Published Technical Disclosure Archive

AI-Powered Plant Disease Detection Tech

Publication ID: 24-11856881_0006_PTD
Published: October 26, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “AI-Powered Plant Disease Detection Tech,” Published Technical Disclosure No. 24-11856881_0006_PTD, Published October 26, 2025, available at https://archive.pr1or.art/24-11856881_0006_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 models for plant disease detection. However, limitations in image processing, model training, and real-time processing capabilities hindered the widespread adoption of this technology. The present invention addresses these limitations by introducing advanced image processing techniques, optimized deep learning models, and real-time processing capabilities, enabling more accurate and efficient plant disease detection.

Novelty and Inventive Step

The present invention's novelty lies in the combination of advanced image processing techniques, optimized deep learning models, and real-time processing capabilities, which enable more accurate and efficient plant disease detection. The inventive step resides in the integration of these components to overcome the limitations of the original patent, providing a significant improvement in disease detection capabilities.

Alternative Embodiments and Variations

Alternative embodiments of the invention may include the use of different image processing techniques, such as edge detection or feature extraction, or the integration of additional data sources, such as weather or soil data, to enhance disease detection. Variations of the invention may also include the application of different machine learning models or the use of alternative training datasets to improve model accuracy.

Potential Commercial Applications and Market

The present invention has significant commercial potential in the agricultural industry, enabling farmers and crop managers to detect plant diseases more accurately and efficiently, reducing crop losses and improving yields. The invention may also be applied in other industries, such as forestry or environmental monitoring, where accurate disease detection is critical.

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 and machine learning, specifically computer vision systems for plant disease detection, involving deep learning models, image processing, and classification techniques

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with expertise in machine learning, computer vision, agricultural image analysis, with knowledge of deep neural networks, image preprocessing techniques, and domain-specific classification methodologies

Obviousness Rationale

A person of ordinary skill would recognize that the PTD's variations represent predictable extensions of the source patent's core deep learning plant disease detection framework. The disclosed techniques of multi-scale image processing, synthetic data generation, and hierarchical classification models are logical incremental improvements that would be apparent to a skilled practitioner seeking to enhance the original patent's methodology. These variations represent standard optimization strategies within machine learning and computer vision domains.

Obvious Combinations & Variations

Source Patent Element
Multi-region classification model for detecting plant diseases using deep learning
PTD Variation
Hierarchical classification model with coarse-grained and fine-grained detection stages
Obviousness Reasoning
Implementing staged classification is a known technique for improving model precision, representing a predictable refinement of existing deep learning approaches
Source Patent Element
Image resizing and preprocessing techniques for disease detection
PTD Variation
Multi-scale image processing module for extracting features from images of varying sizes
Obviousness Reasoning
Adapting image processing techniques to handle variable input sizes is a standard design choice in computer vision that would be obvious to a skilled practitioner
Source Patent Element
Deep learning model for plant disease classification
PTD Variation
Generating synthetic training images using generative adversarial networks to augment dataset
Obviousness Reasoning
Data augmentation through synthetic image generation is a well-established machine learning technique for improving model generalization and performance
Source Patent Element
Server-based image classification system
PTD Variation
Cloud-based server with mobile device image capture and remote processing
Obviousness Reasoning
Distributing computational load between mobile devices and cloud servers is a standard architectural approach in modern machine learning systems
Source Patent Element
Single deep learning classification model
PTD Variation
Ensemble averaging of multiple trained models to improve prediction robustness
Obviousness Reasoning
Model ensemble techniques are a known method for improving classification accuracy and are considered an obvious optimization strategy
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 plant disease detection techniques obvious, as the PTD demonstrates that the proposed modifications represent predictable extensions of existing deep learning methodologies using standard machine learning optimization strategies.

Original Patent Information

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