Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection pat...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/5/2166 |
| _version_ | 1827319801210470400 |
|---|---|
| author | Yongzhen Zhang Yanbo Hui Ying Zhou Juanjuan Liu Ju Gao Xiaoliang Wang Baiwei Wang Mengqi Xie Haonan Hou |
| author_facet | Yongzhen Zhang Yanbo Hui Ying Zhou Juanjuan Liu Ju Gao Xiaoliang Wang Baiwei Wang Mengqi Xie Haonan Hou |
| author_sort | Yongzhen Zhang |
| collection | DOAJ |
| description | Moldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment. |
| first_indexed | 2024-04-25T00:34:47Z |
| format | Article |
| id | doaj.art-cd9cb871bffe41e7b16840948d38b2cf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-25T00:34:47Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cd9cb871bffe41e7b16840948d38b2cf2024-03-12T16:40:21ZengMDPI AGApplied Sciences2076-34172024-03-01145216610.3390/app14052166Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep LearningYongzhen Zhang0Yanbo Hui1Ying Zhou2Juanjuan Liu3Ju Gao4Xiaoliang Wang5Baiwei Wang6Mengqi Xie7Haonan Hou8College of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaCollege of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, ChinaSchool of International Education, Xuchang University, Xuchang 461000, ChinaMoldy corn produces aflatoxin and gibberellin, which can have adverse effects on human health if consumed. Mold is a significant factor that affects the safe storage of corn. If not detected and controlled in a timely manner, it will result in substantial food losses. Understanding the infection patterns of mold on corn kernels and the changing characteristics of the internal structure of corn kernels after infection is crucial for guiding innovation and optimizing detection methods for moldy corn. This knowledge also helps maintain corn storage and ensure food safety. This study was based on X-ray tomography technology to non-destructively detect changes in the structural characteristics of moldy corn kernels. It used image processing technology and model reconstruction algorithms to obtain the 3D model of the embryo, pores and cracks, endosperm and seed coat, and kernels of moldy corn kernels; qualitative analysis of the characteristic changes of two-dimensional slice grayscale images and 3D models of moldy corn kernels; and quantitative analysis of changes in the volume parameters of corn kernels, embryos, endosperm, and seed coats as a whole. It explored the detection method of moldy corn kernels based on a combination of X-ray tomography technology and deep learning algorithms. The analysis concluded that mold infection in maize begins in the embryo and gradually spreads and that mold damage to the tissue structure of maize kernels is irregular in nature. The overall volume parameter changes of corn kernels, embryos, endosperm, and seed coats in the four stages of 0 d, 5 d, 10 d, and 15 d showed a trend of first increasing and then decreasing. The ResNet50 model was enhanced for detecting mold on maize kernels, achieving an accuracy of over 93% in identifying mold features in sliced images of maize kernels. This advancement enabled the non-destructive detection and classification of the degree of mold in maize kernel samples. This article studies the characterization of the characteristic changes of moldy corn kernels and the detection of mildew, which will provide certain help for optimizing the monitoring of corn kernel mildew and the development of rapid detection equipment.https://www.mdpi.com/2076-3417/14/5/2166X-ray tomography technologyResNet50image processingmodel reconstructionmold |
| spellingShingle | Yongzhen Zhang Yanbo Hui Ying Zhou Juanjuan Liu Ju Gao Xiaoliang Wang Baiwei Wang Mengqi Xie Haonan Hou Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning Applied Sciences X-ray tomography technology ResNet50 image processing model reconstruction mold |
| title | Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning |
| title_full | Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning |
| title_fullStr | Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning |
| title_full_unstemmed | Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning |
| title_short | Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning |
| title_sort | characterization and detection classification of moldy corn kernels based on x ct and deep learning |
| topic | X-ray tomography technology ResNet50 image processing model reconstruction mold |
| url | https://www.mdpi.com/2076-3417/14/5/2166 |
| work_keys_str_mv | AT yongzhenzhang characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT yanbohui characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT yingzhou characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT juanjuanliu characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT jugao characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT xiaoliangwang characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT baiweiwang characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT mengqixie characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning AT haonanhou characterizationanddetectionclassificationofmoldycornkernelsbasedonxctanddeeplearning |