Crop Pest Infection Timing Estimation from Environment Data
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Solution Overview
Problem
Existing technologies struggle to accurately predict the timing of pest infections in crops, making it difficult to effectively apply agricultural chemicals before or after infection, thus improving pest protection methods.
Innovation Solution
An information processing device and system that utilizes machine learning to analyze pest occurrence information and environment data, distinguishing between event occurrence periods and event-free periods to estimate the timing of pest-related crop damage accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pest occurrence prediction is performed using past environment information and cultivation information, then pest protection measures can be taken, but the accuracy of infection timing estimation remains insufficient
Solution Approach 1:
The patent segments the time period into event occurrence periods (where pest infection is suspected) and event-free periods (where no infection occurred). This segmentation allows the system to focus analysis on specific time windows, improving the precision of infection timing estimation while maintaining reliable pest protection through comprehensive period coverage.
Solution Approach 2:
Instead of directly predicting infection timing from mixed data, the patent inverts the approach by first identifying periods where infection did NOT occur (event-free periods) and comparing them with periods where infection is suspected. This indirect approach enhances estimation accuracy by providing a baseline for normal conditions.
2Measurement precision
If environmental data and cultivation information are collected for analysis, then infection prediction capability is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the necessary features from the collected environmental data and cultivation information that are relevant to pest infection prediction. By selecting and extracting key parameters rather than processing all available data, the system achieves accurate infection prediction while reducing computational complexity and processing requirements.
3Measurement precision
If comprehensive environment information is analyzed to estimate event occurrence time, then estimation accuracy is improved, but processing time and computational load increase
Solution Approach 1:
The patent performs preliminary segmentation of time periods and pre-identification of event occurrence and event-free periods before conducting detailed analysis. This preliminary action organizes the data structure in advance, enabling more efficient processing during the actual estimation phase and reducing overall computational time while maintaining high accuracy.
Solution Approach 2:
The patent focuses analysis on partial periods (event occurrence periods and event-free periods) rather than analyzing all time data continuously. By concentrating computational resources on these specific partial periods where infection status changes occur, the system achieves accurate timing estimation with reduced processing time compared to continuous full-period analysis.
Data Source
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AI summary
To estimate an event occurrence time when an event caused by a pest and leading to damage to a crop has occurred. Provided is an information processing device including an acquisition unit that acquires pest occurrence information including information on an occurrence time of damage to a crop caused by a pest and environment information including an cultivation environment for the crop; an event period determining unit that determines, using the pest occurrence information, an estimated event occurrence period during which an event leading to the occurrence of the damage to the crop is suspected to have occurred and an event-free period during which the event leading to the occurrence of the damage has not occurred; and an event estimation unit that estimates, by comparing the environment information in the estimated event occurrence period with the environment information in the event-free period, an event occurrence time when the event has occurred from the estimated event occurrence period.