Remote Crop Damage Assessment Using Mobile Image Analysis
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Solution Overview
Problem
The current farmer insurance claims processing is largely manual, making it inefficient and costly, with automated assessment systems using robotic or drone platforms not being successfully implemented, leading to a need for improved remote farm damage assessment and claims processing methods.
Innovation Solution
A system and method for remote farm damage assessment that involves determining damage assessment locales, incorporating them into a workflow, receiving images from user devices with geolocation information, and using a machine learning model to assess damage, providing a damage assessment indication, including whether damage exists and its confidence level, thereby automating the evaluation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual claims processing is used, then assessors can evaluate damage in the field, but the process is slow and costly
Solution Approach 1:
The patent replaces the mechanical manual assessment system with an automated image-based system. Users capture images of damaged crops using mobile devices, and machine learning models automatically analyze these images to determine damage extent and calculate payouts, eliminating the need for physical field visits by assessors and dramatically speeding up the claims process.
Solution Approach 2:
The patent creates digital copies (images) of the physical crop damage. Instead of assessors physically examining crops, the system captures images of the damaged areas and uses these digital replicas for analysis. This copying approach enables remote assessment and allows multiple analyses of the same damage without requiring repeated physical visits.
2Extent of automation
If robotic or drone platforms are used for automation, then assessment can be automated, but implementation has been unsuccessful
Solution Approach 1:
The patent makes the assessment system universally accessible by using common mobile devices that farmers and assessors already possess. Instead of requiring specialized robotic or drone equipment, the system works with standard smartphones and tablets, allowing the same device to serve multiple functions including image capture, GPS location tracking, and communication with the assessment server.
Solution Approach 2:
The patent replaces expensive, complex robotic or drone platforms with inexpensive, widely available mobile devices. The system accepts images from any standard mobile device camera, eliminating the need for costly specialized equipment while achieving the same automation goals through software-based image analysis.
3Productivity
If remote image-based assessment is used, then processing speed increases, but image quality and accuracy must be ensured
Solution Approach 1:
The patent incorporates feedback mechanisms where the system provides real-time guidance to users during image capture. The mobile application guides users on proper imaging techniques, and the system validates received images for quality standards. This feedback loop ensures that images captured by non-expert users meet the necessary quality thresholds for accurate machine learning analysis.
Solution Approach 2:
The patent performs preliminary actions by providing structured guidance workflows before the actual assessment occurs. The system pre-defines assessment locales, specifies required image types and qualities, and guides users through the capture process in advance. This preliminary structuring ensures that when images are received, they are already suitable for accurate analysis without requiring extensive post-capture processing.
Data Source
AI summary
Systems and methods for providing remote farm damage assessment are provided herein. In some embodiments, a system and method for providing remote farm damage assessment may include, determining a set of damage assessment locales for damage assessment; incorporating the set of damage assessment locales into a workflow; providing the workflow to a user device; receiving a first set of damage assessment images from the user device based on the workflow provided, wherein each of the first set of damage assessment images includes geolocation information and camera information; determining a damage assessment based on the first set of damage assessment images using a damage assessment machine learning model; and outputting a damage assessment indication including one or more of whether there is damage, a confidence level of assessing the damage, or a confidence level associated with the level of damage.