Agronomic Prediction Engine for Localized Pest Risk Alerts
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Solution Overview
Problem
Conventional pest extent systems fail to provide timely and localized risk assessments for growers, often receiving reports too late for effective pest control and requiring growers to interpret risks based on their experience, as they do not account for on-farm management or reflect specific field conditions.
Innovation Solution
A computer-implemented system that processes geospatial, crop, user, and weather data using agronomic profile data to generate agronomic condition indicators and pest risk indicators, applying multivariable processing and predictive modeling to calculate future risk levels and provide actionable alerts for individual fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pest extent systems are used, then macro-scale pest monitoring is available, but timely and localized risk assessments are not provided
Solution Approach 1:
The system segments pest risk assessment from macro-scale general monitoring to micro-scale field-specific predictions by dividing the assessment into multiple data dimensions (weather, crop, soil, pest pressure) and processing them separately through multivariable models before integrating results for localized risk indicators
Solution Approach 2:
The patent introduces multivariable processing models as intermediaries between raw field data and pest risk indicators. These models act as mediators that transform diverse data types (weather patterns, crop characteristics, soil conditions) into meaningful localized risk assessments without requiring direct complex data analysis by users
2Loss of time
If paper reports are published by extension offices, then agronomic information is disseminated, but timely alerts for proactive pest control are not provided
Solution Approach 1:
The system performs preliminary pest risk assessment by continuously analyzing field data against historical pest patterns and environmental conditions before actual pest outbreaks occur. This advance prediction enables growers to take preventive measures rather than reacting to problems after they manifest
Solution Approach 2:
The system establishes a feedback loop where pest risk indicators are generated based on current field conditions, implemented by growers, and subsequently monitored to validate predictions. This continuous feedback mechanism improves prediction accuracy over time and enables dynamic adjustment of pest management strategies
3Reliability
If growers interpret risks based on experience, then subjective risk assessment is performed, but objective and consistent pest risk evaluation is not achieved
Solution Approach 1:
The system enables objective pest risk assessment by automating the analysis of field data through multivariable processing models. Instead of relying on grower expertise, the system self-evaluates risk by integrating weather, crop, soil, and pest pressure data through standardized algorithms, providing consistent and replicable risk indicators across different users and locations
Data Source
AI summary
At least some aspects of the present disclosure are directed to systems and methods of an agronomic condition prediction and alerts engine. In some cases, the engine receives a set of field data and the set of field data includes one or more of geospatial data, crop data, user data, agronomic data, and weather data. In some cases, the engine retrieves a set of agronomic profile data from an agronomic data repository, where each of the set of agronomic profile data representing an agronomic condition and comprising one or more of geospatial profile, crop profile, pest profile, agronomic profile, user data profile, and weather profile corresponding to the agronomic condition. In some embodiments, the engine applies a multivariable processing to the set of field data using the set of agronomic profile data and generates an agronomic condition indicator indicative of one or more agronomic conditions.