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Related Experiment Video

Updated: Apr 15, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

Published on: June 28, 2016

A Multi-Scale Dense Perception and Scale-Adaptive Approach for Blueberry Ripeness Detection.

Shutao Guo^1,2,3, Ning Yang^1,2,3, Shanchen Pang^1,2,3

¹Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China.

Foods (Basel, Switzerland)

|April 14, 2026

Summary

This summary is machine-generated.

This study introduces BBYOLOv12, an AI model for accurate blueberry ripeness detection in complex orchards. It significantly improves detection accuracy and reduces computational load for intelligent harvesting.

Keywords:

YOLOv12 deep learning fruit ripeness object detection

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Last Updated: Apr 15, 2026

Construction of Models for Nondestructive Prediction of Ingredient Contents in Blueberries by Near-infrared Spectroscopy Based on HPLC Measurements

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Published on: June 28, 2016

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Fruit Volatile Analysis Using an Electronic Nose

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Published on: March 30, 2012

Area of Science:

Computer Vision
Agricultural Technology
Machine Learning

Background:

Accurate blueberry ripeness detection is vital for intelligent harvesting.
Complex orchard environments with dense fruit clusters pose challenges for current detection methods, leading to missed detections and ripeness misjudgments.

Purpose of the Study:

To develop an improved object detection model, BBYOLOv12, to enhance blueberry ripeness detection accuracy in challenging agricultural settings.
To address limitations in existing models regarding missed detections and ripeness misjudgments in dense fruit clusters.

Main Methods:

Integration of a lightweight RepGhost backbone for efficient multi-scale feature extraction.
Modification of the SimAM attention mechanism to improve feature capture in dense fruit regions.
Implementation of an improved WIoU loss function for optimized small object localization.

Main Results:

BBYOLOv12 achieved high performance metrics: mAP@0.5 of 98.97%, mAP@0.5:0.95 of 83.55%, precision of 97.55%, and recall of 97.27%.
The model demonstrated superior performance compared to baseline and other lightweight models.
Achieved high accuracy with a reduced model complexity (2.36 million parameters and 5.59 GFLOPs).

Conclusions:

BBYOLOv12 offers an effective solution for multi-scale, dense perception tasks in agricultural applications, particularly for blueberry harvesting.
The developed model provides a practical tool with a Graphical User Interface for real-time detection and analysis.
This research contributes to advancing intelligent agricultural systems through improved computer vision techniques.