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Updated: Aug 28, 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

Research on a rapid identification method for counting universal grain crops.

Jie Zhang¹, Shengping Liu^1,2, Wei Wu¹

¹Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, P.R. China.

|September 14, 2022

Summary

This study introduces a novel Android application for rapid grain counting using an improved algorithm. The tool offers high accuracy and efficiency for diverse crop types, aiding agricultural assessments.

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Area of Science:

Agricultural Science
Computer Vision
Image Processing

Background:

Thousand-grain weight is crucial for crop yield assessment.
Existing image analysis methods lack universality for multiple grain types.
A need exists for efficient and accurate grain counting tools.

Purpose of the Study:

To develop an Android application for fast and accurate grain counting.
To propose a new algorithm suitable for various crop grains.
To provide a low-cost, field-deployable tool for agricultural research.

Main Methods:

Exploration of short axis measurement for grain morphology.
Development of a corrosion algorithm based on the short axis.
Implementation of an improved corner point method for counting.
Testing across diverse crop grains and mobile devices.

Main Results:

The developed algorithm achieves 97.9% average accuracy.
Average calculation time is less than 0.7 seconds.
High universality demonstrated across multiple crop types and mobile phones.
Effective grain counting with white paper background and non-glare conditions.

Conclusions:

The proposed grain counting algorithm is highly universal and accurate.
The Android application offers a fast, low-cost tool for field use.
This method provides a valuable reference for grain counting studies.