Advanced Vegetative Health Mapping Technology

Publication ID: 24-11856883_0002_PTD

Published: October 26, 2025

Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “Advanced Vegetative Health Mapping Technology,” Published Technical Disclosure No. 24-11856883_0002_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856883_0002_PTD

This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,856,883.

Background and Problem Solved

The original patent addressed the limitation of individual probes for detecting moisture levels in vegetation. However, this invention expands the core technology to tackle distinct challenges in various industries, where monitoring and improving vegetative health are crucial. For instance, in aquatic ecosystems, water quality parameters affect aquatic health, while in vertical farming, optimizing crop yields relies on accurate vegetative health mapping.

Novelty and Inventive Step

The novelty of this invention lies in the application of the core technology to entirely new industries, addressing distinct challenges and providing innovative solutions. The inventive step is the integration of sensor arrays, machine learning models, and user interfaces to generate three-dimensional maps of vegetative health in these novel applications.

Alternative Embodiments and Variations

Alternative embodiments may include using different types of sensors or machine learning models, or integrating additional data sources, such as weather data or soil type information. Variations may also include adapting the system for use in other industries, such as precision agriculture or environmental monitoring.

Potential Commercial Applications and Market

This invention has significant commercial potential in various industries, including environmental monitoring, agriculture, urban planning, and sports management. The target market includes companies and organizations involved in these industries, as well as government agencies and research institutions.

CPC Classifications

Section	Class	Group
A	A01	A01C21/007
A	A01	A01B69/004
A	A01	A01C7/06
A	A01	A01C7/102
A	A01	A01C23/007
A	A01	A01M7/0089
G	G05	G05D1/0246
G	G05	G05D1/0257
G	G06	G06N20/00
G	G06	G06T7/0012
G	G06	G06T7/70
G	G06	G06T17/05
G	G05	G05D2201/0201
G	G06	G06T2207/10004
G	G06	G06T2207/10028
G	G06	G06T2207/10044
G	G06	G06T2207/20081
G	G06	G06T2207/30188

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Agricultural and environmental monitoring technologies, focusing on sensor-based systems for measuring and analyzing vegetative health, with expertise in geospatial mapping, machine learning, and precision environmental monitoring techniques

Person of Ordinary Skill (PHOSITA) Profile

A technical professional with advanced degrees in agricultural engineering, environmental science, or computer science, possessing skills in sensor design, data analytics, machine learning model development, and geospatial mapping technologies

Obviousness Rationale

A PHOSITA would recognize that the source patent's core technology of generating three-dimensional vegetative health maps using sensor arrays and machine learning can be readily extended to multiple domain-specific applications by simply adapting the sensor configuration and training data. The fundamental technical approach of collecting environmental parameters, processing them through machine learning models, and generating spatial health visualizations remains consistent across different ecosystems and monitoring scenarios.

Obvious Combinations & Variations

Source Patent Element

Three-dimensional mapping of vegetative health using sensor data and machine learning models

PTD Variation

Applying the mapping technique to aquatic ecosystems, urban forests, and sports turf environments

Obviousness Reasoning

Adapting sensor-based health monitoring to different environments represents a predictable variation using known techniques, where the core machine learning and mapping methodology remains unchanged

Source Patent Element

Automated detection of vegetative health degradation

PTD Variation

Extending automated detection to invasive species spread prediction and mitigation strategies

Obviousness Reasoning

Applying machine learning models to predict ecological changes is a standard technique in environmental monitoring, representing an obvious extension of existing predictive technologies

Source Patent Element

Coupling sensor systems with ground vehicles or manual handling devices

PTD Variation

Integrating sensor arrays into vertical farming systems and specialized environmental monitoring platforms

Obviousness Reasoning

Modifying sensor deployment methods across different agricultural and environmental contexts represents a routine design choice for a PHOSITA familiar with sensor integration techniques

Source Patent Element

Machine learning models for interpreting sensor data and generating health indicators

PTD Variation

Developing specialized machine learning models for specific ecosystem health assessments

Obviousness Reasoning

Training machine learning models for different environmental contexts using domain-specific data is a predictable and standard approach in machine learning and sensor-based monitoring

Source Patent Element

User interface for visualizing three-dimensional health mapping

PTD Variation

Creating interactive visualization interfaces for different environmental monitoring scenarios

Obviousness Reasoning

Designing user interfaces to display geospatial health data represents a standard UI/UX design approach that would be obvious to a skilled practitioner

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856883, a person having ordinary skill in the art would find the variations disclosed herein to be obvious extensions of the prior art, as they represent predictable applications of known sensor-based health monitoring technologies across diverse environmental contexts, utilizing standard machine learning techniques and geospatial mapping methodologies.

Original Patent Information

Patent Number	US 11,856,883
Title	Moisture and vegetative health mapping
Assignee(s)	Scythe Robotics, Inc.