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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| Format: | Article |
| Language: | English |
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Sci.Int.(Lahore)
2015
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| Online Access: | http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/1/Mustofa_Usman--SI.pdf |
| _version_ | 1848782345067298816 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T16:03:59Z |
| format | Article |
| id | iium-42862 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:03:59Z |
| publishDate | 2015 |
| publisher | Sci.Int.(Lahore) |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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/ |
| spellingShingle | QA Mathematics Usman, Mustofa Elfaki, Faiz Ahmed Mohamed Wamiliana, Wamiliana Fauzan, Fauzan Daoud, Jamal Ibrahim Statistical model and prediction of pineapple plant weight |
| title | 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_short | Statistical model and prediction of pineapple plant weight |
| title_sort | statistical model and prediction of pineapple plant weight |
| topic | QA Mathematics |
| url | http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/ http://irep.iium.edu.my/42862/1/Mustofa_Usman--SI.pdf |