A Data-Driven Approach to Predict Crop Yield Using Ai Tools (Record no. 70799)

MARC details
000 -LEADER
fixed length control field 02705nam a2200217 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250207100625.0
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fixed length control field 250207b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency UAS Dharwad
041 ## - LANGUAGE CODE
Language code English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 630.2
Author Label AKH
100 ## - MAIN ENTRY--PERSONAL NAME
Name of Author Akhila P. S.
245 ## - TITLE STATEMENT
Title A Data-Driven Approach to Predict Crop Yield Using Ai Tools
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 84
Book Size 32 Cms
520 ## - SUMMARY, ETC.
Abstract. ABSTRACT<br/><br/> Agriculture is a key employment in several countries throughout the globe. Artificial Intelligence (AI) is finding its way into the agricultural industry. Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep Learning (DL) can offer approaches that help produce nutritious grains. Artificial Intelligence (AI) and Machine Learning (ML) play a significant role in predicting the yield of major crops. This research employs advanced statistical and machine learning techniques to predict maize and wheat yields in the Dharwad district from 1980 to 2021, using weather data. The study objectives encompassed the development of yield prediction models, examining the impact of weather parameters on maize and wheat yield, and analysing shifts in cropping patterns.<br/>Correlation analysis reveals significant and non-significant relationships between weather parameters and maize and wheat yield, with maximum and minimum temperature, rainfall, relative humidity, and windspeed showing varying degrees of influence. Regression analysis identifies significant factors affecting yield, with windspeed emerging as a crucial predictor in the Dharwad district for maize crop and maximum temperature as a crucial predictor for wheat crop in the Dharwad district. Machine learning models, including Support Vector Regression and K-nearest neighbor, were employed to predict yields based on weather parameters. K-nearest neighbor demonstrated superior predictive accuracy, offering insights for farmers' decision-making. Markov chain modelling unveiled shifts in cropping patterns.<br/>Policy implications underscore the value of the predictive model's role in modern agriculture by leveraging data to improve decision-making, enhance productivity, and ensure sustainability. In conclusion, this thorough investigation offers insightful information about the production of maize and wheat in the Dharwad district of Karnataka empowering stakeholders to make well-informed decisions, maximize resource utilization, and improve overall sustainability and productivity.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Agricultural Statistics
700 ## - ADDED ENTRY--PERSONAL NAME
2nd Author, 3rd Author Ashalatha K. V.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha Item type THESIS
Edition M.Sc. (Agri)
Classification part 630.2
Call number prefix AKH
Suppress in OPAC No
942 ## - ADDED ENTRY ELEMENTS (KOHA)
-- 630_200000000000000
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-- 70799
-- 70799
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 28/11/2024   630.2/AKH T14033 07/02/2025 1 07/02/2025 THESIS