A rapid, low-cost deep learning system to classify strawberry disease based on cloud service
Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments,...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-02-01
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| Series: | Journal of Integrative Agriculture |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095311921636043 |
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| author | Guo-feng YANG Yong YANG Zi-kang HE Xin-yu ZHANG Yong HE |
| author_facet | Guo-feng YANG Yong YANG Zi-kang HE Xin-yu ZHANG Yong HE |
| author_sort | Guo-feng YANG |
| collection | DOAJ |
| description | Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloud-based strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect. Currently, the client (mini program) has been released on the WeChat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification. |
| format | Article |
| id | doaj-art-ff67c43aa9a14711b58e00b9a60a2ddb |
| institution | Matheson Library |
| issn | 2095-3119 |
| language | English |
| publishDate | 2022-02-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Integrative Agriculture |
| spelling | doaj-art-ff67c43aa9a14711b58e00b9a60a2ddb2025-08-03T00:33:05ZengKeAi Communications Co., Ltd.Journal of Integrative Agriculture2095-31192022-02-01212460473A rapid, low-cost deep learning system to classify strawberry disease based on cloud serviceGuo-feng YANG0Yong YANG1Zi-kang HE2Xin-yu ZHANG3Yong HE4Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China; Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China; Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China; Correspondence YANG YongAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China; Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.ChinaAgricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China; Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R.ChinaAccurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloud-based strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect. Currently, the client (mini program) has been released on the WeChat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.http://www.sciencedirect.com/science/article/pii/S2095311921636043deep learningstrawberry diseaseimage classificationmini programcloud service |
| spellingShingle | Guo-feng YANG Yong YANG Zi-kang HE Xin-yu ZHANG Yong HE A rapid, low-cost deep learning system to classify strawberry disease based on cloud service Journal of Integrative Agriculture deep learning strawberry disease image classification mini program cloud service |
| title | A rapid, low-cost deep learning system to classify strawberry disease based on cloud service |
| title_full | A rapid, low-cost deep learning system to classify strawberry disease based on cloud service |
| title_fullStr | A rapid, low-cost deep learning system to classify strawberry disease based on cloud service |
| title_full_unstemmed | A rapid, low-cost deep learning system to classify strawberry disease based on cloud service |
| title_short | A rapid, low-cost deep learning system to classify strawberry disease based on cloud service |
| title_sort | rapid low cost deep learning system to classify strawberry disease based on cloud service |
| topic | deep learning strawberry disease image classification mini program cloud service |
| url | http://www.sciencedirect.com/science/article/pii/S2095311921636043 |
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