Agronomic System Using Segmented Field Zoning for Precision Input Application
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Solution Overview
Problem
Conventional farming practices, such as uniform plant variety and input application across entire fields, fail to maximize crop yield due to inconsistencies in soil and crop conditions, leading to reduced yields, wasted resources, and environmental impact.
Innovation Solution
An agricultural system that includes a network interface for receiving agricultural prescriptions, which are comprised of characteristics and actions, allowing for real-time sensing and evaluation of soil and crop conditions to identify limiting factors and optimize input application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform plant variety and constant input application are used across entire fields, then farming operations are simplified and easier to manage, but crop yield is reduced due to inability to account for soil and crop condition variations
Solution Approach 1:
The field is divided into multiple zones or areas based on soil characteristics, crop conditions, and environmental factors. Each zone is managed separately with tailored plant varieties and input application rates, allowing the system to capture spatial variations while maintaining manageable operational complexity through automated zone identification and prescription generation.
Solution Approach 2:
Different plant varieties and input application rates are selected for different locations within the field based on local soil and crop conditions. The system generates location-specific agricultural prescriptions that optimize crop yield for each zone while the overall system maintains simplicity through automated decision-making based on sensor data and predictive models.
2Productivity
If precision farming techniques are implemented to maximize crop yield, then crop yield increases, but the system complexity and time required for data collection and analysis increase
Solution Approach 1:
The system integrates multiple functions into a unified platform that simultaneously collects data from various sensors, analyzes soil and crop conditions, predicts yield, and generates agricultural prescriptions. This multi-functional approach increases crop yield through precision farming while avoiding the complexity of separate systems for each function.
Solution Approach 2:
The system uses intermediate processing layers including data aggregation servers, predictive algorithms, and automated prescription generators that translate raw sensor data into actionable agricultural recommendations. These intermediaries simplify the complexity by automating the decision-making process and providing user-friendly outputs to farmers.
3Ease of operation
If historic data is relied upon for agronomic forecasting, then forecasting process is simplified, but forecast accuracy decreases due to year-to-year variations in agronomic factors
Solution Approach 1:
The system transitions from static historic data analysis to dynamic real-time monitoring and prediction. Sensors continuously collect data on soil conditions, crop growth, and environmental factors, and the system uses predictive models that adapt to current conditions rather than relying solely on past patterns. This dynamic approach maintains simplicity through automated real-time processing while significantly improving forecast accuracy.
Solution Approach 2:
The system incorporates continuous feedback loops where real-time sensor data is collected, analyzed, and used to update predictive models and generate adjusted agricultural prescriptions. This feedback mechanism allows the system to learn from current conditions and improve forecast accuracy over time while maintaining ease of operation through automated iterative processing.
4Ease of operation
If inputs are applied at constant rate across entire field, then application operations are simplified, but resource waste increases and environmental impact worsens
Solution Approach 1:
The field is segmented into multiple zones with different input requirements based on soil fertility, moisture levels, and crop density. The system generates zone-specific input prescriptions that optimize resource use while automated application equipment applies inputs at variable rates to each zone, reducing waste without significantly complicating operations through centralized control.
Solution Approach 2:
The system dynamically adjusts input application rates as a function of local soil and crop conditions. Sensors monitor parameters such as soil moisture, nitrogen levels, and crop growth stage, and the system automatically modifies input rates accordingly. This parameter-based approach reduces input waste while maintaining operational simplicity through automated rate adjustment based on real-time data.
Data Source
AI summary
In one aspect, an agricultural system is provided and includes an information gathering component, a first component and a second component. The information gathering component is configured to gather information pertaining to at least one agricultural characteristic and generate agricultural data associated with the gathered information. The agricultural data is transmitted over a network and used to generate an agricultural prescription, which is comprised of at least one agricultural characteristic and at least one agricultural action. The first component includes a network interface for receiving the agricultural prescription over the network and the second component is in communication with the first component. The second component is configured to receive the agricultural prescription from the first component and is configured to output the at least one agricultural action.