Hybrid deep learning with alexnet feature extraction and unet classification for early detection in leaf disease (Record no. 22746)
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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 | 20250429110531.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250429b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 26035 |
| Author | Chakraborty, Uddyalok |
| 245 ## - TITLE STATEMENT | |
| Title | Hybrid deep learning with alexnet feature extraction and unet classification for early detection in leaf disease |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(3), Jan |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3255-3262p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This study addresses the imperative need for early detection of leaf<br/>diseases in tobacco, pepper, and tomato plants, as these diseases<br/>significantly impact crop yield and quality. Existing methods often fall<br/>short in accurately identifying diseases across diverse plant species.<br/>The research aims to bridge this gap by proposing a hybrid deep<br/>learning approach, combining the robust feature extraction<br/>capabilities of AlexNet with the precise segmentation and classification<br/>prowess of UNet. The proposed hybrid model leverages AlexNet<br/>proficiency in extracting hierarchical features from plant leaf images<br/>and subsequently utilizes UNet for accurate disease classification. This<br/>synergistic combination enables the model to overcome the challenges<br/>posed by the varied morphologies of tobacco, pepper, and tomato<br/>leaves. Experimental results demonstrate the effectiveness of the<br/>proposed methodology, showcasing superior performance in terms of<br/>accuracy, sensitivity, and specificity compared to existing techniques.<br/>The hybrid deep learning approach exhibits promising potential for<br/>early and accurate detection of leaf diseases, contributing to<br/>sustainable crop management practices. |
| 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) | 26036 |
| Co-Author | Thilagavathy, D. |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | Chennai ICT Academy |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_14_Iss_3_Paper_4_3255_3262.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 | 29/04/2025 | 2025-0725 | 29/04/2025 | 29/04/2025 | Articles Abstract Database |