Deep learning model for improving the rice plant disease detection performance (Record no. 19043)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230327094105.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230327b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20274 |
| Author | Shrivastava, Gaurav |
| 245 ## - TITLE STATEMENT | |
| Title | Deep learning model for improving the rice plant disease detection performance |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.13(1), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 2775-2781p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Rice is one of the most utilized grains in India. It is a seasonal crop<br/>which mostly grows between June to October. This crop mostly grows<br/>in natural conditions and its production has a significant influence on<br/>different diseases in the plant. Early stage detection of diseases can help<br/>in improving the production. In this paper, an analysis and study on<br/>deep learning models for getting accurate rice plant disease detection<br/>is presented. In this context, first the recent contributions on detecting<br/>the diseases by analysing the plant leaf images are reviewed. Then, a<br/>comparison among sequential model and 2D-CNN model has been<br/>performed. The experimental analysis demonstrates that 2D-CNN<br/>outperforms as compared to the simple sequential model. The<br/>experiments are extended by including the different image feature<br/>selection models. In order to extract features, sobel based edge<br/>detection, Local Binary Pattern (LBP) based texture analysis and their<br/>combinations i.e. sobel and LBP, Sobel, LBP and color, and a<br/>combination of color and sobel are used. The experiments are<br/>performed on Kaggle based rice plant disease detection dataset and the<br/>performance in terms of precision, recall, f1-score and accuracy has<br/>been measured. The experimental evaluation highlights two major<br/>points (1) the CNN does not require additional features for better<br/>classification consequences (2) the highly trained models are able to<br/>respond faster as compared to less trained models. Based on the<br/>obtained performance, a more accurate model for plant disease<br/>detection is designed. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20275 |
| Co-Author | Barua, Kuntal |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_13_Iss_1_Paper_5_2775_2781.pdf |
| 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 | School of Engineering & Technology | Archieval Section | 27/03/2023 | 2023-0514 | 27/03/2023 | 27/03/2023 | Articles Abstract Database |