Predictive Agricultural Management System for Crop Optimization
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Solution Overview
Problem
Agricultural managers face challenges in maximizing crop yield and efficiency due to various variables, and lack a meaningful way to assimilate and utilize available data for predictive decision-making, often relying on human intuition rather than data-driven strategies.
Innovation Solution
A predictive agricultural management system that utilizes machine-learning models to aggregate and analyze data from multiple sources, including weather, disease, insect prevalence, and environmental factors, to provide actionable insights for optimal harvest dates, pesticide use, irrigation plans, and crop management strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agricultural managers rely on human intuition and manual analysis, then decision-making process is simple and accessible, but predictive accuracy and data utilization efficiency are insufficient
Solution Approach 1:
The patent introduces computer-based models and algorithms as intermediaries between raw agricultural data and decision-making processes. These models automatically process and analyze multiple data sources (weather, soil, crop data) to generate predictive insights, resolving the contradiction by providing high predictive accuracy through automated systems while shielding users from underlying complexity.
Solution Approach 2:
The patent replaces manual human analysis (mechanical cognitive process) with automated computer-based predictive models. This substitution enables processing of large datasets with higher precision and speed, while the automated system handles the computational complexity, allowing managers to benefit from advanced analytics without directly managing system complexity.
2Loss of information
If agricultural managers collect and analyze more data from multiple sources, then predictive insights improve, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent merges multiple data sources (weather data, soil data, crop data, historical data) into a unified predictive model framework. By integrating these diverse data streams through standardized processing pipelines, the system maximizes data utilization while managing complexity through unified architecture rather than separate processing systems.
Solution Approach 2:
The patent creates a universal predictive model system that can process multiple types of agricultural data through common algorithms and frameworks. This multi-functional approach allows the same system infrastructure to handle various data sources and prediction tasks, improving data utilization efficiency while avoiding the need for separate complex processing systems for each data type.
3Measurement precision
If agricultural managers use computer-based models to process all collected data, then predictive accuracy improves, but individual managers may not have access to data from neighboring proprietors
Solution Approach 1:
The patent segments the data ecosystem into local (individual proprietor) and regional (aggregated) levels. Individual managers can access and process their own data through local predictive models, while anonymized aggregated data from neighboring proprietors is made available through centralized platforms. This segmentation resolves the contradiction by enabling both high local prediction accuracy and broader data accessibility through hierarchical data architecture.
Data Source
AI summary
Systems and methods for predictive management of plants. Agricultural (and natural resource) managers may have a multitude of data sets and data sources available, but often lack a meaningful or proven way to assimilate all available data and then conclusively select actions. For example, a vineyard manager may be able to collect data about local and regional weather, precipitation, disease prevalence, insect prevalence, pesticide use, crop varietal, cover crop selection and many other inputs to a predictive machine-learning vineyard management engine. As all this data is collected through local devices and third-party services, a prediction model may be used to determine specific outcomes or recommended actions based on the trained predictive model. For example, the model may be used to predict optimal harvest date, disease spread and vector spread, pest spread and impact, best pesticide use, irrigation plans, fruit quality, and the like.