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Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

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A quality detection method of corn based on spectral technology and deep learning model.

Jiao Yang¹, Xiaodan Ma¹, Haiou Guan²

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

Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy

|October 3, 2023

Summary

This study introduces a novel deep learning model for accurate corn quality detection using near-infrared spectroscopy. The advanced method significantly improves detection accuracy compared to traditional techniques.

Keywords:

Corn Deep learning Detection model Feature extraction Near-infrared spectroscopy Wavelet transform

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Area of Science:

Agricultural Science
Spectroscopy
Machine Learning

Background:

Corn quality is crucial for global food security, breeding, and cultivation.
Current corn quality detection methods are often cumbersome, slow, and inaccurate.

Purpose of the Study:

To develop an efficient and accurate corn quality detection model.
To integrate near-infrared (NIR) spectroscopy with deep learning for enhanced analysis.

Main Methods:

Utilized near-infrared (NIR) spectroscopy combined with a deep learning convolutional neural network (LeNet-5).
Applied wavelet transform (WT) and multivariate scattering correction (MSC) for spectral preprocessing.
Employed the Competitive Adaptive Reweighted Sampling Algorithm (CARS) for feature selection.

Main Results:

Achieved an average detection accuracy of 96.46% on the test set.
Demonstrated superior performance over traditional machine learning models, with an average accuracy increase of 39.32%.

Conclusions:

The proposed LeNet-5 deep learning model offers a highly accurate and efficient solution for corn quality detection.
This approach overcomes limitations of traditional methods, paving the way for improved agricultural practices.