A Method for Segmenting Grain Particles in Hyperspectral Images

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2025-04-01 DOI:10.1109/JSEN.2025.3554236

Zhen Yang;Yuhang Niu;Tingting He;Huawei Jiang;Like Zhao

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引用次数: 0

Abstract

Hyperspectral imaging, known for its high spectral resolution and nondestructive detection characteristics, has been widely applied in grain quality evaluation. Grain quality evaluation utilizing near-infrared hyperspectral images often uses individual particles as the evaluation unit and thus relies on the segmentation of particles. However, widely applicable methods for the segmentation of grain particles remain inadequate because of the challenge of segmenting adhesive particles. Therefore, this study introduces a new method designed to significantly improve the accuracy of segmenting grain particles. This method considers the shape characteristics of grains to correct the oversegmentation in watershed segmentation caused by adhesive particles. The feasibility of the method was tested by applying it to three grains: corn, wheat, and peanuts. Results demonstrated that the new method performed significantly better than watershed segmentation, with accuracies of no less than 90% for the three grains. The new method has the potential to support various studies related to grain quality evaluation, such as mold identification and moisture detection of individual particles.

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一种高光谱图像中颗粒的分割方法

高光谱成像以其高光谱分辨率和无损检测的特点，在粮食质量评价中得到了广泛的应用。利用近红外高光谱图像进行粮食质量评价，往往以单个颗粒为评价单位，依赖于对颗粒的分割。然而，由于胶粘剂颗粒分割的挑战，广泛应用的颗粒分割方法仍然不足。因此，本研究引入了一种新的方法，旨在显著提高颗粒分割的精度。该方法考虑了颗粒的形状特征，对粘连颗粒在分水岭分割中造成的过分割进行了校正。通过将该方法应用于玉米、小麦和花生三种谷物，测试了该方法的可行性。结果表明，新方法的分割效果明显优于流域分割，三种谷物的分割准确率不低于90%。这种新方法有可能支持与粮食质量评估有关的各种研究，例如单个颗粒的霉菌鉴定和水分检测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

期刊介绍： The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice