Identification of Soybean Leaf Diseases via Deep Learning (Record no. 11380)
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| 000 -LEADER | |
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250408123023.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200224b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 12428 |
| Author | Wu, Q. |
| 245 ## - TITLE STATEMENT | |
| Title | Identification of Soybean Leaf Diseases via Deep Learning |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.100(4), Dec |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New York |
| Name of publisher, distributor, etc. | Springer |
| Year | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 659-666p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | We propose a novel approach for identifying soybean leaf diseases in the natural environment by convolutional neural network (CNN). AlexNet, GoogLeNet and ResNet were utilized for transfer learning. Firstly, 27 models were obtained by setting different batch sizes and the number of iterations. Then, the effects of CNN structure on identification performance were explored. The optimal model is based on ResNet and has the highest accuracy of 94.29%. In the parameter settings of the optimal network, the number of iterations and batch size are 1056 and 16, respectively, and the training depth is 140. Overall, the proposed method is effective for identifying soybean leaf diseases in the natural environment. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4690 |
| Topical term or geographic name entry element | Construction Engineering and Management (ME-CE) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 12429 |
| Co-Author | Zhang, K. |
| 773 0# - HOST ITEM ENTRY | |
| Title | Journal of the institution of engineers (India): Series A |
| International Standard Serial Number | 2250-2149 |
| Place, publisher, and date of publication | Switzerland Springer |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://link.springer.com/article/10.1007/s40030-019-00390-y |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 24/02/2020 | 2021054 | 24/02/2020 | 24/02/2020 | Articles Abstract Database |