Machine Learning Based Statistical Analysis of Dry Chilli Price Forecasting in Haveri District of Karnataka (Record no. 70754)

MARC details
000 -LEADER
fixed length control field 02676nam a2200217 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250129112646.0
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fixed length control field 250129b |||||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency UAS Dharwad
041 ## - LANGUAGE CODE
Language code English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 519.502463
Author Label DEV
100 ## - MAIN ENTRY--PERSONAL NAME
Name of Author Devihosoor Mangala C.
245 ## - TITLE STATEMENT
Title Machine Learning Based Statistical Analysis of Dry Chilli Price Forecasting in Haveri District of Karnataka
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 121
Book Size 32 Cms
520 ## - SUMMARY, ETC.
Abstract. ABSTRACT<br/><br/> Spices are conventional aromatic vegetables mainly utilized for flavouring of food. Among these, chilli (Capsicum annuum), is one of most important spice used around the world. The cultivation and trade of spices, particularly chilli, play a significant role in global culinary practices, with India is major hub in this domain. Renowned as the "Spice Bowl of the World," India's abundant production, consumption, and exportation of spices underscore its pivotal position in the industry. However, the volatility inherent in horticultural markets, exacerbated by natural calamities, necessitates robust forecasting mechanisms to empower farmers to make informed decisions. Recognizing this need, a study was conducted to predict the price of dry chilli in the Bydagi market of Haveri district, Karnataka. Leveraging secondary data sourced from Agmarknet spanning from 2000 to 2022, supervised machine learning techniques were employed, specifically employing Python within a Jupyter notebook, with Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short Term Memory Neural Network (LSTM), Random Forest (RF), and Decision Tree (DT), models scrutinized. The findings underscored the efficacy of the LSTM exhibit superior than ANN, and RNN and RF exhibiting superior performance compared to the DT. The testing R2 values for deep learning models are ANN (0.58), RNN (0.82), LSTM (0.93). Similarly, for Machine learning models RF (0.91), and DT (0.85) and other metrices are also used for comparison of models. This research culminates in a forecast model poised to offer tangible benefits to dry chilli farmers, furnishing them with invaluable insights to navigate the dynamic Agricultural landscape. By leveraging advanced analytical techniques, stakeholders can mitigate risks, optimize resource allocation, and bolster resilience in the face of market fluctuations, thereby fostering sustainability and prosperity within the spice industry.<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Agricultural Statistics
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2nd Author, 3rd Author Ashalatha K. V.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha Item type THESIS
Edition M.Sc. (Agri)
Classification part 519.502463
Call number prefix DEV
Suppress in OPAC No
942 ## - ADDED ENTRY ELEMENTS (KOHA)
-- 519_502463000000000
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-- 70754
-- 70754
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 16/10/2024   519.502463/DEV T13987 29/01/2025 1 29/01/2025 THESIS