AI-Powered Plant Disease Detection & Management System

Publication ID: 24-11856881_0010_PTD

Published: October 26, 2025

Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “AI-Powered Plant Disease Detection & Management System,” Published Technical Disclosure No. 24-11856881_0010_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856881_0010_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', demonstrated the potential of deep learning in plant disease detection. However, it relied on a limited scope of data inputs and did not fully integrate with other agricultural systems. The present invention addresses these limitations by introducing a multi-modal sensing approach, integrating genomic and environmental data, and providing a holistic disease management platform.

Novelty and Inventive Step

The present invention's novelty lies in its multi-modal sensing approach, integration of genomic and environmental data, and provision of a holistic disease management platform. The inventive step is the combination of these elements to provide a comprehensive, real-time plant disease detection and management system that surpasses the capabilities of the original patent.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include the use of different sensing modalities, such as acoustic or olfactory sensors, or the integration of additional data sources, such as weather forecasts or soil moisture levels. Variations of the invention could also include the development of specialized disease management platforms for specific crops or regions.

Potential Commercial Applications and Market

The next-generation plant disease detection and management system has significant commercial potential in the agricultural industry, with potential applications in precision farming, crop insurance, and agricultural research. The target market includes farmers, agricultural cooperatives, and research institutions, with potential for expansion into adjacent industries such as forestry and environmental monitoring.

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 focused on plant disease detection and classification systems using computer vision and deep learning techniques. Requires expertise in image processing, machine learning algorithms, agricultural diagnostics, and sensor integration

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with advanced degrees in computer science, agricultural engineering, or machine learning, with practical experience in developing AI-driven diagnostic systems for agricultural applications. Familiar with deep learning architectures, multi-scale image analysis, and sensor data integration

Obviousness Rationale

A person of ordinary skill would recognize that the PTD's variations represent predictable extensions of the source patent's multi-stage deep learning approach for plant disease detection. The disclosed improvements leverage known machine learning techniques and sensor integration strategies that would be apparent to a skilled practitioner seeking to enhance plant disease monitoring systems. The PTD's claims represent logical combinations of existing technological capabilities within the agricultural AI domain.

Obvious Combinations & Variations

Source Patent Element

Multi-stage classification model for analyzing plant images across different regions

PTD Variation

Multi-modal sensing module integrating visual, thermal, and spectral data capture

Obviousness Reasoning

Extending single-modality image classification to multi-modal sensing represents a predictable improvement using known sensor fusion techniques. A PHOSITA would recognize the value of incorporating additional data types to enhance disease detection accuracy

Source Patent Element

Deep learning model for classifying plant disease regions

PTD Variation

Hybrid deep learning model trained to identify disease patterns across multiple data modalities

Obviousness Reasoning

Combining multiple data sources and training a unified model is a standard machine learning approach. A PHOSITA would find it obvious to expand single-modal classification to a more comprehensive multi-modal detection strategy

Source Patent Element

Computer system for processing and analyzing plant images

PTD Variation

Cloud-based agricultural intelligence platform with data ingestion and machine learning analytics

Obviousness Reasoning

Transitioning from localized image processing to a scalable cloud platform represents a standard architectural evolution. A PHOSITA would recognize this as a natural progression of computational approaches in agricultural technology

Source Patent Element

Image classification and region-based analysis techniques

PTD Variation

IoT-enabled sensor network with real-time anomaly detection and robotic actuation

Obviousness Reasoning

Extending image analysis to automated monitoring and intervention is a predictable technological advancement. A PHOSITA would view this as an obvious application of existing machine learning and robotics technologies

Source Patent Element

Machine learning models for plant image classification

PTD Variation

Machine learning-based genomics analysis for designing disease-resistant plant varieties

Obviousness Reasoning

Applying machine learning techniques to genomic analysis represents a standard approach in biotechnology. A PHOSITA would find it obvious to extend classification algorithms to genetic marker identification and trait development

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856881 and the disclosed variations, a person of ordinary skill in the art would find the claimed innovations obvious and lacking inventive step. The published technical disclosure demonstrates that the claimed agricultural AI and disease detection systems represent predictable combinations of known techniques in machine learning, sensor integration, and agricultural diagnostics, thereby rendering subsequent similar claims unpatentable under 35 U.S.C. § 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