Detection of Strawberry Diseases Using a Convolutional Neural Network
The strawberry (<i>Fragaria</i> × <i>ananassa</i> Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/10/1/31 |
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| author | Jia-Rong Xiao Pei-Che Chung Hung-Yi Wu Quoc-Hung Phan Jer-Liang Andrew Yeh Max Ti-Kuang Hou |
| author_facet | Jia-Rong Xiao Pei-Che Chung Hung-Yi Wu Quoc-Hung Phan Jer-Liang Andrew Yeh Max Ti-Kuang Hou |
| author_sort | Jia-Rong Xiao |
| collection | DOAJ |
| description | The strawberry (<i>Fragaria</i> × <i>ananassa</i> Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases. |
| first_indexed | 2024-03-10T13:46:45Z |
| format | Article |
| id | doaj.art-b60b51d126de4ac385527cdf571f5e20 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-10T13:46:45Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-b60b51d126de4ac385527cdf571f5e202023-11-21T02:31:18ZengMDPI AGPlants2223-77472020-12-011013110.3390/plants10010031Detection of Strawberry Diseases Using a Convolutional Neural NetworkJia-Rong Xiao0Pei-Che Chung1Hung-Yi Wu2Quoc-Hung Phan3Jer-Liang Andrew Yeh4Max Ti-Kuang Hou5Department of Mechanical Engineering, National United University, Miaoli 360001, TaiwanMiaoli District Agricultural Research and Extension Station, Miaoli 363201, TaiwanDepartment of Plant Pathology and Microbiology, National Taiwan University, Taipei 106319, TaiwanDepartment of Mechanical Engineering, National United University, Miaoli 360001, TaiwanDepartment of Power Mechanical Engineering, National Tsing Hwa University, Hsinchu 300044, TaiwanDepartment of Mechanical Engineering, National United University, Miaoli 360001, TaiwanThe strawberry (<i>Fragaria</i> × <i>ananassa</i> Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.https://www.mdpi.com/2223-7747/10/1/31strawberry diseasesconvolution neural networkimage recognition |
| spellingShingle | Jia-Rong Xiao Pei-Che Chung Hung-Yi Wu Quoc-Hung Phan Jer-Liang Andrew Yeh Max Ti-Kuang Hou Detection of Strawberry Diseases Using a Convolutional Neural Network Plants strawberry diseases convolution neural network image recognition |
| title | Detection of Strawberry Diseases Using a Convolutional Neural Network |
| title_full | Detection of Strawberry Diseases Using a Convolutional Neural Network |
| title_fullStr | Detection of Strawberry Diseases Using a Convolutional Neural Network |
| title_full_unstemmed | Detection of Strawberry Diseases Using a Convolutional Neural Network |
| title_short | Detection of Strawberry Diseases Using a Convolutional Neural Network |
| title_sort | detection of strawberry diseases using a convolutional neural network |
| topic | strawberry diseases convolution neural network image recognition |
| url | https://www.mdpi.com/2223-7747/10/1/31 |
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