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Related Experiment Video

Updated: Jul 16, 2025

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

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

Published on: March 28, 2025

Maize seed appearance quality assessment based on improved Inception-ResNet.

Chang Song¹, Bo Peng¹, Huanyue Wang¹

¹College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China.

Frontiers in Plant Science

|September 11, 2023

Summary

This study introduces an improved Inception-ResNet network for rapid, accurate corn seed quality evaluation. The AI model achieves high accuracy in detecting seed appearance quality, supporting intelligent seed sorting.

Keywords:

attention mechanism corn seed depthwise separable convolution feature fusion quality assessment

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Last Updated: Jul 16, 2025

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Author Spotlight: Improved Methods for Preparing Transverse Sections and Unrolled Whole Mounts of Maize Leaf Primordia for Fluorescence and Confocal Imaging

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A Simple Dry Sectioning Method for Obtaining Whole-Seed-Sized Resin Section and Its Applications

A Simple Dry Sectioning Method for Obtaining Whole-Seed-Sized Resin Section and Its Applications

Published on: January 23, 2021

Area of Science:

Agricultural Engineering
Computer Vision
Machine Learning

Background:

Manual seed quality inspection is subjective, time-consuming, and hinders practical application needs.
Automated methods are required for efficient and objective seed quality assessment.

Purpose of the Study:

To develop a rapid and accurate seed-quality evaluation method using an improved deep learning network.
To address the limitations of manual inspection for corn seed appearance quality.

Main Methods:

An improved Inception-ResNet network was utilized for corn seed quality evaluation.
Depthwise separable convolution and an attention mechanism were incorporated to optimize the network.
A feature fusion strategy was employed to enhance information retention.

Main Results:

The proposed method achieved high performance metrics: 96.03% detection accuracy, 96.27% precision, 96.03% recall, and 96.15% F1 score.
Average image detection time was approximately 2.44 seconds, enabling rapid, nondestructive detection.
The model demonstrated effective feature learning and information extraction for appearance quality.

Conclusions:

The developed AI-powered method offers a reliable and efficient solution for corn seed appearance quality detection.
This research provides a foundation for developing intelligent seed sorting equipment.
The study highlights the potential of deep learning in agricultural applications for quality control.