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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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Published on: February 2, 2019

Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.

Ruoling Deng¹, Ming Tao¹, Xunan Huang¹

¹College of Engineering, South China Agricultural University, Guangzhou 510642, China.

Sensors (Basel, Switzerland)

|January 7, 2021

Summary

A new deep learning model accurately counts rice grains on panicles, improving breeding research. This automated method offers a reliable and efficient alternative to manual counting, achieving 99.4% accuracy.

Keywords:

convolutional neural network grain detection image primary branch rice

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Area of Science:

Agricultural Science
Computer Vision
Deep Learning

Background:

Grain number per rice panicle is crucial for yield and breeding.
Manual grain counting is labor-intensive and prone to errors.

Purpose of the Study:

To develop an automated model for recognizing and counting rice grains on panicles.
To enhance the efficiency and accuracy of rice yield-related research.

Main Methods:

Utilized deep learning, specifically a convolutional neural network (CNN).
Integrated Feature Pyramid Network (FPN) into the Faster R-CNN architecture.
Compared performance against Faster R-CNN and Single Shot Detector (SSD) models.

Main Results:

The proposed model demonstrated superior reliability and accuracy compared to existing methods.
Accuracy remained high (99.4%) regardless of lighting or grain moisture conditions.
The model effectively handled rice branches with varying grain numbers.

Conclusions:

The developed grain detection model is accurate, reliable, and robust.
It is suitable for diverse conditions encountered in rice panicle analysis.
This technology can significantly aid rice breeding and yield research.