Agricultural Intelligence System for Micro-Local Field Recommendations
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Solution Overview
Problem
Agricultural growers face challenges in making strategic decisions due to difficulties in obtaining reliable and precise data for decision-making, particularly regarding weather, soil conditions, pest risks, and market values, which are often generalized at a large regional level and require time-consuming analysis.
Innovation Solution
A computer-implemented method and networked agricultural intelligence system that processes field definition data, retrieves input data from various networks, determines field conditions, and provides recommendations on agricultural activities by analyzing weather, soil, and crop data at a granular level, using a cloud-based software as a service model and integrating data from multiple sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If weather data is generalized for a large region, then data coverage is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments the large regional area into multiple smaller geographic zones or grids, allowing weather data to be collected and analyzed at a more granular level. This segmentation enables the system to provide localized weather information for each zone while maintaining comprehensive coverage across the entire region, thus resolving the contradiction between broad coverage and precise measurement.
2Loss of information
If data is aggregated manually, then information completeness is improved, but productivity deteriorates
Solution Approach 1:
The system replaces manual data aggregation with an automated computer-implemented method that systematically collects, integrates, and analyzes data from multiple sources including weather stations, soil sensors, and agricultural databases. This automated information processing system maintains complete information while dramatically improving productivity by eliminating time-consuming manual aggregation tasks.
3Measurement precision
If field-level data analysis is performed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system employs a multi-functional integrated platform that combines data collection, processing, analysis, and recommendation generation in a single unified system. This universal system handles multiple agricultural data types (weather, soil, crop conditions) and performs various functions (monitoring, analysis, recommendation) simultaneously, reducing the need for separate specialized devices and thereby managing complexity while maintaining high measurement precision at the field level.
Data Source
AI summary
A computer-implemented method for recommending agricultural activities is implemented by an agricultural intelligence computer system in communication with a memory. The method includes receiving a plurality of field definition data, retrieving a plurality of input data from a plurality of data networks, determining a field region based on the field definition data, identifying a subset of the plurality of input data associated with the field region, determining a plurality of field condition data based on the subset of the plurality of input data, identifying a plurality of field activity options, determining a recommendation score for each of the plurality of field activity options based at least in part on the plurality of field condition data, and providing a recommended field activity option from the plurality of field activity options based on the plurality of recommendation scores.