Microbiome Classification via Environmental Data and ML
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Solution Overview
Problem
Current agricultural practices face challenges in identifying optimal product applications for crops and soils due to the complexity of environmental factors, particularly in soil-active products, where microbial communities and historical chemical use alter soil ecosystems, making it difficult to assess and predict performance across varying environments.
Innovation Solution
A computer-implemented system that analyzes DNA sequencing data and environmental information to develop predictive models for classifying microbiomes at specific geographic sites, generating recommendations for customized agricultural product formulations, application rates, and practices to enhance plant health and crop productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DNA sequencing is performed to classify microbiomes at geographic sites, then measurement precision of microbial community composition is improved, but loss of substance increases due to extensive physical sampling requirements
Solution Approach 1:
The patent creates a virtual representation of the microbiome through predictive modeling and machine learning algorithms that simulate microbial community composition based on environmental data, replacing the need for extensive physical sampling and direct DNA sequencing while maintaining classification accuracy
Solution Approach 2:
The patent replaces the mechanical process of physical sampling and laboratory-based DNA sequencing with a computational system that uses machine learning models to predict microbiome characteristics from environmental parameters, eliminating the need for physical sample collection and processing
2Measurement precision
If extensive DNA sequencing and physical sampling are conducted to assess microbiomes, then measurement precision is improved, but productivity decreases due to time-consuming and resource-intensive processes
Solution Approach 1:
The patent performs preliminary classification of geographic sites into microbiome types using machine learning models trained on environmental data before agricultural operations begin, enabling farmers to make informed decisions about product applications and agronomic practices in advance, thereby improving operational efficiency
Solution Approach 2:
The patent creates a computational model that replicates the functionality of extensive DNA sequencing and physical sampling processes, providing microbiome classification results quickly without the time-consuming laboratory procedures, thus maintaining accuracy while improving productivity
3Adaptability or versatility
If customized agronomic programs are developed based on detailed microbiome analysis, then adaptability to specific geographic sites is improved, but device complexity increases due to sophisticated DNA sequencing and data analysis requirements
Solution Approach 1:
The patent applies local quality by tailoring agronomic recommendations to specific geographic sites based on their unique environmental characteristics and predicted microbiome types, using machine learning models that process local environmental data to generate customized advice for each location
Solution Approach 2:
The patent creates a universal platform that can classify any geographic site into microbiome types using a standardized machine learning approach, allowing the same system to adapt to diverse environments and generate site-specific recommendations without requiring complex, location-specific equipment
4Ease of operation
If conventional agricultural practices are used without microbiome classification, then ease of operation is maintained, but loss of information occurs regarding optimal product applications for specific soil and environmental conditions
Solution Approach 1:
The patent creates a simplified computational interface that copies the complex functionality of microbiome analysis into an easy-to-use platform, allowing farmers to input basic environmental data and receive customized agronomic recommendations without needing to understand or perform complex DNA sequencing procedures
Solution Approach 2:
The patent replaces complex laboratory-based microbiome analysis mechanisms with a computational system that processes environmental data through machine learning models, delivering optimal application parameters through a user-friendly interface that maintains ease of operation while preventing information loss
Data Source
AI summary
Systems and methods are provided for classifying a microbiome at a geographic site where agricultural activity is, or will be, conducted in order to improve and/or promote agricultural productivity at the site. Machine learning and/or artificial intelligence classifier tools use DNA sequencing input data and environmental information to generate recommendations for customized soil and/or crop treatment compositions, irrigation practices, and/or other agricultural activity, to enhance plant health and crop productivity.