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Related Experiment Video

Updated: Jun 14, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.

Shaotong Ning¹, Feng Tan¹, Xue Chen¹

¹College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China.

Sensors (Basel, Switzerland)

|August 29, 2024

Summary

This summary is machine-generated.

This study introduces an improved YOLOv8 model for accurate maize leaf counting. The enhanced method significantly boosts detection accuracy and efficiency, aiding plant growth assessment and breeding decisions.

Keywords:

SatrNet YOLOv8 leaf counting lightweight maize

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Last Updated: Jun 14, 2025

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: January 21, 2013

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Accurate maize leaf counting is crucial for assessing plant growth and optimizing population structure.
Traditional manual methods are laborious and prone to errors, while existing image processing techniques lack accuracy and adaptability for practical use.

Purpose of the Study:

To develop an improved, lightweight YOLOv8-based method for accurate maize leaf detection and counting.
To enhance feature representation and fusion capabilities for better maize growth status detection.

Main Methods:

The study proposes an improved lightweight YOLOv8 model incorporating StarNet backbone and a Convolution and Attention Fusion Module (CAFM).
Modifications include enhancing the C2f module with StarBlock in the neck network and implementing a Lightweight Shared Convolutional Detection Head (LSCD).

Main Results:

The improved model achieved high performance metrics: 97.9% precision, 95.5% recall, and 97.5% mAP50.
The model size and parameter count were significantly reduced by 39.68% and 40.86% respectively, compared to the original YOLOv8.

Conclusions:

The developed model offers superior accuracy and efficiency for maize leaf detection and counting.
This technology supports scientific decision-making in breeding, mobile detection device applications, and high-quality maize growth assessment.