000 -LEADER |
fixed length control field |
a |
003 - CONTROL NUMBER IDENTIFIER |
control field |
OSt |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20220928150036.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
220928b xxu||||| |||| 00| 0 eng d |
040 ## - CATALOGING SOURCE |
Original cataloging agency |
AIKTC-KRRC |
Transcribing agency |
AIKTC-KRRC |
100 ## - MAIN ENTRY--PERSONAL NAME |
9 (RLIN) |
18165 |
Author |
Tian, Xiaole |
245 ## - TITLE STATEMENT |
Title |
Identification of tomato leaf diseases based on a deep neuro-fuzzy network |
250 ## - EDITION STATEMENT |
Volume, Issue number |
Vol.103(2), June |
260 ## - PUBLICATION, DISTRIBUTION, ETC. |
Place of publication, distribution, etc. |
New York |
Name of publisher, distributor, etc. |
Springer |
Year |
2022 |
300 ## - PHYSICAL DESCRIPTION |
Pagination |
695-706p. |
520 ## - SUMMARY, ETC. |
Summary, etc. |
The emergence and spread of diseases can reduce the yield of tomato crops, resulting in lower income for farmers. Accurate identification of tomato leaf diseases is an urgent matter for control and treatment. The recognition accuracy has improved with the advancement of deep learning. But because of uncertainty and ambiguity of information, the fuzzy rules, which can describe and process the fuzzy information, are incorporated into deep learning to increase the identification accuracy. In this paper, we adopt a deep neuro-fuzzy neural network to classify tomato leaf diseases. To extract complex features, we adopt the fuzzy inference layer and fuzzy pooling layer in the neuro-fuzzy network. And then input these into the fully connected layer for classification. Based on a big dataset containing 8 kinds of infected and uninfected tomato leaf images, the applied model achieved recognition accuracy of 94.19%. And three evaluation indexes were used to measure the performance. The experimental results prove the advantage of the deep neuro-fuzzy neural network in tomato diseases. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
9 (RLIN) |
4642 |
Topical term or geographic name entry element |
Humanities and Applied Sciences |
700 ## - ADDED ENTRY--PERSONAL NAME |
9 (RLIN) |
18166 |
Co-Author |
Meng, Xiangyan |
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/s40030-022-00642-4 |
Link text |
Click here |
942 ## - ADDED ENTRY ELEMENTS (KOHA) |
Source of classification or shelving scheme |
|
Koha item type |
Articles Abstract Database |