Semantic Crop Damage Assessment Using Multi-Source Reasoning
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Solution Overview
Problem
Conventional crop damage assessment techniques are inaccurate and not universally applicable due to their reliance on specific data sources, leading to biased estimates and a lack of consideration for the diversity of natural calamities and their interrelationships with crop types.
Innovation Solution
A system utilizing domain ontologies and semantic reasoning over spatio-temporal data integrates satellite-based earth observations, weather observations, and IoT sensors to automatically assess crop damage, incorporating social media and news articles for comprehensive insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques use only specific data sources (weather data, satellite data, or geological information) individually or in combination, then the assessment process is simpler, but the accuracy and reliability of crop damage estimation deteriorates due to bias and inability to capture diversity of natural calamities
Solution Approach 1:
The patent merges multiple data sources including satellite imagery, weather data, geological information, and social media data into a unified assessment framework. This combination allows the system to capture the diversity of natural calamities and their interrelationships with crop types, thereby improving estimation accuracy while managing complexity through integrated processing
Solution Approach 2:
The patent creates a universal crop damage assessment system that can handle multiple types of natural calamities (floods, droughts, cyclones, earthquakes, landslides) and various crop types through a single multi-functional framework. This approach eliminates the need for separate assessment methods for each calamity-crop combination, improving both accuracy and efficiency
2Productivity
If conventional techniques use manual survey-based approaches, then the implementation is straightforward, but the productivity and coverage area are limited
Solution Approach 1:
The patent replaces manual survey-based mechanical assessment methods with an automated computer-based system that processes satellite imagery, weather data, and other information sources. This substitution dramatically increases productivity and coverage area while the system manages complexity through automated data integration and analysis algorithms
Solution Approach 2:
The system enables self-service crop damage assessment by automatically collecting data from multiple sources, processing the information, and generating assessment reports without requiring manual field surveys. This automation significantly improves productivity while the standardized processing framework manages system complexity
3Adaptability or versatility
If conventional techniques focus on single-type calamity assessment, then the method is simpler to implement, but the adaptability to different natural calamities and crop types deteriorates
Solution Approach 1:
The patent implements a universal assessment framework that can handle multiple calamity types (floods, droughts, cyclones, earthquakes, landslides) and various crop types through a single system. This multi-functional approach improves adaptability while managing complexity through integrated data processing and standardized assessment protocols
Solution Approach 2:
The system dynamically adapts to different calamity types and crop types by adjusting its data processing and analysis methods based on the specific assessment context. This dynamic capability improves versatility while the underlying standardized framework manages complexity through flexible yet structured processing routines
Data Source
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AI summary
The disclosure generally relates to methods and systems for crop damage assessment using semantic reasoning. Conventional techniques using only specific data either individually or in a combination may result in bias and may not accurately estimate the crop damage, due to diversity in each of the natural calamities. The present disclosure solves the technical problems in the art using domain ontologies and a semantic reasoning over the spatio-temporal data for the automatic assessment of the crop damage due to the natural calamities. The present disclosure establishes automated crop loss assessment using trigger-based analysis of plurality of sources like satellite-based earth observations, weather observations, social media posts and news articles, for obtaining a spatio-temporal data. Then the spatio-temporal data is reasoned over the domain knowledge graph, using the semantic reasoning technique, for the crop damage assessment.