Machine Learning Plant Pathogen Classification Using Genetic Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting and classifying plant pathogen infestations, particularly fungal diseases, are subjective and prone to errors, often leading to late detection and inconsistent assessment of infestation levels, which complicates the measurement of fungicidal effects and increases standard deviation when multiple assessors are involved.
Innovation Solution
A computer-implemented method providing training data for machine learning algorithms using image data of infested plants combined with genetic analysis results, such as DNA or RNA data, to objectively classify pathogen infestations with high reliability and low standard deviation, enabling early detection and accurate classification of pathogen types and infestation stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual assessment by phytopathologists is used to detect and classify pathogen infestations, then expert knowledge can be utilized, but subjectivity and human error increase measurement precision and reliability
Solution Approach 1:
The patent replaces the mechanical/visual assessment system performed by phytopathologists with an automated image analysis system using machine learning algorithms. The system processes digital images of plant leaves to detect and classify pathogen infestations, substituting human visual inspection with computational analysis that eliminates subjectivity while maintaining or improving detection accuracy through consistent application of trained models.
2Productivity
If multiple phytopathologists conduct infestation assessments, then more data can be collected, but standard deviation increases due to individual judgement differences
Solution Approach 1:
The patent creates a standardized assessment model that can be replicated across multiple users and applications. The machine learning algorithm serves as a copyable, standardized system that produces identical results when given the same input images, eliminating the variability introduced by different human assessors while enabling high-volume data collection through automated processing.
3Device complexity
If visual assessment methods are used, then simple equipment is required, but detection capability is limited to later infestation stages
Solution Approach 1:
The patent replaces simple visual inspection with an automated image analysis system that can detect subtle patterns and features invisible to the human eye. The machine learning model processes digital images to identify early signs of pathogen infestation that would be imperceptible in visual assessment, significantly improving early detection capability while maintaining relative simplicity through standard imaging equipment.
Data Source
AI summary
Computer-implemented method for providing training data for a machine learning algorithm for image classifying a pathogen infestation of a plant, comprising the steps: providing image data of a plant or a plant part infested with a pathogen; providing genetic result data of the plant or the plant part to which the image data referred comprising at least information about the type of pathogen; labeling the image data with the genetic result data.