Statistical model and prediction of pineapple plant weight

In the Great Giant Pineapples Company, the problem of prediction of weight of fruits at harvest has become a long critical problem for the planning, cannery and marketing. The company has been trying for a long time to find the best method to predict the production of pineapple weight per hectare by...

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Main Authors:	Usman, Mustofa, Elfaki, Faiz Ahmed Mohamed, Wamiliana, Wamiliana, Fauzan, Fauzan, Daoud, Jamal Ibrahim
Format:	Article
Language:	English
Published:	Sci.Int.(Lahore) 2015
Subjects:	QA Mathematics
Online Access:	http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/1/Mustofa_Usman--SI.pdf

id	iium-42862
recordtype	eprints
spelling	iium-428622017-11-21T07:43:29Z http://irep.iium.edu.my/42862/ Statistical model and prediction of pineapple plant weight Usman, Mustofa Elfaki, Faiz Ahmed Mohamed Wamiliana, Wamiliana Fauzan, Fauzan Daoud, Jamal Ibrahim QA Mathematics In the Great Giant Pineapples Company, the problem of prediction of weight of fruits at harvest has become a long critical problem for the planning, cannery and marketing. The company has been trying for a long time to find the best method to predict the production of pineapple weight per hectare by using the information of plant weight. It is well known that the fruit weight has linear relationship with the pineapples plant weight. In this study, the modeling and prediction of pineapples plant weight will be discussed based on some factors. The experiment have been conducted in four difference locations and cultivar classes and varieties, namely location 094D with cultivar class Medium Crown and variety GP1, location 126C with cultivar class Medium Crown and variety GP1, location 158H with cultivar class Small Crown and variety GP1and in location 576D with cultivar class Medium Crown and variety GP1. The age of plants are 15 months of age. From each location 40 data has been taken by method of systematic random sampling. Than from each datum the plant weight (W) in kg, number of perfect leaves (NPL), the length of the longest leaf (LLL) in cm, and the width of the longest leaf (WLL) in cm are measured. From the analysis the plant weight best predicted by using variables NPL, LLL, and WLL in all locations. Sci.Int.(Lahore) 2015-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/42862/1/Mustofa_Usman--SI.pdf Usman, Mustofa and Elfaki, Faiz Ahmed Mohamed and Wamiliana, Wamiliana and Fauzan, Fauzan and Daoud, Jamal Ibrahim (2015) Statistical model and prediction of pineapple plant weight. Science International Lahore, 27 (2). pp. 943-949. ISSN 1013-5316 http://sci-int.com/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Usman, Mustofa Elfaki, Faiz Ahmed Mohamed Wamiliana, Wamiliana Fauzan, Fauzan Daoud, Jamal Ibrahim Statistical model and prediction of pineapple plant weight
description	In the Great Giant Pineapples Company, the problem of prediction of weight of fruits at harvest has become a long critical problem for the planning, cannery and marketing. The company has been trying for a long time to find the best method to predict the production of pineapple weight per hectare by using the information of plant weight. It is well known that the fruit weight has linear relationship with the pineapples plant weight. In this study, the modeling and prediction of pineapples plant weight will be discussed based on some factors. The experiment have been conducted in four difference locations and cultivar classes and varieties, namely location 094D with cultivar class Medium Crown and variety GP1, location 126C with cultivar class Medium Crown and variety GP1, location 158H with cultivar class Small Crown and variety GP1and in location 576D with cultivar class Medium Crown and variety GP1. The age of plants are 15 months of age. From each location 40 data has been taken by method of systematic random sampling. Than from each datum the plant weight (W) in kg, number of perfect leaves (NPL), the length of the longest leaf (LLL) in cm, and the width of the longest leaf (WLL) in cm are measured. From the analysis the plant weight best predicted by using variables NPL, LLL, and WLL in all locations.
format	Article
author	Usman, Mustofa Elfaki, Faiz Ahmed Mohamed Wamiliana, Wamiliana Fauzan, Fauzan Daoud, Jamal Ibrahim
author_facet	Usman, Mustofa Elfaki, Faiz Ahmed Mohamed Wamiliana, Wamiliana Fauzan, Fauzan Daoud, Jamal Ibrahim
author_sort	Usman, Mustofa
title	Statistical model and prediction of pineapple plant weight
title_short	Statistical model and prediction of pineapple plant weight
title_full	Statistical model and prediction of pineapple plant weight
title_fullStr	Statistical model and prediction of pineapple plant weight
title_full_unstemmed	Statistical model and prediction of pineapple plant weight
title_sort	statistical model and prediction of pineapple plant weight
publisher	Sci.Int.(Lahore)
publishDate	2015
url	http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/1/Mustofa_Usman--SI.pdf
first_indexed	2023-09-18T21:01:05Z
last_indexed	2023-09-18T21:01:05Z
_version_	1777410627663298560

Statistical model and prediction of pineapple plant weight

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