Automating Pickled Mustard Trimming with Image Segmentation

Based on Patent Research | CN-111070284-A (2020)

Ensuring consistent texture and quality in preserved food products, such as szechuan pickles, is paramount. Manually removing tough 'rubber band meat' (skin and tendon) is often inefficient and inconsistent, affecting the final product's appeal. Image segmentation, which precisely outlines specific regions within an image, offers a solution. This technology accurately identifies and maps these undesirable parts. It enables automated, consistent removal, improving product quality and operational efficiency.

SOLUTION OVERVIEW

Manual Inspection Enhanced by AI-powered Detection

In food manufacturing, ensuring consistent product quality in items like preserved szechuan pickles presents a core challenge. Image segmentation technology offers a powerful solution. This system receives visual input of food items, such as mustard tuber slices, then employs advanced algorithms to precisely delineate specific regions. It accurately identifies and maps the exact contours of undesirable components like tough skin or tendon, often referred to as 'rubber band meat'. This process generates a detailed digital outline, guiding subsequent automated removal for uniform quality.

This capability enables significant automation, allowing for integration into existing production lines to streamline processing. Instead of manual sorting or trimming, the technology precisely targets and removes inconsistent textures, boosting operational throughput and maintaining product integrity. Consider it like an automated fruit peeler that adjusts its depth based on each fruit's exact contours, minimizing waste and maximizing edible portions. Such intelligent vision systems elevate production standards, optimize resource utilization, and consistently deliver superior preserved food products to consumers.

HOW IT WORKS

Discovering Defects via Image Analysis

Capturing Product Imagery

High-resolution cameras capture transparency images of food items, such as pickled mustard tuber slices, as they move along the production line. These detailed visual inputs provide a comprehensive view of the product's surface and internal structure, preparing them for analysis.

Analyzing Texture Anomalies

The captured images are fed into the AI system, which employs advanced image segmentation algorithms to analyze each slice for inconsistencies. It precisely identifies and maps the exact contours of undesirable components, such as tough skin or tendon, often referred to as "rubber band meat."

Guiding Precision Removal

Based on the detailed analysis, the system generates a precise digital outline, or segmentation map, of the identified anomalies. This accurate map then guides automated machinery to target and remove the inconsistent textures with high precision, ensuring consistent and uniform product quality.

Potential Benefits

Enhanced Product Consistency

This system precisely identifies and removes undesirable components like 'rubber band meat', ensuring uniform texture and quality across all preserved food products. It eliminates inconsistencies inherent in manual processing, leading to a superior consumer experience.

Increased Operational Efficiency

By automating the precise identification and removal of unwanted parts, the solution significantly streamlines production lines. This reduces manual labor, boosts throughput, and optimizes processing time in food manufacturing.

Minimized Material Waste

Accurate image segmentation ensures only targeted undesirable components are removed, preserving more edible product. This intelligent targeting reduces raw material waste and optimizes resource utilization, contributing to cost savings.

Improved Quality Control

The AI system establishes a consistent standard for product quality by precisely mapping and enabling automated removal of defects. This ensures every item meets stringent quality benchmarks, enhancing brand reputation and consumer trust.

Implementation

1 Install Vision Hardware. Set up high-resolution cameras and automated removal equipment on the production line. Ensure proper alignment and robust connectivity.

2 Gather Training Data. Collect diverse images of mustard tuber slices, manually annotating 'rubber band meat' to create a dataset for model training.

3 Configure AI Model. Train the image segmentation model using the prepared data. Calibrate its algorithms to precisely identify undesirable textures in products.

4 Integrate Production System. Connect the AI system with existing factory automation. Ensure seamless data flow to guide automated trimming machinery.

5 Validate System Accuracy. Conduct comprehensive tests to verify the system's precision in identifying and removing target anomalies. Optimize for consistent product quality.

Source: Analysis based on Patent CN-111070284-A "Rubber band cutting method for flaky pickled mustard tuber slices" (Filed: April 2020).