Two-Sided Agricultural Product Scanning for Automated Quality Assessment
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Solution Overview
Problem
Current agricultural product inspections are time-consuming, laborious, subjective, and expensive, lacking efficient, objective, and cost-effective methods for accurately determining quality and varietal purity, especially during transportation and handling in supply chains.
Innovation Solution
A two-sided scanning apparatus using multiple cameras to capture images of agricultural products from opposing viewpoints, enabling digitization, segmentation, classification, and estimation of physical weights, with image processing technologies powered by computer vision and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to determine agricultural product quality, then subjective assessment can be performed, but the inspection process becomes time-consuming and laborious
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated imaging system using cameras and computer vision algorithms. The system captures images of agricultural products and uses machine learning models to automatically assess quality attributes such as varietal purity, damage, and foreign matter, eliminating the need for time-consuming manual inspection while maintaining or improving assessment accuracy.
Solution Approach 2:
The system enables self-service quality assessment where the agricultural products are automatically analyzed by the imaging and processing system without requiring human intervention. The computer vision algorithms independently evaluate quality attributes, allowing the inspection process to serve itself without labor-intensive manual assessment.
2Measurement precision
If manual inspection methods are used for agricultural product analysis, then detailed quality assessment can be performed, but the cost and labor requirements increase significantly
Solution Approach 1:
The patent replaces expensive manual inspection processes with a cost-effective automated imaging system. By using cameras and open-source computer vision algorithms, the system achieves detailed quality assessment without the high labor costs associated with trained inspectors, making precise quality analysis more accessible and affordable.
Solution Approach 2:
The system uses inexpensive camera equipment and open-source software algorithms instead of expensive specialized inspection devices. The approach leverages readily available, low-cost components that can be deployed widely without requiring significant capital investment, reducing the overall cost of quality inspection while maintaining assessment precision.
3Reliability
If conventional inspection methods are used during supply chain transportation, then quality monitoring can be performed, but the process requires substantial manual labor and training
Solution Approach 1:
The patent replaces complex manual inspection procedures with an automated imaging system that can be easily operated during transportation. The system captures images and automatically processes them through computer vision algorithms, eliminating the need for trained inspectors and complex manual assessment procedures while maintaining reliable quality monitoring throughout the supply chain.
Solution Approach 2:
The quality monitoring system performs self-service analysis by automatically capturing, processing, and evaluating images of agricultural products during transportation. The computer vision algorithms independently assess quality attributes without requiring human operators, making the system easy to operate and deploy in various supply chain environments without extensive training requirements.
4Productivity
If automated imaging systems are implemented for agricultural product analysis, then inspection speed and efficiency improve, but the system complexity increases
Solution Approach 1:
The patent employs a universal imaging system that can perform multiple quality assessment functions using a single camera setup. The computer vision algorithms are designed to evaluate various quality attributes including varietal purity, damage, foreign matter, and physical characteristics, allowing one system to replace multiple specialized inspection devices, thereby improving productivity without proportionally increasing complexity.
Solution Approach 2:
The system uses computer vision algorithms as an intermediary between the simple image capture process and the complex quality assessment requirements. The algorithms automatically process images and extract multiple quality metrics, bridging the gap between simple imaging hardware and sophisticated quality analysis needs, thus improving inspection efficiency while managing system complexity through software mediation.
Data Source
AI summary
This disclosure enables various technologies for enabling various analysis of various agricultural products.