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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.19628 |
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| _version_ | 1866913370077134848 |
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| author | Adsavakulchai, Suwannee Prommasaeng, Mawin |
| author_facet | Adsavakulchai, Suwannee Prommasaeng, Mawin |
| contents | This research aims to detect the physical characteristics of corn kernels and analyze images using a deep learning model. The data analysis based on the CRISP-DM framework which consists of six steps, business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The business goal reduces the cost of the separation of abnormal corn kernels. The dataset comprises 1,800 images of corn kernels and divided equally between normal and abnormal corn kernels. The dataset was divided into three subsets: 1,000 images for training the deep learning model, 600 images for validation and 200 images for testing. The tools for analysis in this research are Jupyter Lab, Python, TensorFlow Keras, and Convolutional Neural Networks. The results revealed that the deep learning model achieved the accuracy rate of 99% in differentiating between normal and abnormal corn kernel images that is a highly effective model in this context. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_19628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Model for Detecting Abnormal Corn Kernels Adsavakulchai, Suwannee Prommasaeng, Mawin Computational Engineering, Finance, and Science This research aims to detect the physical characteristics of corn kernels and analyze images using a deep learning model. The data analysis based on the CRISP-DM framework which consists of six steps, business understanding, data understanding, data preparation, modelling, evaluation, and deployment. The business goal reduces the cost of the separation of abnormal corn kernels. The dataset comprises 1,800 images of corn kernels and divided equally between normal and abnormal corn kernels. The dataset was divided into three subsets: 1,000 images for training the deep learning model, 600 images for validation and 200 images for testing. The tools for analysis in this research are Jupyter Lab, Python, TensorFlow Keras, and Convolutional Neural Networks. The results revealed that the deep learning model achieved the accuracy rate of 99% in differentiating between normal and abnormal corn kernel images that is a highly effective model in this context. |
| title | Deep Learning Model for Detecting Abnormal Corn Kernels |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2405.19628 |