841

  Registering performance data (recording measured values G01D; information storage G11B) · CPC title