Crop Pest Risk Prediction Using Time-Series Environmental Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting crop pests and diseases rely heavily on factors like crop genomes and microbial pathogens, which are difficult to collect and manage, limiting their applicability, and existing image analysis methods require visible pests or diseases for determination, making them ineffective as preventative measures.
Innovation Solution
An apparatus and method using time-series environmental data, such as temperature and humidity, to predict crop pest and disease risks through deep learning, generating a risk prediction model and providing a prescription for environmental adjustments to prevent occurrences, while also analyzing crop images to determine pest types and progress when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prediction models based on crop genomes, crop nutrients, and microbial pathogens are used, then prediction accuracy is improved, but data collection and management difficulty increases
Solution Approach 1:
The patent extracts and focuses on a specific subset of environmental factors (temperature, humidity, CO2 concentration, solar radiation) that are most critical for pest and disease prediction, rather than attempting to collect and manage all possible biological and environmental data. This selective extraction maintains prediction accuracy while significantly reducing data collection complexity.
Solution Approach 2:
The prediction model is designed to be universally applicable across multiple crop types and pest-disease combinations by using general environmental parameters that affect all plant life. This universal approach eliminates the need for crop-specific or pest-specific data collection systems, reducing management difficulty while maintaining broad predictive capability.
2Measurement precision
If image analysis methods are used to determine pests and diseases, then determination accuracy is improved, but resource consumption increases and prevention capability is reduced
Solution Approach 1:
The system performs preliminary prediction using environmental data before pests and diseases become visible. By predicting risk levels in advance based on environmental conditions, the system can alert farmers to take preventive measures before actual infestation occurs, eliminating the need for resource-intensive image analysis while maintaining prevention capability.
Solution Approach 2:
The patent introduces environmental data as an intermediary indicator that correlates with pest and disease risk. Instead of directly analyzing crops for signs of infestation (which requires visible pests and high resources), the system uses environmental parameters as a proxy to predict risk, reducing resource consumption while maintaining determination accuracy.
3Reliability
If pesticides are sprayed after pests become visible, then treatment effectiveness is reduced, but early prediction capability is not utilized
Solution Approach 1:
The system performs preliminary prediction of pest and disease risk based on environmental data before actual infestation occurs. This early warning allows farmers to take preventive actions (such as applying biological control agents or adjusting environmental conditions) before pests become visible, significantly improving treatment effectiveness and reducing the need for chemical pesticides.
4Measurement precision
If deep learning and image analysis are applied continuously, then prediction accuracy is maintained, but unnecessary chemical application and resource waste increase
Solution Approach 1:
The system uses periodic environmental monitoring and threshold-based prediction rather than continuous image analysis. By continuously collecting environmental data and comparing it against known risk thresholds, the system maintains prediction accuracy while activating resource-intensive interventions (such as pesticide application or detailed image analysis) only when necessary, reducing chemical waste and resource consumption.
Data Source
AI summary
Proposed are an apparatus and method for predicting a crop pest and disease risk using time-series environmental data, by which a prediction model for predicting a crop pest and disease risk according to changes in a growth environment is generated by collecting and analyzing time-series public environmental data such as temperature, humidity, CO2 concentration, and solar radiation at a crop cultivation site, and a treatment recipe for taking a rapid action before pests and diseases occur or at an early stage using the prediction model is provided.