| 000 -LEADER |
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| fixed length control field |
nam a22 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220927143629.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220927b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
18139 |
| Author |
Seetharaman, K. |
| 245 ## - TITLE STATEMENT |
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| Title |
Leaf disease detection in banana plant using gabor extraction and region-based convolution neural network (RCNN) |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.103(2), June |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
New York |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2022 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
501-508p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Disease identification in bananas has proven to be more difficult in the field due to the fact that it is susceptible to a variety of diseases and causes significant losses to farmers. As a result, this research provides improved image processing algorithms for earlier disease identification in banana leaves. The images are preprocessed using a histogram pixel localization technique with a median filter and the segmentation is done through a region-based edge normalization. Here a novel integrated system is formulated for feature extraction using Gabor-based binary patterns with convolution recurrent neural network. Finally, a region-based convolution neural network is used to identify the disease area by extracting and classifying features in order to increase disease diagnostic accuracy. The proposed Convolutional Recurrent Neural Network–Region-Based Convolutional Neural Network (CRNN–RCNN) classifier provides a precision score of 97.7%, a recall score of 97.7%, and a sensitivity score of 98.69% when evaluated in a dataset with complex image backgrounds. For the banana dataset, the proposed CRNN–RCNN model achieves an accuracy of 98%, which is greater than the accuracy obtained by CNN (87.6%), DCNN (88.9%), KNN (79.56%), and SVM (92.63%). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
18140 |
| Co-Author |
Mahendran, T. |
| 773 0# - HOST ITEM ENTRY |
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| 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 |
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| URL |
https://link.springer.com/article/10.1007/s40030-022-00628-2 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
