Digital Cultivation Map Generation via Satellite Image Analysis
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Solution Overview
Problem
Existing precision farming methods require manual input for defining management zones and learning blocks, which is error-prone and complex, especially when different zones have different orientations, limiting the ability to automatically account for machine working parameters in digital cultivation maps.
Innovation Solution
A method and system that analyze images of fields to identify cultivation features, ascertain working parameters, and create digital cultivation maps with zones matched to these parameters, allowing for automatic consideration of machine working parameters in precision farming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual input is used for defining management zones and learning blocks, then the user can define zones according to their needs, but the process becomes error-prone and complex, especially when different zones have different orientations
Solution Approach 1:
The system automatically extracts cultivation features from satellite images and determines management zones without requiring manual user input. The computer system performs image analysis, identifies cultivation patterns, and generates zones autonomously, eliminating the complex manual configuration process while maintaining adaptability to different field conditions
Solution Approach 2:
The manual mechanical process of defining zones is replaced by an automated image processing and analysis system. The system uses computer vision algorithms to extract cultivation features from satellite imagery and automatically generates management zones based on detected patterns, replacing the need for manual geometric construction and orientation specification
2Ease of operation
If manual orientation of driving tracks is performed, then the user can specify cultivation directions, but the process becomes error-prone and complex
Solution Approach 1:
The system automatically determines driving track orientations by analyzing cultivation features in satellite images. The computer system extracts directional information from detected cultivation patterns and uses this data to orient management zones and learning blocks, eliminating manual orientation input and its associated errors
Solution Approach 2:
The system copies directional information directly from the satellite image data itself. By detecting cultivation features such as crop rows and field patterns in the images, the system reproduces the actual field orientations in the digital model, ensuring accuracy matches the physical reality rather than relying on manual transcription
3Extent of automation
If automatic image analysis is used to identify cultivation features, then working parameters can be ascertained automatically, but the system complexity increases
Solution Approach 1:
The system uses satellite images as an intermediary data source that contains embedded cultivation information. By analyzing these images for cultivation features such as crop patterns, field boundaries, and driving tracks, the system automatically derives working parameters without requiring direct complex measurements or manual surveys of the field conditions
Solution Approach 2:
Complex manual field measurements and surveys are replaced by automated image processing algorithms. The system uses computer vision and pattern recognition to extract cultivation features from satellite imagery, automatically determining working parameters such as zone orientations, dimensions, and locations without mechanical field operations
Data Source
AI summary
The present invention relates to the technical field of precision farming. The present invention provides a method, a computer system and a computer program product with which a digital cultivation map for a field is created, said cultivation map comprising multiple zones, with at least one feature of at least one zone matched to at least one value of a machine working parameter that has been obtained from an image of the field.