AI-Driven Plant Disease Detection System

Publication ID: 24-11856881_0003_PTD

Published: October 26, 2025

Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “AI-Driven Plant Disease Detection System,” Published Technical Disclosure No. 24-11856881_0003_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856881_0003_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 effectiveness of deep learning in plant disease classification. However, it had limitations in terms of real-time data integration, secure data sharing, and adaptive decision support. The new invention addresses these limitations by synergistically combining the patented deep learning approach with IoT-based sensor networks, blockchain-based secure data sharing, AI-driven decision support, and new material-based sensors, enabling a more powerful and comprehensive plant disease detection and management system.

Novelty and Inventive Step

The new invention's novelty lies in the synergistic combination of multiple distinct technologies, including IoT, blockchain, AI, and new materials, with the patented deep learning approach. This integration enables real-time data-driven decision making, secure data sharing, and adaptive disease management, which is not obvious from the original patent.

Alternative Embodiments and Variations

Alternative embodiments of the invention could include integrating the deep learning model with other types of sensors, such as drones or satellite imaging, or using different blockchain protocols for secure data sharing. Variations of the invention could also include applying the system to other types of crops or diseases, or integrating it with other agricultural systems, such as precision irrigation or autonomous farming.

Potential Commercial Applications and Market

The Synergistic Plant Disease Detection and Management System has significant commercial potential in the agricultural industry, particularly in precision agriculture, crop protection, and sustainable farming practices. The system's ability to minimize environmental impact and optimize treatment strategies aligns with the growing demand for sustainable and environmentally-friendly agricultural practices. The target market includes farmers, researchers, and agricultural companies seeking to improve crop yields and reduce disease-related losses.

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 and deep learning systems for plant disease detection, involving interdisciplinary skills in image processing, neural network design, sensor integration, and agricultural diagnostics

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with advanced degrees in computer science, machine learning, or agricultural engineering, possessing expertise in convolutional neural networks, image classification techniques, and knowledge of agricultural sensing technologies

Obviousness Rationale

A person having ordinary skill would recognize that extending the source patent's deep learning plant disease detection framework to incorporate additional technologies like IoT, blockchain, and advanced sensing represents predictable variations within the existing technological domain. The core machine learning methodology remains consistent, with the PTD introducing integrative approaches that a skilled practitioner would find straightforward to implement given existing technological capabilities. The variations represent logical combinations of known techniques that would be apparent to a skilled researcher in the field.

Obvious Combinations & Variations

Source Patent Element

Multi-stage deep learning classification model for plant disease detection

PTD Variation

Integrating IoT sensor networks with the deep learning model to receive real-time environmental data

Obviousness Reasoning

Augmenting machine learning models with sensor data is a known technique for improving predictive accuracy, representing a predictable extension of existing technological approaches

Source Patent Element

Image classification techniques for identifying plant disease regions

PTD Variation

Blockchain-based secure data sharing protocol for disease diagnosis information

Obviousness Reasoning

Implementing secure data transmission protocols is a standard design choice for distributed machine learning systems, representing an obvious technological improvement

Source Patent Element

Convolutional neural network for image-based disease classification

PTD Variation

Integrating biochemical sensors with CNN for enhanced tissue change detection

Obviousness Reasoning

Combining multiple sensing modalities with machine learning is a well-established approach for improving diagnostic precision, representing a finite set of predictable technological solutions

Source Patent Element

Multi-scale image processing for disease identification

PTD Variation

Climate model integration with machine learning for disease outbreak prediction

Obviousness Reasoning

Incorporating contextual environmental data into predictive models is a standard technique for improving machine learning system performance, representing an obvious technological progression

35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11856881 and the disclosed technological variations, a person having ordinary skill in the art would find the proposed system extensions obvious and lacking inventive step. The published technical disclosure demonstrates that the claimed innovations represent predictable combinations of known techniques in machine learning, agricultural sensing, and data management technologies, thereby rendering potential patent claims obvious 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