A lightweight tomato leaf disease identification method based on shared‐twin neural networks
Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf di...
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| Published in | IET image processing Vol. 18; no. 9; pp. 2291 - 2303 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Wiley
01.07.2024
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| Abstract | Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small.
A lightweight tomato leaf disease identification method based on shared‐twin neural networks, for rapid detection of tomato diseases |
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| AbstractList | Abstract Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small. Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small. A lightweight tomato leaf disease identification method based on shared‐twin neural networks, for rapid detection of tomato diseases Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small. |
| Author | Yong, Liu Jiayao, Liu Yunsheng, Wang Shipu, Xu Linfeng, Wang |
| Author_xml | – sequence: 1 givenname: Wang surname: Linfeng fullname: Linfeng, Wang organization: Shanghai Academy of Agricultural Sciences – sequence: 2 givenname: Liu surname: Jiayao fullname: Jiayao, Liu organization: Shanghai Academy of Agricultural Sciences – sequence: 3 givenname: Liu surname: Yong fullname: Yong, Liu organization: Shanghai Academy of Agricultural Sciences – sequence: 4 givenname: Wang orcidid: 0000-0002-0701-833X surname: Yunsheng fullname: Yunsheng, Wang email: wanglinfeng60@163.com organization: Shanghai Academy of Agricultural Sciences – sequence: 5 givenname: Xu surname: Shipu fullname: Shipu, Xu email: 1216450124@qq.com organization: Shanghai Academy of Agricultural Sciences |
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| Cites_doi | 10.1109/ACCESS.2020.2982456 10.32604/cmc.2023.041819 10.1016/j.inpa.2020.04.004 10.1109/ICoICT52021.2021.9527425 10.3390/agriculture12020228 10.1109/CVPR.2006.100 10.1016/j.compag.2022.107054 10.3390/electronics11060951 10.1109/ACCESS.2020.3021487 10.1016/j.compag.2020.105730 10.3390/s21237987 10.13031/aea.14507 10.3390/agriculture11070651 10.1016/j.asoc.2022.108969 10.1117/12.2557180 10.1007/s11042-022-11915-2 10.1016/j.procs.2018.07.070 10.1016/j.procs.2020.03.225 10.1109/CVPR.2015.7298594 10.1007/978-981-16-9873-6_54 10.1109/CVPR.2018.00745 10.1016/j.compag.2022.107486 10.3390/plants9101302 10.3389/fpls.2022.846767 10.1109/ACCESS.2021.3069646 10.1146/annurev.phyto.43.113004.133839 10.1016/j.eswa.2022.118117 10.1186/s13007-020-00624-2 10.1016/j.compag.2022.107423 |
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| Title | A lightweight tomato leaf disease identification method based on shared‐twin neural networks |
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