Machine Learning Model for Prioritized Agricultural Field Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional precision agriculture methods struggle to effectively prioritize and manage nutrient deficiencies in fields due to limited budgets and lack of consideration for treatment interactions, often relying on historical yield data rather than yield potential, leading to underperformance in crop management.
Innovation Solution
A computer-implemented method using machine learning models, specifically trained with historical machine and soils data, to classify agricultural field data and generate prioritized prescription instruction sets for interventions, taking into account interactions and costs, thereby optimizing field management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional precision agriculture methods analyze agricultural field data to identify nutrient deficiencies, then field management can be targeted, but the limited budget prevents full treatment of all identified fields
Solution Approach 1:
The patent segments the agricultural fields into prioritized categories based on nutrient deficiency severity and treatment interaction potential. By dividing fields into priority levels (e.g., high, medium, low priority), the system enables selective treatment allocation that maximizes impact within limited budgets, directly resolving the contradiction between comprehensive analysis and constrained resource allocation.
Solution Approach 2:
The system performs preliminary classification of fields using machine learning models before treatment allocation. By pre-ranking fields based on predicted yield improvement and treatment effectiveness, the system enables budget-constrained stakeholders to allocate resources to the most impactful fields first, addressing the budget limitation while maintaining analysis precision.
2Ease of operation
If treatment recommendations are provided without considering treatment interactions, then recommendation generation is simplified, but treatment effectiveness is reduced due to unaccounted synergistic or antagonistic effects
Solution Approach 1:
The system performs preliminary analysis of treatment interactions using machine learning models that evaluate synergistic and antagonistic effects before generating recommendations. By pre-assessing potential treatment conflicts and combinations, the system maintains simplified recommendation generation while significantly improving treatment effectiveness through interaction-aware prioritization.
Solution Approach 2:
The system incorporates feedback mechanisms that consider historical treatment outcomes and predicted interaction effects when generating recommendations. This feedback loop enables the system to refine treatment priorities based on actual treatment responses, improving reliability while maintaining operational simplicity through automated iterative optimization.
3Ease of manufacture
If conventional methods base treatment decisions on historical yield data, then analysis is straightforward, but the true yield potential of fields is not accurately captured
Solution Approach 1:
The patent transforms the analysis parameters from historical yield data to multiple predictive parameters including soil nutrient content, plant health indices, and environmental conditions. By changing the data parameters from past performance metrics to current state metrics, the system maintains analysis simplicity while dramatically improving yield potential accuracy through machine learning-based predictive modeling.
Solution Approach 2:
The system introduces machine learning models as intermediary components between raw field data and treatment recommendations. These intermediaries process complex relationships between multiple parameters (soil nutrients, plant health, environment) to predict yield potential more accurately than historical data alone, while the automated processing maintains ease of analysis.
4Reliability
If comprehensive field treatment is implemented, then all nutrient deficiencies are addressed, but the limited budget prevents full coverage of all fields
Solution Approach 1:
The system segments fields into priority tiers based on predicted treatment effectiveness and budget availability. By categorizing fields into high-priority (immediate treatment needed) and lower-priority categories, the system enables complete treatment of critical fields while deferring less critical treatments, maximizing treatment completeness within budget constraints.
Solution Approach 2:
The system applies partial treatment strategies by focusing resources on the most impactful fields rather than attempting uniform treatment across all fields. This partial action approach ensures complete treatment of high-priority fields where it matters most, while accepting that not all fields receive treatment within the budget constraint, thereby optimizing the reliability-budget tradeoff.
Data Source
AI summary
A computer-implemented method of prioritizing agricultural field management includes training a machine learning model using historical machine data, receiving machine data, classifying the machine data, generating an agricultural prescription instruction set; and performing the agricultural prescription instruction set. A computing system includes a processor and a memory storing instructions that, when executed by the processor, cause the computing system to train a machine learning model using historical machine data, receive machine data, classify the machine data, generate an agricultural prescription instruction set; and perform the agricultural prescription instruction set. A non-transitory computer readable medium includes program instructions that when executed, cause a computer to train a machine learning model using historical machine data, receive machine data, classify the machine data, generate an agricultural prescription instruction set; and perform the agricultural prescription instruction set.