Agricultural Measure Planning With Remote and Local Field Sensing
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Solution Overview
Problem
Precision agriculture faces challenges in determining the exact dosage of plant protection agents and nutrients due to spatial variations in soil and weather conditions within a field, leading to inefficient use and potential resistance formation, with existing remote sensing data lacking daily updates and sufficient spatial resolution.
Innovation Solution
A method and system that utilize digital images from remote sensors to plan and implement partial-area-specific agricultural measures, combining remote sensing data with real-time local sensor feedback to adapt applications to current field conditions, ensuring precise use of resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite images are used to obtain field information, then spatial coverage is improved, but spatial resolution and update frequency deteriorate
Solution Approach 1:
The field is divided into multiple zones based on satellite image analysis, with each zone receiving tailored agricultural treatment. This segmentation allows the system to work with lower-resolution satellite data while still achieving precise local化管理 by treating each segment independently with zone-specific parameters.
Solution Approach 2:
The system merges satellite image data with ground-based sensor data to create a comprehensive field model. The satellite data provides broad spatial coverage while ground sensors supply high-resolution local information, combining both data sources to overcome the limitations of either alone.
2Area of stationary object
If satellite images are used to obtain field information, then spatial coverage is improved, but update frequency deteriorates
Solution Approach 1:
Satellite images are used to pre-segment the field into zones and plan agricultural measures in advance. This preliminary action based on broad spatial data is then refined and executed with real-time adjustments using ground sensors, allowing the system to leverage advance planning while maintaining current operational accuracy.
Solution Approach 2:
The system implements continuous feedback loops where ground-based sensors monitor current field conditions and feed this data back to adjust the agricultural measures being implemented. This feedback mechanism allows the system to respond to real-time changes despite relying on less frequent satellite updates for overall field structure.
3Ease of operation
If uniform agricultural measures are applied across the entire field, then operational simplicity is improved, but resource efficiency deteriorates
Solution Approach 1:
The system applies the principle of local quality by determining different agricultural parameters for different zones within the field. Each zone receives treatment optimized for its specific characteristics (soil type, moisture content, crop stage), ensuring that resources are applied precisely where needed rather than uniformly across the entire field.
Solution Approach 2:
The system implements partial action by applying agricultural measures only to specific zones where they are actually needed, rather than treating the entire field uniformly. This allows the system to maintain operational simplicity through automated zone-based control while significantly improving resource efficiency by avoiding unnecessary applications in areas that don't require them.
4Reliability
If plant protection agents are applied throughout the entire field, then protection coverage is improved, but resistance formation risk increases
Solution Approach 1:
The system applies plant protection agents locally to only those zones where pests or diseases are actually detected, rather than broadcasting treatment across the entire field. This localized approach maintains effective protection coverage in affected areas while reducing overall chemical exposure that drives resistance development.
Solution Approach 2:
The system implements partial action by applying plant protection agents only to specific zones where they are genuinely needed based on sensor detection, rather than uniform field-wide application. This reduces the total quantity of chemicals used and minimizes the selection pressure that leads to resistance formation, while still maintaining adequate protection where required.
Data Source
AI summary
The present invention relates to the planning and implementation of agricultural measures using remote sensing data and local field data.Using remote sensors, the total required amount and partial-area-specific required amounts of plant protection agents and/or nutrients and/or seeds and/or the like can be determined, and based on this information, the use of an application device can be planned. Using local field sensors, the current local required amounts in the field are determined so that the application device can apply the corresponding amounts as required.