Agricultural Data Analysis System for Correlation Detection
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Solution Overview
Problem
Agricultural data analysis systems face challenges in timely and accurate operation, particularly for farmers who struggle to analyze and interpret data from display monitors on planters and combine harvesters, leading to difficulties in making informed decisions about planting, harvesting, and resource management.
Innovation Solution
A computer system and method for agricultural data analysis that monitors and analyzes yield and field data, including weather, harvest, planting, fertilizer, and pesticide data, to identify correlations and potential issues, and sends alerts and recommendations to users for improved decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the operator manually analyzes data and maps from the display monitor, then the operator can understand field operation status, but the operator has difficulty making timely decisions due to the complexity and time required for data analysis
Solution Approach 1:
The system performs self-service by automatically analyzing agricultural data, identifying correlations, and generating insights without requiring manual operator intervention. The computer system autonomously processes yield data, field data, weather data, and other agricultural data to identify correlations and potential issues, freeing the operator from time-consuming manual analysis while maintaining high accuracy in data interpretation
Solution Approach 2:
The patent replaces the mechanical human cognitive process of data analysis with an automated computer-based system. Instead of the operator manually examining maps and data on the display monitor, a computer system with processing logic automatically analyzes the data, identifies correlations between variables, and generates insights, substituting human mental effort with computational processing that is both faster and more accurate
2Reliability
If the system monitors and analyzes multiple types of agricultural data to identify correlations, then the quality of farming decisions improves, but the complexity of the data analysis system increases
Solution Approach 1:
The computer system is designed with multi-functionality to handle diverse agricultural data types including yield data, field data, weather data, harvest data, planting data, fertilizer data, and pesticide data. The same processing logic and correlation analysis mechanisms are universally applied across all these different data types, allowing the system to identify correlations between any variables without requiring separate specialized systems for each data type
Solution Approach 2:
The patent introduces an intermediary computer system that acts as a mediator between raw agricultural data and the operator's decision-making process. This intermediary automatically processes multiple data types, identifies correlations, and presents synthesized insights to the operator, simplifying the complexity by handling the analytical burden internally while presenting simplified, actionable information to the user
3Productivity
If the system automatically identifies correlations and sends alerts to users, then operational efficiency improves, but the extent of automation increases system complexity
Solution Approach 1:
The system implements feedback by automatically monitoring agricultural data, identifying correlations and potential issues, and sending alerts to users when problems are detected. This closed-loop feedback mechanism enables the system to continuously improve operational efficiency by providing timely information about field conditions, yield variations, and potential issues, allowing operators to take corrective actions before problems escalate
Solution Approach 2:
The patent applies preliminary action by proactively analyzing data and sending alerts before issues become critical. The system continuously monitors agricultural data and identifies potential problems early in the process, allowing operators to take preventive actions ahead of time. For example, the system can identify correlations that suggest potential equipment issues or field conditions that may affect yield, enabling preemptive interventions that improve operational efficiency
Data Source
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AI summary
Described herein are systems and methods for agricultural data analysis. In one embodiment, a computer system for monitoring field operations includes a database for storing agricultural data including yield and field data and at least one processing unit that is coupled to the database. The at least one processing unit is configured to execute instructions to monitor field operations, to store agricultural data, to automatically determine whether at least one correlation between different variables or parameters of the agricultural data exceeds a threshold, and to perform analysis of the agricultural data to identify a category of man-made issues or other issues that have potentially caused the correlation when at least one correlation occurs between different variables or parameters of the agricultural data.