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Updated: Jan 17, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Research on persimmon fruit diameter accurate detection method based on improved RCNN instance segmentation

Yuan Fang^1,2, Yangyang Liu^1,2, Ya Feng^1,3

¹School of Mechanical Engineering, Anhui University of Technology, Ma'anshan, China.

Frontiers in Plant Science

|September 15, 2025

Summary

This summary is machine-generated.

This study introduces an improved Mask RCNN algorithm for accurate persimmon recognition and diameter measurement. The enhanced method significantly reduces errors caused by fruit overlap, improving precision for agricultural applications.

Keywords:

Mask RCNN binarization fruit diameter detection instance segmentation algorithm persimmon recognition

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Area of Science:

Computer Vision
Agricultural Technology
Machine Learning

Background:

Inaccurate fruit recognition and diameter detection hinder persimmon inspection.
Existing methods struggle with overlapping or occluded fruits.

Purpose of the Study:

To develop a novel algorithm for accurate persimmon recognition and fruit diameter detection.
To improve segmentation accuracy and reduce measurement errors in persimmon inspection.

Main Methods:

Utilized Mask R-CNN with instance segmentation, incorporating cropping, morphological processing, and concave point segmentation.
Integrated template matching for image recognition and addressed fruit sticking issues.

Main Results:

Achieved a mean Average Precision (mAP) of 94.25%, an 8.05% improvement over the original algorithm.
Increased Mean Intersection-over-Union (MIoU) by 18.5% and reduced maximum relative error in diameter measurement to 1.3%.

Conclusions:

The improved Mask RCNN algorithm enhances persimmon recognition and diameter measurement accuracy.
Provides valuable insights for intelligent inspection, yield estimation, and mechanized picking in agriculture.