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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Automated grading of oleaster fruit using deep learning.

Aram Azadpour¹, Kaveh Mollazade², Mohsen Ramezani³

¹Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran.

Scientific Reports

|February 12, 2025

Summary

This study developed an automated machine vision system for grading oleaster fruit. Deep learning models achieved high accuracy in classifying fruit quality at various speeds, improving post-harvest technology.

Keywords:

Image segmentation Mask R-CNN Quality evaluation Real-time classification YOLOv8

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Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

The agriculture sector relies on efficient post-harvest technologies, especially in developing economies.
Manual grading of oleaster fruit is labor-intensive and subjective, hindering scalability.
Increasing global demand necessitates automated solutions for oleaster fruit classification.

Purpose of the Study:

To develop a real-time machine vision system for automated oleaster fruit grading.
To evaluate the performance of deep learning models in classifying oleaster fruit at different grading velocities.
To establish an efficient automated system for oleaster fruit quality assessment.

Main Methods:

Acquired a dataset of oleaster fruit video frames at varying conveyor belt velocities (4.82–21.51 cm/s).
Utilized Mask R-CNN for accurate sample segmentation, achieving 100% detection and low error rates (4.17–5.79%).
Employed YOLOv8n for real-time classification, demonstrating high accuracy and efficiency.

Main Results:

Mask R-CNN accurately segmented all oleaster classes across all tested velocities.
YOLOv8x and YOLOv8n models showed comparable classification performance.
The YOLOv8n model achieved 92% overall classification accuracy at 21.51 cm/s, with high sensitivity (87.10–94.89%).

Conclusions:

Deep learning models are effective for developing automated oleaster fruit grading machines.
The developed system offers a viable solution for efficient and accurate oleaster fruit classification.
This technology can significantly enhance post-harvest processing in the agricultural sector.