Farming Machine Settings Database Using Neural Networks
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Solution Overview
Problem
Current farming management information systems and machine-to-machine (M2M) communications in precision agriculture face challenges in efficiently collecting and organizing diverse farming conditions and crop variability across different fields and machines, limiting their effectiveness.
Innovation Solution
The implementation of a farming machine settings database using artificial neural networks (ANNs) and convolutional neural networks (CNNs) within a remote computing system that processes agricultural information, determines situational operational settings, and shares them with farming machines through a communications network, enhancing precision and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional farming management information systems are used to collect and organize farming information, then basic tracking and monitoring can be achieved, but the systems cannot effectively handle the great variability in farming conditions and crop types across different fields and machines
Solution Approach 1:
The system creates customized farming information schemas that are tailored to specific local conditions including crop type, field characteristics, and machine specifications. Each farming operation can define its own data structure and organization rules that match its unique requirements, allowing the system to adapt to diverse farming conditions while maintaining reliable information management through locally-optimized schemas
Solution Approach 2:
The farming information schema is designed to be dynamic and configurable rather than fixed. The system allows farmers to modify data structures, add custom fields, and reorganize information categories based on changing farming conditions, crop varieties, and equipment. This dynamic adaptability enables the system to reliably handle variability across different fields and machines
2Measurement precision
If farming information systems collect detailed information about every field and machine, then precision and customization can be improved, but the complexity of managing and organizing this diverse information increases significantly
Solution Approach 1:
The system segments farming information into modular, hierarchical categories such as crop-specific schemas, field-specific parameters, machine-specific settings, and operator preferences. This segmentation allows detailed precision information to be collected and organized in manageable units that can be independently configured and maintained, reducing overall system complexity while preserving measurement precision
Solution Approach 2:
The system employs a universal schema framework that can accommodate multiple crop types, field conditions, and machine varieties through a common data structure. This universal approach allows the system to handle diverse farming information with consistent organization rules, reducing complexity by avoiding the need for entirely separate systems for different farming scenarios
Data Source
AI summary
Described herein is a novel farming machine settings database as well as technologies for generating and updating the database using computing schemes such as schemes including artificial neural networks (ANNs) or, more specifically, convolutional neural networks (CNNs).