Published Technical Disclosure Archive

AI-Powered Vegetative Health Mapping & Monitoring System

Publication ID: 24-11856883_0001_PTD
Published: October 26, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “AI-Powered Vegetative Health Mapping & Monitoring System,” Published Technical Disclosure No. 24-11856883_0001_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856883_0001_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 for moisture and vegetative health mapping has limitations in terms of data analysis, predictive capabilities, and treatment implementation. The new invention addresses these limitations by introducing real-time monitoring, machine learning-based predictive modeling, and automated treatment plans to ensure optimal vegetative health.

Novelty and Inventive Step

The new invention's novelty lies in its ability to provide real-time monitoring, predictive modeling, and automated treatment plans, which are not present in the original patent. The inventive step is the integration of machine learning algorithms and automation modules to optimize vegetative health and reduce water waste.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include using different types of sensors, such as drones or satellite imaging, to collect data. The system could also be integrated with existing irrigation systems or farm management software to provide a more comprehensive solution.

Potential Commercial Applications and Market

The enhanced vegetative health mapping and improvement system has significant commercial potential in the agriculture, landscaping, and environmental monitoring industries. The system can help farmers and landscapers optimize crop yields, reduce water waste, and improve environmental sustainability, making it an attractive solution for businesses and governments seeking to improve their environmental footprint.

CPC Classifications

SectionClassGroup
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

Field of Art

Agricultural monitoring and precision agriculture technologies, focusing on vegetation health assessment, sensor-based data collection, and environmental monitoring systems

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in agricultural engineering, remote sensing technologies, data analytics, machine learning, and sensor integration, typically holding a BS/MS in agricultural engineering, computer science, or related field with 3-5 years of industry experience

Obviousness Rationale

A PHOSITA would recognize that the PTD's variations represent predictable extensions of the source patent's core technology by integrating standard machine learning techniques, sensor data processing, and automated treatment recommendations into the existing moisture mapping framework. The disclosed improvements follow naturally from the source patent's foundational approach of collecting spatial vegetative health data, representing an incremental technological advancement using known techniques in sensor integration and data analysis.

Obvious Combinations & Variations

Source Patent Element
Receiving coordinates defining a bounded region of interest for moisture mapping
PTD Variation
Expanding sensor data collection to include multiple location types like drones and satellite imaging
Obviousness Reasoning
A PHOSITA would recognize multiple sensor platforms as interchangeable for spatial data collection, representing a predictable design variation
Source Patent Element
Machine learning model for assessing vegetative health
PTD Variation
Generating predictive models using historical sensor data to forecast potential health issues
Obviousness Reasoning
Applying machine learning techniques to historical data for predictive analysis represents a standard technique in data science and agricultural monitoring
Source Patent Element
Determining degradation of vegetative health based on previous mapping data
PTD Variation
Automated treatment planning and execution based on detected health variations
Obviousness Reasoning
Translating diagnostic information into targeted treatment represents an obvious next step in precision agriculture technologies
Source Patent Element
Three-dimensional mapping of moisture and vegetation indicators
PTD Variation
Real-time monitoring system with wireless communication and alert generation
Obviousness Reasoning
Integrating wireless communication and alert systems represents a predictable technological enhancement for remote monitoring applications
Source Patent Element
Sensor-based moisture detection for vegetative health assessment
PTD Variation
Comprehensive system integrating sensor data, machine learning, and automated treatment modules
Obviousness Reasoning
Combining known technological components to create an integrated monitoring and treatment system represents an obvious design optimization
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856883, a person of ordinary skill in the art would find the claimed variations in the present publication to be obvious extensions of the prior art, representing predictable technological improvements using standard techniques in agricultural monitoring and data analysis. The disclosed system's integration of sensor data collection, machine learning, and automated treatment planning would be considered an obvious combination of known elements producing expected results in the field of precision agriculture.

Original Patent Information

Patent NumberUS 11,856,883
TitleMoisture and vegetative health mapping
Assignee(s)Scythe Robotics, Inc.