Intelligent optical fruit sorting and grading system

Enhance product quality and reduce waste with cutting-edge optical sorting technology, designed to detect and grade fruit and vegetable imperfections with precision, ensuring high-speed, consistent throughput in food processing lines.

Intelligent optical fruit sorting and grading system
Great for making:
Intelligent optical fruit sorting and grading system Apples Intelligent optical fruit sorting and grading system Potatoes Intelligent optical fruit sorting and grading system Blueberries Intelligent optical fruit sorting and grading system Onions Intelligent optical fruit sorting and grading system Potato chips Intelligent optical fruit sorting and grading system Pistachios Intelligent optical fruit sorting and grading system Hazelnuts Intelligent optical fruit sorting and grading system Beans Intelligent optical fruit sorting and grading system Olives

Sorts and Grades Diverse Produce Efficiently

The Spectrim with LUCAi® from TOMRA is an innovative optical sorting and grading machine designed to enhance efficiency and precision in fruit and produce processing. Utilizing deep learning and advanced computing hardware, this solution tackles complex quality assessment tasks with exceptional accuracy, identifying defects in a wide range of products from apples to nuts. With LUCAi® technology, the system enhances throughput by reducing manual inspection errors and is easily integrated into existing processing lines through its user-friendly interface. Operating continuously and at high speed, it meets the demands of the food and agriculture industries by delivering consistent results in sorting and grading. This machine excels at minimizing fruit loss and optimizing pack-out results, ensuring high-quality output. Configurations are available to support different produce types and operational requirements, backed by engineering support for seamless customization and installation.

Benefits

  • Increases yield and reduces waste by detecting over 95% of imperfections.
  • Cuts operational costs through reduced manual labor and improved grading precision.
  • Enhances product quality and consistency across diverse produce types.
  • Simplifies integration and operation with an intuitive user interface.
  • Supports continuous, automated processing for high-speed operations.

General


Applications
Potatoes,Protein,Fruit,Vegetables,Nuts and dried fruit,Seeds and grains
End products
Blueberries,Cherries,Onions,Potato chips,Pistachios,Peas,Kiwi,Mangoes,Meat products,Seafood,Citrus fruits,Bell peppers,Hazelnuts,Beans,Cauliflower,Olives,Pecans,Fresh pack potatoes,Strawberries,Frozen potato products,Pineapples,Cashews,Almonds,Carrots,Avocados,Apples,Peanuts,Dehydrated potato products,Macadamias,Radishes,Walnuts
Steps before
Harvesting,Sorting,Washing,Pre-selection
Steps after
Quality Control,Packing,Storage,Distribution
Input ingredients
apples,fruit,stem splits,punctures
Output ingredients
graded apples,sorted fruit,premium yields,reduced fruit loss,consistent quality
Market info
TOMRA is known for its expertise in sensor-based solutions for resource productivity, particularly in recycling, food sorting, and mining industries, offering advanced technology to improve resource recovery and efficiency, enhancing sustainability and operational performance.

Technical Specifications


Accuracy
Detects 95% of stem-related imperfections,mitigates 99% of total stem defects
Automation Level
Deep learning technology with LUCAi®
Grading Precision
Unprecedented precision with LUCAi® models
User Interface
User-friendly interface
Operational Cost Reduction
Reduced operational costs with LUCAi®
Throughput Improvement
Improves throughput by decreasing inspection errors
Flexibility
Adjusts grading parameters for seasonal dynamics
Integration Capability
Easy integration into operations
Detection Capabilities
Stem splits and punctures detection across apple varieties

Operating Characteristics


Automation level
Deep Learning Technology
Batch vs. continuous operation
Batch / Inline Continuous
Cleaning method
N/A (Assumed automated with minimal manual intervention)
Changeover time
Adjustable with pre-trained models
Energy efficiency
Optimized for precision and cost-efficiency
Inspection error reduction
Decreased manual inspection errors
User interface
User-friendly interface

Material Compatibility


Biological compatibility
Yes
Corrosive resistance (e.g. acids)
Yes
Cleanability
Easy

Product type Characteristics


Fruit Types
Apples,multiple varieties
Market Preferences
Adjustable grading parameters

Physical Characteristics


Control panel type
User-friendly interface

Custom Options


Control panel type
User-friendly interface
Integration possibilities
Easy to integrate into operations
Parameter adjustability
Adjust severity of grading parameters
Deep learning models
Pre-trained LUCAi® models for apple defects
Equipment Groups
Optical Sorters
Product Groups
Food Fruit Fruit pulp Meat Nuts Protein Seafood Seeds Vegetables
Processes
Detection Grading Inspection Measurement & Control Sorting