A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction
Moringa oleifera, the best known of the thirteen species of the genus Moringacae, has achieved importance due to its multipurpose usage with high nutritional value. There is very little work has been done in the advancement of moringa varieties in Pakistan. Thus, it needs to develop a new variety...
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| Format: | Article |
| Language: | English |
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The University of Lahore
2022-03-01
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| Series: | Pakistan Journal of Engineering & Technology |
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| Online Access: | https://hpej.net/journals/pakjet/article/view/1574 |
| _version_ | 1818356646906167296 |
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| author | Muhammad Ayoub Shabir Hussain Akmal Khan Muhammad Zahid Junaid Abdul Wahid Liao Zhifang Rukshanda Rehman |
| author_facet | Muhammad Ayoub Shabir Hussain Akmal Khan Muhammad Zahid Junaid Abdul Wahid Liao Zhifang Rukshanda Rehman |
| author_sort | Muhammad Ayoub |
| collection | DOAJ |
| description |
Moringa oleifera, the best known of the thirteen species of the genus Moringacae, has achieved importance due to its multipurpose usage with high nutritional value. There is very little work has been done in the advancement of moringa varieties in Pakistan. Thus, it needs to develop a new variety of moringa with better nutritional value. The agrarian performs many experiments like interbreeding of Moringa oleifera germplasm with exotic germplasm. Furthermore, they grow it in the nursery and then move it on the field, which almost took six months in a traditional approach. It consumes various resources and time to access the quality of newly developed varieties. This work aims to use machine learning and deep learning approaches to reduce the utilization of various resources and time which is used by the agrarian to develop a new moringa variety. We used machine learning and deep learning approaches to make predictions about new varieties before their proper plantation. In this research work, we took two moringa parents’ varieties with their required features like plant height, protein, potassium. We trained machine learning and deep learning models on the feature values of parents’ varieties. Our proposed machine learning model made the best predictions, using parents’ plant features to determine these parameter values in their offspring varieties, which will help to choose the best interbreed variety of moringa oleifera.
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| first_indexed | 2024-12-13T20:00:32Z |
| format | Article |
| id | doaj.art-82bd360a66e04b229cee0d121e5d4b9c |
| institution | Directory Open Access Journal |
| issn | 2664-2042 2664-2050 |
| language | English |
| last_indexed | 2024-12-13T20:00:32Z |
| publishDate | 2022-03-01 |
| publisher | The University of Lahore |
| record_format | Article |
| series | Pakistan Journal of Engineering & Technology |
| spelling | doaj.art-82bd360a66e04b229cee0d121e5d4b9c2022-12-21T23:33:11ZengThe University of LahorePakistan Journal of Engineering & Technology2664-20422664-20502022-03-015110.51846/vol5iss1pp68-77A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties PredictionMuhammad Ayoub0Shabir Hussain1Akmal Khan2Muhammad Zahid3Junaid Abdul Wahid4Liao Zhifang5Rukshanda Rehman6School of Computer Science and Engineering, Central South University, ChinaSchool of Information Engineering, Zhengzhou University, ChinaDepartment of Data Science, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Agronomy, University of Agriculture Faisalabad, Faisalabad, PakistanSchool of Information Engineering, Zhengzhou University, Zhengzhou, ChinaSchool of Computer Science and Engineering, Central South University, ChinaDepartment of Zoology,The Islamia University, Bahawalpur, Pakistan Moringa oleifera, the best known of the thirteen species of the genus Moringacae, has achieved importance due to its multipurpose usage with high nutritional value. There is very little work has been done in the advancement of moringa varieties in Pakistan. Thus, it needs to develop a new variety of moringa with better nutritional value. The agrarian performs many experiments like interbreeding of Moringa oleifera germplasm with exotic germplasm. Furthermore, they grow it in the nursery and then move it on the field, which almost took six months in a traditional approach. It consumes various resources and time to access the quality of newly developed varieties. This work aims to use machine learning and deep learning approaches to reduce the utilization of various resources and time which is used by the agrarian to develop a new moringa variety. We used machine learning and deep learning approaches to make predictions about new varieties before their proper plantation. In this research work, we took two moringa parents’ varieties with their required features like plant height, protein, potassium. We trained machine learning and deep learning models on the feature values of parents’ varieties. Our proposed machine learning model made the best predictions, using parents’ plant features to determine these parameter values in their offspring varieties, which will help to choose the best interbreed variety of moringa oleifera. https://hpej.net/journals/pakjet/article/view/1574Machine LearningDeep LearningAgronomyMoringa Oleifera |
| spellingShingle | Muhammad Ayoub Shabir Hussain Akmal Khan Muhammad Zahid Junaid Abdul Wahid Liao Zhifang Rukshanda Rehman A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction Pakistan Journal of Engineering & Technology Machine Learning Deep Learning Agronomy Moringa Oleifera |
| title | A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction |
| title_full | A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction |
| title_fullStr | A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction |
| title_full_unstemmed | A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction |
| title_short | A Predictive Machine Learning and Deep Learning Approach on Agriculture Datasets for New Moringa Oleifera Varieties Prediction |
| title_sort | predictive machine learning and deep learning approach on agriculture datasets for new moringa oleifera varieties prediction |
| topic | Machine Learning Deep Learning Agronomy Moringa Oleifera |
| url | https://hpej.net/journals/pakjet/article/view/1574 |
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