Published Technical Disclosure Archive

AI-Driven Crop Monitoring Tech: Precision Agriculture

Publication ID: 24-11856910_0002_PTD
Published: October 26, 2025
Category:New Applications & Use Cases

Legal Citation

pr1or.art Inc., “AI-Driven Crop Monitoring Tech: Precision Agriculture,” Published Technical Disclosure No. 24-11856910_0002_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856910_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. 11,856,910.

Background and Problem Solved

The original patent, 'Plants and seeds of hybrid corn variety CH011010', focused on the development of a high-yielding corn hybrid. However, the agricultural industry still faces significant challenges in terms of crop monitoring, yield prediction, and pest management. The present invention addresses these limitations by applying the core technology of the original patent to entirely new applications and use cases, thereby providing a comprehensive solution for intelligent crop monitoring and management.

Novelty and Inventive Step

The new claims introduce novel applications and use cases for the hybrid corn variety CH011010, which were not previously considered. The inventive step lies in the integration of cutting-edge technologies such as drones, IoT sensors, machine learning, and computer vision to create a comprehensive and intelligent crop monitoring and management system.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include the use of other crop varieties, different types of sensors or drones, or variations in the machine learning algorithms employed. Additionally, the system could be adapted for use in other agricultural contexts, such as monitoring livestock or detecting diseases in plants.

Potential Commercial Applications and Market

The present invention has significant commercial potential in the agricultural technology industry, with potential applications in precision farming, crop insurance, and agricultural consulting. The target market includes farmers, agricultural companies, and government agencies seeking to improve crop yields, reduce environmental impact, and increase agricultural efficiency.

CPC Classifications

SectionClassGroup
A A01 A01H6/4684
A A01 A01H5/10

Field of Art

Agricultural biotechnology, specifically corn breeding and precision agriculture technologies, involving genetic engineering, crop monitoring, and data-driven agricultural management systems

Person of Ordinary Skill (PHOSITA) Profile

A professional with advanced degrees in agricultural science, biotechnology, or agricultural engineering, possessing expertise in crop genetics, sensor technologies, machine learning, and data analytics for agricultural applications

Obviousness Rationale

A PHOSITA would recognize that integrating modern digital technologies with specific hybrid corn varieties represents a predictable and logical extension of existing agricultural research. The combination of IoT sensors, drone imaging, and machine learning with a specific corn variety follows established patterns of precision agriculture technology development. The technical variations proposed in the PTD represent incremental improvements that would be obvious to a skilled practitioner seeking to optimize crop management strategies.

Obvious Combinations & Variations

Source Patent Element
Hybrid corn variety CH011010 with specific genetic characteristics
PTD Variation
Using multispectral drone imaging to monitor plant health and detect fungal infections
Obviousness Reasoning
Applying advanced sensing technologies to crop monitoring is a known technique in precision agriculture, with predictable results in early disease detection and crop management
Source Patent Element
Genetic characteristics of corn variety CH011010
PTD Variation
Machine learning algorithms for yield prediction based on sensor data
Obviousness Reasoning
Data-driven predictive modeling is a standard approach in agricultural science, representing a straightforward application of existing machine learning techniques to crop performance analysis
Source Patent Element
Hybrid corn variety with specific genetic traits
PTD Variation
IoT-enabled soil moisture sensors and automated irrigation systems
Obviousness Reasoning
Precision irrigation techniques are well-established in agricultural engineering, with finite identified solutions for optimizing water management based on crop-specific requirements
Source Patent Element
Corn breeding techniques and genetic variety development
PTD Variation
Computer vision and machine learning for early pest detection
Obviousness Reasoning
Image recognition technologies applied to agricultural pest management represent a logical extension of existing diagnostic and monitoring techniques
Source Patent Element
Hybrid corn variety CH011010 with specific genetic characteristics
PTD Variation
GPS and GIS mapping for detailed crop health and yield analysis
Obviousness Reasoning
Geospatial mapping of agricultural data is a standard technique in precision farming, representing a predictable combination of existing technologies
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856910 for hybrid corn variety CH011010, the present publication demonstrates that the integration of digital monitoring technologies with specific corn varieties would be obvious to a person having ordinary skill in agricultural biotechnology. The proposed technical variations represent predictable combinations of known techniques in precision agriculture, rendering potential patent claims covering similar technological approaches obvious and thus unpatentable under 35 U.S.C. Section 103.

Original Patent Information

Patent NumberUS 11,856,910
TitlePlants and seeds of hybrid corn variety CH011010
Assignee(s)MONSANTO TECHNOLOGY LLC