Light gradient boosting machine for optimizing crop maintenance and yield prediction in Agricultur (Record no. 22704)
[ 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 | 20250424103931.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250424b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25451 |
| Author | Kumar, Sunil |
| 245 ## - TITLE STATEMENT | |
| Title | Light gradient boosting machine for optimizing crop maintenance and yield prediction in Agricultur |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.15(2), Oct |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3551-3555p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | In agriculture, optimizing crop yield and maintenance practices is<br/>essential for ensuring food security and sustainable farming.<br/>Traditional approaches often lack the efficiency needed to process<br/>large agricultural datasets and accurately predict yield under varying<br/>environmental conditions. This project leverages the Light Gradient<br/>Boosting Machine (LightGBM), a high-performance, gradient-<br/>boosting framework specifically designed for large-scale data<br/>handling, to address the challenge of yield prediction and crop<br/>maintenance optimization. By integrating LightGBM, which handles<br/>heterogeneous data with high accuracy, we aim to enhance predictions<br/>on crop yield while minimizing resource use. The proposed method<br/>analyzes a range of factors, including soil quality, weather conditions,<br/>irrigation practices, and historical crop yield records. Initial results<br/>indicate that LightGBM outperforms conventional models with a<br/>94.7% accuracy rate in yield prediction and reduces maintenance costs<br/>by up to 20% by recommending optimized agricultural practices based<br/>on specific environmental conditions. These findings underscore the<br/>potential of LightGBM as an effective tool in precision agriculture,<br/>ultimately aiding farmers in making informed decisions and improving<br/>agricultural productivity. |
| 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) | 25968 |
| Co-Author | Mohammed Ali Sohail |
| 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_15_Iss_2_Paper_11_3551_3555.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 | 24/04/2025 | 2025-0653 | 24/04/2025 | 24/04/2025 | Articles Abstract Database |