Advanced Plant Disease Detection AI Technology

Publication ID: 24-11856881_0009_PTD

Published: October 26, 2025

Category:Specialized Variations & Niche Solutions

Legal Citation

pr1or.art Inc., “Advanced Plant Disease Detection AI Technology,” Published Technical Disclosure No. 24-11856881_0009_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856881_0009_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. 11,856,881.

Background and Problem Solved

The original patent disclosed a computer system for detecting plant diseases using a multi-stage, multi-scale deep learning model. However, this invention did not address the unique challenges of detecting plant diseases in specialized environments. The present invention solves this problem by providing customized solutions for these niche markets.

Novelty and Inventive Step

The new claims introduce novel and non-obvious adaptations of the original invention to address the unique requirements of specialized environments. The inventive step lies in the customized design and implementation of the plant disease detection systems for these niche markets.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include systems for detecting plant diseases in other specialized environments, such as areas with limited water resources, areas prone to wildfires, or areas with unique soil compositions. Variations of the invention could also include different types of computer systems, such as cloud-based or edge computing systems.

Potential Commercial Applications and Market

The present invention has significant commercial potential in various industries, including agriculture, disaster relief, and environmental monitoring. The target markets include high-security agricultural facilities, disaster relief organizations, and companies operating in extreme weather conditions or remote areas.

CPC Classifications

Section	Class	Group
A	A01	A01B79/005
G	G06	G06F18/214
G	G06	G06F18/24317
G	G06	G06F18/254
G	G06	G06N3/045
G	G06	G06N3/08
G	G06	G06T3/40
G	G06	G06T7/0012
G	G06	G06V10/764
G	G06	G06V10/774
G	G06	G06V10/82
G	G06	G06V20/188
G	G06	G06V20/60
G	G06	G06V20/68
A	A01	A01G7/00
G	G06	G06T2207/20016
G	G06	G06T2207/20081
G	G06	G06T2207/20084

Section 103 Obviousness Analysis (PHOSITA)

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Field of Art

Agricultural technology and machine learning, specifically computer vision systems for plant disease detection, involving deep learning models, image processing, and classification techniques across agricultural environments

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in computer vision, machine learning, agricultural imaging, and deep neural network design, possessing knowledge of multi-stage classification models and experience adapting AI systems to varied environmental conditions

Obviousness Rationale

A person of ordinary skill would recognize that the source patent's multi-stage deep learning approach for plant disease detection provides a flexible framework that can be readily adapted to specialized agricultural contexts by modifying hardware configurations, network architectures, and training datasets while maintaining core classification methodologies.

Obvious Combinations & Variations

Source Patent Element

Multi-stage classification model for detecting plant diseases across image regions

PTD Variation

Applying the classification model in high-security agricultural facilities with restricted-access network protocols

Obviousness Reasoning

Adapting network access protocols represents a predictable design variation that does not fundamentally alter the core deep learning classification approach

Source Patent Element

Image resizing and region extraction techniques for disease classification

PTD Variation

Implementing the classification model in ruggedized computer systems operating in extreme temperature ranges

Obviousness Reasoning

Modifying hardware environmental tolerances is a routine engineering adaptation that would not require inventive effort beyond standard ruggedization techniques

Source Patent Element

Deep learning model trained on plant disease image datasets

PTD Variation

Retraining the model for specific environmental conditions like high-altitude or disaster-affected regions

Obviousness Reasoning

Retraining neural networks for domain-specific contexts is a standard machine learning practice involving transfer learning and dataset modification

Source Patent Element

Computer system for receiving and processing plant disease images

PTD Variation

Implementing solar-powered systems with satellite image transmission for remote agricultural areas

Obviousness Reasoning

Integrating alternative power sources and communication networks represents a predictable solution for extending technological capabilities to resource-constrained environments

Source Patent Element

Multi-scale image processing techniques for disease detection

PTD Variation

Adapting image processing algorithms for specific environmental constraints like limited connectivity or unique terrain

Obviousness Reasoning

Modifying image processing parameters to accommodate environmental variations is a routine engineering optimization within established machine learning frameworks

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856881, a person having ordinary skill in the art would find the disclosed variations in plant disease detection systems to be obvious extensions of existing multi-stage deep learning classification techniques, representing predictable adaptations that do not rise to the level of non-obvious innovation under 35 U.S.C. Section 103.

Original Patent Information

Patent Number	US 11,856,881
Title	Detection of plant diseases with multi-stage, multi-scale deep learning
Assignee(s)	CLIMATE LLC