Published Technical Disclosure Archive

Advanced AI Plant Disease Detection Technology

Publication ID: 24-11856881_0008_PTD
Published: October 26, 2025
Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Advanced AI Plant Disease Detection Technology,” Published Technical Disclosure No. 24-11856881_0008_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856881_0008_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, 'Detection of plant diseases with multi-stage, multi-scale deep learning', presented a significant advancement in plant disease detection. However, it relied solely on image processing and machine learning. The new invention addresses the limitations of the original patent by incorporating additional technologies to enhance the accuracy, integrity, and transparency of disease detection and classification.

Novelty and Inventive Step

The new claims introduce novel synergistic combinations of technologies, including IoT, blockchain, AI, and novel materials, which significantly enhance the accuracy, integrity, and transparency of plant disease detection and management. These combinations are non-obvious and provide a significant improvement over the original patent.

Alternative Embodiments and Variations

Other ways to implement the invention include integrating the deep learning model with other sensing technologies, such as drones or satellite imaging, to gather more comprehensive environmental data. Additionally, the system could be adapted for use in different agricultural settings, such as greenhouses or vertical farming.

Potential Commercial Applications and Market

The invention has significant commercial potential in the agricultural technology industry, particularly in precision agriculture, crop protection, and farm management. The market for plant disease detection and management systems is growing rapidly, driven by the need for sustainable and efficient agricultural practices.

CPC Classifications

SectionClassGroup
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

Field of Art

Agricultural technology, machine learning, computer vision, and plant disease detection systems, with expertise in deep learning models, image classification, and multi-scale analysis techniques

Person of Ordinary Skill (PHOSITA) Profile

A skilled professional with advanced degrees in computer science, agricultural engineering, or machine learning, with practical experience in developing AI-powered image recognition systems for agricultural applications

Obviousness Rationale

The PTD's proposed variations represent predictable extensions of the source patent's multi-stage deep learning approach for plant disease detection. A skilled practitioner would recognize that integrating IoT, blockchain, and additional sensing technologies are natural evolutionary steps in enhancing the core machine learning classification methodology. The fundamental technical framework of region-based classification and multi-scale analysis remains consistent, with the PTD merely adding complementary technologies to improve data collection, verification, and decision support.

Obvious Combinations & Variations

Source Patent Element
Multi-stage classification model with first and second digital models analyzing image regions
PTD Variation
Integrating IoT sensor network to provide additional environmental context for disease classification
Obviousness Reasoning
Augmenting image-based classification with sensor data is a predictable enhancement that a PHOSITA would consider to improve diagnostic accuracy
Source Patent Element
Image processing techniques involving resizing and sliding window approaches
PTD Variation
Incorporating blockchain technology to ensure data integrity and traceability of classification results
Obviousness Reasoning
Applying blockchain to validate machine learning outputs represents a known technique for enhancing system credibility in data-sensitive domains
Source Patent Element
Deep learning model for classifying plant disease regions at multiple scales
PTD Variation
Adding AI-powered decision support to provide personalized disease management recommendations
Obviousness Reasoning
Extending classification models to include actionable recommendations is an obvious next step in creating practical diagnostic systems
Source Patent Element
Computer system for processing and classifying plant disease images
PTD Variation
Integrating novel material-based sensing technologies to detect disease biomarkers
Obviousness Reasoning
Expanding detection methods using advanced materials represents a standard approach to improving diagnostic precision
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856881, the present disclosure demonstrates that the claimed combinations of multi-stage deep learning, IoT integration, blockchain verification, and enhanced sensing technologies would be obvious to a person having ordinary skill in the art of agricultural machine learning systems. The incremental technological additions represent predictable variations that do not rise to the level of non-obvious innovation, thereby establishing this disclosure as anticipatory prior art against potential future patent claims in this technological domain.

Original Patent Information

Patent NumberUS 11,856,881
TitleDetection of plant diseases with multi-stage, multi-scale deep learning
Assignee(s)CLIMATE LLC