Advanced image processing algorithms for categorizing and evaluating plant diseases: a study (Record no. 17492)
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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 | 20220912095422.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220912b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17847 |
| Author | Mahalaxmi, G. |
| 245 ## - TITLE STATEMENT | |
| Title | Advanced image processing algorithms for categorizing and evaluating plant diseases: a study |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol14(1), Feb |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Hyderabad |
| Name of publisher, distributor, etc. | IUP Publications |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 35-49p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The paper studies the approaches to detecting, evaluating and categorizing plant diseases from digital images in the visible spectrum using appropriate processing techniques. Despite the fact that disease symptoms might appear anywhere on the plant, only approaches that looked at obvious symptoms in leaves and stems were examined. This was designed for various reasons: to keep the report short and because methods dealing with roots, seeds, and fruits have some unique characteristics that would necessitate a separate survey. The concepts chosen are organized into three categories based on their goal: detection, severity quantification and categorization. Each classification is further categorized based on the algorithm's primary technical solution. The paper also examines and contrasts the benefits and drawbacks of different prospective strategies. Image acquisition, image preprocessing, feature extraction and neural network-based categorization are a few of the techniques included. Researchers working on both vegetable pathology and pattern recognition can benefit from this study, which provides a detailed and accessible summary of this vital field of research. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4619 |
| Topical term or geographic name entry element | EXTC Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 17848 |
| Co-Author | Tirupal, T. |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0975-5551 |
| Place, publisher, and date of publication | Hyderabad IUP Publications |
| Title | IUP Journal of telecommunications |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://iupindia.in/0222/Telecommunications/Advanced_Image.asp |
| 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 | 12/09/2022 | 2022-1560 | 12/09/2022 | 12/09/2022 | Articles Abstract Database |