A Lightweight Neural Network-Based Method for Identifying Early-Blight and Late-Blight Leaves of Potato
Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf imag...
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| Published in | Applied sciences Vol. 13; no. 3; p. 1487 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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MDPI AG
01.01.2023
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| Abstract | Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system. |
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| AbstractList | Crop pests and diseases are one of the most critical disasters that limit agricultural production. In this paper, we trained a lightweight convolutional neural network model and built a Django framework-based potato disease leaf recognition system, which can recognize three types of potato leaf images including early blight, late blight, and healthy. A lightweight, neural network-based model for the identification of early potato leaf diseases significantly reduces the number of model parameters, whereas the accuracy of Top-1 identification is over 93%. We imported the trained model into the Django framework to build a website for a potato early leaf disease identification system, thus providing technical support for the implementation of a mobile-based potato leaf disease identification and early warning system. |
| Author | Li, Jia Wang, Fuxiang Wang, Chunguang Kang, Feilong |
| Author_xml | – sequence: 1 givenname: Feilong surname: Kang fullname: Kang, Feilong – sequence: 2 givenname: Jia surname: Li fullname: Li, Jia – sequence: 3 givenname: Chunguang surname: Wang fullname: Wang, Chunguang – sequence: 4 givenname: Fuxiang surname: Wang fullname: Wang, Fuxiang |
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| Cites_doi | 10.1007/s10489-019-01468-7 10.1038/srep27790 10.1007/978-981-15-1002-1_58 10.1016/j.compag.2019.105146 10.1109/ICCV.2019.00140 10.24963/ijcai.2020/694 10.1109/CVPR.2017.243 10.1016/j.compag.2019.105117 10.1145/2911996.2912035 10.1109/ICCV48922.2021.00951 10.1007/s11042-022-12620-w 10.1109/TIP.2018.2821921 10.1080/08839514.2017.1315516 10.1109/TPAMI.2022.3166956 10.3389/fpls.2017.01348 10.1155/2016/3289801 10.1016/j.compag.2018.01.009 10.1609/aaai.v35i3.16347 10.1007/978-3-031-20050-2_22 10.3389/fpls.2016.01419 10.1016/j.compag.2018.03.032 10.3389/fpls.2017.01852 10.1002/fsn3.2415 10.1145/3505244 10.1109/CVPR.2018.00474 10.1109/TGRS.2018.2868851 10.1145/3380688.3380697 10.1371/journal.pone.0123262 |
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| References | Ferentinos (ref_16) 2018; 145 Elsken (ref_34) 2019; 20 Mohanty (ref_20) 2016; 7 Xiaoli (ref_5) 2016; 6 ref_13 ref_35 ref_12 ref_11 ref_33 ref_10 ref_32 ref_31 ref_30 ref_19 Amanda (ref_21) 2017; 8 Yuan (ref_18) 2019; 49 Too (ref_15) 2019; 161 Wang (ref_1) 2021; 9 Khan (ref_28) 2022; 54 Pandey (ref_6) 2017; 8 Chen (ref_3) 2022; 81 Deng (ref_2) 2018; 57 ref_25 ref_24 Sladojevic (ref_22) 2016; 2016 Brahimi (ref_23) 2017; 31 ref_27 ref_26 Deng (ref_29) 2018; 27 ref_8 Nazki (ref_9) 2020; 168 ref_4 Yang (ref_14) 2022; 45 Zhong (ref_17) 2020; 168 ref_7 |
| References_xml | – volume: 49 start-page: 3570 year: 2019 ident: ref_18 article-title: An effective image classification method for shallow densely connected convolution networks through squeezing and splitting techniques publication-title: Appl. Intell. doi: 10.1007/s10489-019-01468-7 – volume: 6 start-page: 27790 year: 2016 ident: ref_5 article-title: Hyperspectral imaging for determining pigment contents in cucumber leaves in response to angular leaf spot disease publication-title: Sci. Rep. doi: 10.1038/srep27790 – ident: ref_7 doi: 10.1007/978-981-15-1002-1_58 – volume: 168 start-page: 105146 year: 2020 ident: ref_17 article-title: Research on deep learning in apple leaf disease recognition publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.105146 – ident: ref_24 – ident: ref_26 doi: 10.1109/ICCV.2019.00140 – ident: ref_11 – ident: ref_31 doi: 10.24963/ijcai.2020/694 – ident: ref_13 doi: 10.1109/CVPR.2017.243 – volume: 168 start-page: 105117 year: 2020 ident: ref_9 article-title: Unsupervised image translation using adversarial networks for improved plant disease recognition publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.105117 – ident: ref_30 doi: 10.1145/2911996.2912035 – ident: ref_32 doi: 10.1109/ICCV48922.2021.00951 – volume: 81 start-page: 20797 year: 2022 ident: ref_3 article-title: Mobile convolution neural network for the recognition of potato leaf disease images publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-022-12620-w – volume: 27 start-page: 3893 year: 2018 ident: ref_29 article-title: Triplet-based deep hashing network for cross-modal retrieval publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2018.2821921 – ident: ref_35 – volume: 31 start-page: 299 year: 2017 ident: ref_23 article-title: Deep Learning for Tomato Diseases: Classification and Symptoms Visualization publication-title: Appl. Artif. Intell. doi: 10.1080/08839514.2017.1315516 – volume: 45 start-page: 2384 year: 2022 ident: ref_14 article-title: Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2022.3166956 – volume: 8 start-page: 1348 year: 2017 ident: ref_6 article-title: High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging publication-title: Front. Plant Sci. doi: 10.3389/fpls.2017.01348 – volume: 2016 start-page: 3289801 year: 2016 ident: ref_22 article-title: Deep neural networks based recognition of plant diseases by leaf image classification publication-title: Comput. Intell. Neurosci. doi: 10.1155/2016/3289801 – volume: 145 start-page: 311 year: 2018 ident: ref_16 article-title: Deep learning models for plant disease detection and diagnosis publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.01.009 – ident: ref_10 doi: 10.1609/aaai.v35i3.16347 – volume: 20 start-page: 1997 year: 2019 ident: ref_34 article-title: Neural architecture search: A survey publication-title: J. Mach. Learn. Res. – ident: ref_33 doi: 10.1007/978-3-031-20050-2_22 – volume: 7 start-page: 1419 year: 2016 ident: ref_20 article-title: Using deep learning for image-based plant disease detection publication-title: Front. Plant Sci. doi: 10.3389/fpls.2016.01419 – ident: ref_27 – ident: ref_12 – volume: 161 start-page: 272 year: 2019 ident: ref_15 article-title: A comparative study of fine-tuning deep learning models for plant disease identification publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.03.032 – volume: 8 start-page: 1852 year: 2017 ident: ref_21 article-title: Deep learning for image-based cassava disease detection publication-title: Front. Plant Sci. doi: 10.3389/fpls.2017.01852 – volume: 9 start-page: 4420 year: 2021 ident: ref_1 article-title: Study on starch content detection and visualization of potato based on hyperspectral imaging publication-title: Food Sci. Nutr. doi: 10.1002/fsn3.2415 – volume: 54 start-page: 1 year: 2022 ident: ref_28 article-title: Transformers in vision: A survey publication-title: ACM Comput. Surv. (CSUR) doi: 10.1145/3505244 – ident: ref_19 – ident: ref_25 doi: 10.1109/CVPR.2018.00474 – volume: 57 start-page: 1741 year: 2018 ident: ref_2 article-title: Active transfer learning network: A unified deep joint spectral–spatial feature learning model for hyperspectral image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2018.2868851 – ident: ref_8 doi: 10.1145/3380688.3380697 – ident: ref_4 doi: 10.1371/journal.pone.0123262 |
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| SubjectTerms | Agricultural production Agriculture Artificial intelligence Classification convolutional neural networks Crop diseases Crops Deep learning Django framework machine learning Neural networks Pesticides potato disease leaf |
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| Title | A Lightweight Neural Network-Based Method for Identifying Early-Blight and Late-Blight Leaves of Potato |
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