Agricultural Prescription Updates From Filtered Execution Deviations
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Solution Overview
Problem
Existing agricultural practices lack efficient methods to optimize the lifecycle processes based on real-time data analysis and automation, leading to suboptimal crop yields and resource utilization.
Innovation Solution
A distributed computing system that captures, analyzes, and generates agricultural prescriptions using wireless communication networks, sensors, and user devices to optimize farming processes, including data capture, analysis, and automated execution of agricultural tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agricultural practices are used, then simplicity and ease of operation are maintained, but productivity and resource utilization are suboptimal
Solution Approach 1:
The agricultural system is segmented into multiple independent components: sensor nodes distributed across fields, wireless communication modules, data processing units, and execution devices. Each component performs a specific function, allowing the system to scale without proportional increases in overall complexity. Sensors capture localized data independently, which is then aggregated and processed to generate prescription recommendations.
Solution Approach 2:
The system employs universal platforms that can perform multiple functions: mobile devices serve as both data collection interfaces and prescription delivery mechanisms, while computing systems handle both data processing and generation of actionable recommendations. This multi-functionality reduces the need for specialized equipment, maintaining ease of operation while improving productivity.
2Measurement precision
If real-time data analysis and automation are implemented, then resource management and decision-making precision are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The system implements continuous feedback loops where sensors monitor agricultural parameters in real-time, data is analyzed against target ranges, and prescriptions are generated and executed based on deviations. This automated feedback mechanism enables precise measurement and response without requiring complex manual intervention, as the system self-regulates based on sensor inputs and pre-defined agricultural models.
Solution Approach 2:
The system performs self-service through automated data collection, analysis, and prescription generation. Sensors automatically capture environmental and crop data, the computing system independently processes this data using agricultural models, and recommendations are automatically delivered to execution devices. This reduces the need for expert manual analysis while maintaining high measurement precision through consistent automated processing.
3Productivity
If automated execution of agricultural tasks is implemented, then productivity and resource utilization are optimized, but ease of operation decreases
Solution Approach 1:
The system introduces an intermediary layer between simple sensor inputs and complex execution actions. The computing system acts as a mediator that translates raw sensor data into actionable prescriptions, which are then delivered through familiar interfaces like mobile devices. This intermediary processing layer enables automated optimization while maintaining ease of operation, as farmers interact through standard devices rather than complex control systems.
Solution Approach 2:
The system optimizes agricultural outcomes by dynamically changing key parameters such as irrigation timing, fertilizer application rates, and harvest schedules based on real-time sensor data analysis. These parameter adjustments are automatically executed by connected devices while farmers can monitor and approve changes through simple interfaces, maintaining operational simplicity while achieving optimized resource utilization through precise parameter control.
Data Source
AI summary
A drive unit adapted for use in farm equipment includes a first transceiver and a processing module. The processing module is configured to receive first data from the farm equipment, including sensor data and/or controller data, and integrate the received first data with additional second data indicating an agricultural prescription for a geographic region to form agricultural data. The processing module is also configured to apply one or more spatial filter constraints and temporal filter constraints to the agricultural data and then compute, from the filtered agricultural data, a difference between actual execution of one or more parameters of the agricultural prescription and expected execution of the one or more parameters. The processing module is further configured to, when the difference exceeds a predefined stored threshold, autonomously generate and transmit to a remote computing device, a settings alert to thereby trigger generation of an updated agricultural prescription.