Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model
Mildew in maize kernels is typically caused by various fungi, necessitating prompt detection and treatment to minimize losses during harvest and storage. In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection...
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MDPI AG
2024-11-01
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| author | Yu Xia Ao Shen Tianci Che Wenbo Liu Jie Kang Wei Tang |
| author_facet | Yu Xia Ao Shen Tianci Che Wenbo Liu Jie Kang Wei Tang |
| author_sort | Yu Xia |
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| description | Mildew in maize kernels is typically caused by various fungi, necessitating prompt detection and treatment to minimize losses during harvest and storage. In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection model and to enhance its portability for deployment on additional mobile devices. To guarantee the fruitful progression of this research, an initial experiment was conducted on maize seeds to obtain a sufficient number of images of mildewed maize kernels, which were classified into three grades (sound, mild, and severe). Subsequently, a maize seed image was extracted to create an image of a single maize seed, which was then divided to establish the data set. An enhanced YOLOv5s–ShuffleNet–CBAM model was ultimately developed. The results demonstrated that the model achieved with an <i>mAP50</i> value of 0.955 and a model size of 2.4 MB. This resulted in a notable reduction in the model parameters and calculation amount while simultaneously enhancing model precision. Furthermore, K-fold cross-validation demonstrated the model stability, and Grad-CAM validated the model effectiveness. In the future, the proposed lightweight model in this study can be applied to other crops in the context of portable or online inspection systems, thus advancing effective and high-quality agricultural applications. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-aadb3c29c56f47ab8a52d9fe86ad25862025-08-20T02:26:45ZengMDPI AGApplied Sciences2076-34172024-11-0114221048910.3390/app142210489Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning ModelYu Xia0Ao Shen1Tianci Che2Wenbo Liu3Jie Kang4Wei Tang5School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaSchool of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi’an 710021, ChinaMildew in maize kernels is typically caused by various fungi, necessitating prompt detection and treatment to minimize losses during harvest and storage. In this study, a deep learning YOLOv5s algorithm based on machine vision technology was employed to develop a maize seed surface mildew detection model and to enhance its portability for deployment on additional mobile devices. To guarantee the fruitful progression of this research, an initial experiment was conducted on maize seeds to obtain a sufficient number of images of mildewed maize kernels, which were classified into three grades (sound, mild, and severe). Subsequently, a maize seed image was extracted to create an image of a single maize seed, which was then divided to establish the data set. An enhanced YOLOv5s–ShuffleNet–CBAM model was ultimately developed. The results demonstrated that the model achieved with an <i>mAP50</i> value of 0.955 and a model size of 2.4 MB. This resulted in a notable reduction in the model parameters and calculation amount while simultaneously enhancing model precision. Furthermore, K-fold cross-validation demonstrated the model stability, and Grad-CAM validated the model effectiveness. In the future, the proposed lightweight model in this study can be applied to other crops in the context of portable or online inspection systems, thus advancing effective and high-quality agricultural applications.https://www.mdpi.com/2076-3417/14/22/10489deep learningmachine visionYOLOv5smaize seedmildew detection |
| spellingShingle | Yu Xia Ao Shen Tianci Che Wenbo Liu Jie Kang Wei Tang Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model Applied Sciences deep learning machine vision YOLOv5s maize seed mildew detection |
| title | Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model |
| title_full | Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model |
| title_fullStr | Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model |
| title_full_unstemmed | Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model |
| title_short | Early Detection of Surface Mildew in Maize Kernels Using Machine Vision Coupled with Improved YOLOv5 Deep Learning Model |
| title_sort | early detection of surface mildew in maize kernels using machine vision coupled with improved yolov5 deep learning model |
| topic | deep learning machine vision YOLOv5s maize seed mildew detection |
| url | https://www.mdpi.com/2076-3417/14/22/10489 |
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