Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network
In recent years, the convolution neural network has been the most widely used deep learning algorithm in the field of plant disease diagnosis and has performed well in classification. However, in practice, there are still some specific issues that have not been paid adequate attention to. For instan...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.910878/full |
| _version_ | 1828305433031344128 |
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| author | Huiru Zhou Jie Deng Dingzhou Cai Xuan Lv Bo Ming Wu |
| author_facet | Huiru Zhou Jie Deng Dingzhou Cai Xuan Lv Bo Ming Wu |
| author_sort | Huiru Zhou |
| collection | DOAJ |
| description | In recent years, the convolution neural network has been the most widely used deep learning algorithm in the field of plant disease diagnosis and has performed well in classification. However, in practice, there are still some specific issues that have not been paid adequate attention to. For instance, the same pathogen may cause similar or different symptoms when infecting plant leaves, while the same pathogen may cause similar or disparate symptoms on different parts of the plant. Therefore, questions come up naturally: should the images showing different symptoms of the same disease be in one class or two separate classes in the image database? Also, how will the different classification methods affect the results of image recognition? In this study, taking rice leaf blast and neck blast caused by Magnaporthe oryzae, and rice sheath blight caused by Rhizoctonia solani as examples, three experiments were designed to explore how database configuration affects recognition accuracy in recognizing different symptoms of the same disease on the same plant part, similar symptoms of the same disease on different parts, and different symptoms on different parts. The results suggested that when the symptoms of the same disease were the same or similar, no matter whether they were on the same plant part or not, training combined classes of these images can get better performance than training them separately. When the difference between symptoms was obvious, the classification was relatively easy, and both separate training and combined training could achieve relatively high recognition accuracy. The results also, to a certain extent, indicated that the greater the number of images in the training data set, the higher the average classification accuracy. |
| first_indexed | 2024-04-13T14:28:32Z |
| format | Article |
| id | doaj.art-67945cc2363a4dc4917680f4631f655c |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-13T14:28:32Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-67945cc2363a4dc4917680f4631f655c2022-12-22T02:43:15ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-07-011310.3389/fpls.2022.910878910878Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural NetworkHuiru ZhouJie DengDingzhou CaiXuan LvBo Ming WuIn recent years, the convolution neural network has been the most widely used deep learning algorithm in the field of plant disease diagnosis and has performed well in classification. However, in practice, there are still some specific issues that have not been paid adequate attention to. For instance, the same pathogen may cause similar or different symptoms when infecting plant leaves, while the same pathogen may cause similar or disparate symptoms on different parts of the plant. Therefore, questions come up naturally: should the images showing different symptoms of the same disease be in one class or two separate classes in the image database? Also, how will the different classification methods affect the results of image recognition? In this study, taking rice leaf blast and neck blast caused by Magnaporthe oryzae, and rice sheath blight caused by Rhizoctonia solani as examples, three experiments were designed to explore how database configuration affects recognition accuracy in recognizing different symptoms of the same disease on the same plant part, similar symptoms of the same disease on different parts, and different symptoms on different parts. The results suggested that when the symptoms of the same disease were the same or similar, no matter whether they were on the same plant part or not, training combined classes of these images can get better performance than training them separately. When the difference between symptoms was obvious, the classification was relatively easy, and both separate training and combined training could achieve relatively high recognition accuracy. The results also, to a certain extent, indicated that the greater the number of images in the training data set, the higher the average classification accuracy.https://www.frontiersin.org/articles/10.3389/fpls.2022.910878/fulldeep learningconvolutional neural networkrice diseasesimage recognitioncrop disease datasetmodel fitting |
| spellingShingle | Huiru Zhou Jie Deng Dingzhou Cai Xuan Lv Bo Ming Wu Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network Frontiers in Plant Science deep learning convolutional neural network rice diseases image recognition crop disease dataset model fitting |
| title | Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network |
| title_full | Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network |
| title_fullStr | Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network |
| title_full_unstemmed | Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network |
| title_short | Effects of Image Dataset Configuration on the Accuracy of Rice Disease Recognition Based on Convolution Neural Network |
| title_sort | effects of image dataset configuration on the accuracy of rice disease recognition based on convolution neural network |
| topic | deep learning convolutional neural network rice diseases image recognition crop disease dataset model fitting |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.910878/full |
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