Traditional citrus grading has long been a manual process requiring significant human labor. This approach is not only tedious but also prone to errors due to its high demand on workers’ attention, leading to reduced overall efficiency. To address these challenges, an orchard operator in Kaohsiung, Taiwan partnered with Hitspectra, an intelligent inspection and vision system integrator. Hitspectra implemented a cutting-edge computer-vision-based sorting solution that dramatically improved the operation’s productivity.

The Challenge of Manual Fruit Sorting

Citrus fruit grading requires inspecting multiple characteristics including size, color, blemishes, rot, mold, and other defects. Performing these evaluations manually is time-consuming and labor-intensive. Mistakes in grading can lead to financial losses, while slow processing rates limit output capacity for commercial operations.

Deep Learning-Powered Inspection System

Hitspectra developed an innovative computer-vision system that integrates deep learning methodologies to automate citrus inspection. This approach enables the identification of quality parameters that are difficult to quantify through traditional rule-based systems:

  • Enhanced Detection Capabilities: Unlike conventional sorting methods, this solution can identify subtle defects and irregularities that might be missed by human inspectors
  • Multiple Inspection Criteria: The system evaluates fruit based on a comprehensive set of criteria including shape, size, color variations, surface blemishes, mold, decay patterns

The implementation process involved integrating specialized cameras into the existing sorting infrastructure. As citrus fruits move along the conveyor belt, multiple imaging systems capture detailed views from different angles. Advanced algorithms then analyze these images against training data to determine quality parameters and categorize each fruit as “acceptable” or “non-acceptable”.

Increased Efficiency Through Automation

The transition to automated inspection delivered remarkable results:

  1. Throughput Improvements: A single operator can now process twice the volume (800 kg/hour) compared to three manual workers
  2. Accuracy Enhancement: The AI system has achieved a 90% accuracy rate, with potential for further improvement through additional training data

As Hitspectra’s manager noted: “Automated visual inspection transforms agricultural processing by reducing reliance on manual labor and enabling standardized quality assessment.”

Technical Specifications of the Imaging Solution

The system utilizes The Imaging Source’s advanced industrial cameras featuring:

  • 5-megapixel resolution providing sufficient detail without overwhelming computational resources
  • USB3 Vision compliance ensuring seamless integration into existing infrastructure
  • HDR imaging functionality for capturing high-quality images under varying lighting conditions
  • Compact design suitable for installation in confined spaces

These technical characteristics enable the system to maintain efficient operation while delivering precise inspection results. The camera technology plays a crucial role in balancing detailed image capture with processing speed, allowing the automated system to operate within optimal cycle times.

The implementation of this vision-based sorting solution demonstrates how computer vision and deep learning technologies are transforming traditional agricultural processes into highly efficient, standardized operations that deliver consistent quality outcomes.

Last Updated: 2025-09-05 00:02:06