Agronomic Analytics for Crop Yield Optimization
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Solution Overview
Problem
Agricultural data collected from various farming practices and geographic areas is often not effectively analyzed to determine the impact of different factors on crop yield, leading to suboptimal farming decisions.
Innovation Solution
An agronomic analytics system that aggregates planting and harvest data from multiple farm fields to analyze the yield impact of factors such as seed variety, soil type, precipitation, and farming practices, providing a yield analysis and recommendations for improved crop yields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If agricultural data is collected from various farming practices and geographic areas, then the quantity of data increases, but the ability to effectively analyze the data to determine yield impact decreases
Solution Approach 1:
The patent introduces an agronomic analytics system as an intermediary between raw agricultural data and farmers. This system processes, analyzes, and interprets large volumes of collected data from multiple fields and geographic areas, converting raw data into actionable yield insights. The analytics system acts as the mediator that bridges the gap between data collection and effective utilization.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting data from planting through harvest, analyzing the results, and providing recommendations that feed back into future farming decisions. This closed-loop feedback system allows the quantity of collected data to progressively improve the quality of insights generated, as each farming cycle provides additional data that refines the analytics models.
2Measurement precision
If data is collected with fine granularity using monitors and sensors, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent employs multi-functional monitoring systems that combine multiple sensors and data collection capabilities into integrated platforms. These universal devices can measure various parameters (soil moisture, temperature, plant health, yield) simultaneously, reducing the need for multiple separate complex devices while maintaining high measurement precision across all parameters.
Solution Approach 2:
The system incorporates automated data collection and processing capabilities where the monitoring equipment self-calibrates, self-diagnoses, and automatically transmits data without requiring complex manual intervention. This self-service functionality reduces operational complexity while maintaining precise measurements throughout the farming cycle.
3Reliability
If yield analysis is performed using aggregated crop data from multiple fields, then reliability of yield insights improves, but the time required for data processing increases
Solution Approach 1:
The system performs preliminary data processing and cleaning during data collection phases, organizing and validating data as it is gathered from multiple fields. By preparing the data in advance rather than processing raw data from scratch during analysis, the system reduces the time required for comprehensive yield analysis while maintaining reliability through aggregation of data from multiple sources.
Solution Approach 2:
The patent implements segmented data processing where analysis is performed in manageable portions - by field, by crop type, by time period - rather than attempting to process all aggregated data simultaneously. This segmentation allows parallel processing and reduces overall processing time while maintaining the reliability benefits of aggregated multi-field data through systematic analysis of segments.
Data Source
AI summary
Yield analysis techniques are provided herein. The effect of various factors on yield are identified through agronomic analytics applied to many different types of data that can be collected by different farmers using different farming practices in different geographic areas. A yield analysis request for a crop type can be received. Based on aggregated crop data, a yield impact of one or more yield factors can be determined for the crop type. The aggregated crop data can be based on planting data and harvest data for a plurality of farm fields. The aggregated crop data can be determined in part by associating the planting data and harvest data for individual farm fields of the plurality of farm fields. A yield analysis can be provided for the crop type based on the yield impact of the yield factors. Farmers can adjust farming practices to achieve higher yield using the yield analysis.