Validation of Leaf Area Index of Maize for Graded Levels of Fertilizers Using Conventional and Artificial Intelligence Techniques (Record no. 70785)

MARC details
000 -LEADER
fixed length control field 02336nam a2200217 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250203130222.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250203b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency UAS Dharwad
041 ## - LANGUAGE CODE
Language code English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 630
Author Label BIN
100 ## - MAIN ENTRY--PERSONAL NAME
Name of Author Bindu M.
245 ## - TITLE STATEMENT
Title Validation of Leaf Area Index of Maize for Graded Levels of Fertilizers Using Conventional and Artificial Intelligence Techniques
250 ## - EDITION STATEMENT
Edition Statement M.Sc. (Agri)
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of Publisher Dharwad
Name of Publisher University of Agricultural Sciences
Publication Year 2024
300 ## - PHYSICAL DESCRIPTION
Book Pages 176
Book Size 32 Cms
520 ## - SUMMARY, ETC.
Abstract. ABSTRACT<br/><br/> A field experiment was conducted at MARS, Dharwad during kharif 2023 on medium black soil for validation of leaf area index of maize for graded levels of fertilizers using conventional and artificial intelligence techniques. The experiment was laid out in split plot design comprising with three fertilizer levels 50, 100 (100: 50: 25 kg N: P2O5: K2O ha-1) and 150 per cent RDF as main plot and five methods of estimation of leaf area index (LAI) of maize [length × breadth method, disc method, leaf area meter, canopy analyzer and artificial intelligence (AI)] as sub plot and control (Without fertilizers).<br/>Application of 150 per cent RDF recorded significantly higher grain (75.64 q ha-1) and stover yield (96.18 q ha-1) of maize than 50 per cent RDF (38.69 q ha-1 and 55.71 q ha-1, respectively) and it was on par with 100 per cent RDF (72.77 q ha-1 and 94.64 q ha-1, respectively).<br/>Among the subplots there was no significant differences in grain and stover yield of maize. Among interactions, 150 per cent RDF + LAI estimation by AI method showed significantly higher grain yield (75.70 q ha-1) and stover yield (96.26 q ha-1) than control.<br/>Among the different methods of LAI estimation, AI method showed least deviation (1.02-14.77 %) particularly at grain filling (1.02 %) followed by silking stage (2.9 %) and maximum deviation (46.1-58.0 %) was observed with disc method at all the growth stages.<br/>Among machine learning models, random forest model outperformed other models with R² (0.67-0.94) and RMSE (0.02-0.26) at all the growth stages (Knee-high stage, tasseling stage, siliking stage and grain filling stage) compared to other models.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Agronomy
700 ## - ADDED ENTRY--PERSONAL NAME
2nd Author, 3rd Author Potdar M. P.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha Item type THESIS
Edition M.Sc. (Agri)
Classification part 630
Call number prefix BIN
Suppress in OPAC No
942 ## - ADDED ENTRY ELEMENTS (KOHA)
-- 630_000000000000000
999 ## -
-- 70785
-- 70785
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
    Dewey Decimal Classification     University of Agricultural Sciences, Dharwad University of Agricultural Sciences, Dharwad 29/10/2024   630/BIN T14019 03/02/2025 1 03/02/2025 THESIS