Crop Selection Using Satellite Image Prediction Models
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Solution Overview
Problem
Existing crop selection systems require extensive data collection and specialized knowledge to build databases, making it difficult for non-experts to select suitable crops for cultivated land without significant time and cost investments.
Innovation Solution
A crop selection apparatus and method utilizing machine learning to create a prediction model based on information from sample cultivated lands, allowing for the calculation of prediction values for various crops and selecting suitable crops for specific lands without specialized knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a database of soil conditions and crop varieties is built using conventional methods, then crop selection accuracy is improved, but the time and cost required for data collection and database construction increase significantly
Solution Approach 1:
The patent uses satellite images as a copy or proxy representation of actual soil conditions. Instead of physically collecting and analyzing extensive soil data, the system captures visual information from satellite images and processes this copy data through machine learning models to infer soil properties and determine suitable crops, thereby avoiding the time-consuming process of actual soil analysis for every cultivated land
Solution Approach 2:
The patent replaces manual soil analysis and expert knowledge-based database construction with an automated machine learning system. The machine learning model automatically processes satellite image data to predict soil conditions and crop suitability, substituting the mechanical process of manual data collection and expert verification with an automated computational approach
2Measurement precision
If a database of soil conditions and crop varieties is built using conventional methods, then crop selection accuracy is improved, but the expertise and resources required for database construction increase significantly
Solution Approach 1:
The patent uses satellite images as a copy or proxy representation of actual soil conditions. Instead of physically collecting and analyzing extensive soil data, the system captures visual information from satellite images and processes this copy data through machine learning models to infer soil properties and determine suitable crops, thereby avoiding the time-consuming process of actual soil analysis for every cultivated land
Solution Approach 2:
The patent introduces satellite images as an intermediary medium between the physical soil conditions and the crop selection process. Rather than directly analyzing soil properties, the system uses satellite image data as an intermediate representation that can be processed by machine learning models to infer soil characteristics and determine crop suitability
3Measurement precision
If conventional crop selection methods are used, then specialized knowledge is required for accurate crop selection, but the system becomes inaccessible to users without expert knowledge
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically processes satellite image data and provides crop selection recommendations without requiring user expertise. The system performs soil condition inference and crop suitability assessment autonomously, allowing any user to obtain accurate crop selection advice simply by providing location information, without needing to understand or input complex soil data
Data Source
AI summary
A cultivation-target crop selection assisting apparatus 10 includes: an information collection unit 11 configured to collect information regarding a specific cultivated land; a prediction value calculation unit 12 configured to calculate a prediction value of actual performance of cultivation of one or a plurality of varieties of crops in the specific cultivated land by applying the information collected by the information collection unit 11 to a prediction model 14 created by performing machine learning on a relationship between information regarding a sample cultivated land and actual performance information regarding a crop produced in the sample cultivated land; and a crop selection unit 13 configured to select a crop that is suitable for being cultivated in the specific cultivated land based on the prediction value calculated for the one or plurality of varieties of crops.