Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks
Climate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211053777 |
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| _version_ | 1832556816798580736 |
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| author | Sobia Wassan Chen Xi NZ Jhanjhi Laiqa Binte-Imran |
| author_facet | Sobia Wassan Chen Xi NZ Jhanjhi Laiqa Binte-Imran |
| author_sort | Sobia Wassan |
| collection | DOAJ |
| description | Climate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timing of growing and reproduction might be an essential tactic by which plant life can avoid frost. Flowers are more sensitive to hoarfrost than leaves but more sensitive to frost in most cases. In most cases, frost affects the size of the plant, its growth, and the production of seeds. In this article, we examined that how frost affects plants and flowers? How it affects the roots and prevents the growth of plants, vegetables, and fruits? Furthermore, we predicted how the frost will grow and how we should take early precautions to protect our crops? We presented the convolutional neural network model framework and used the conv1d algorithm to evaluate one-dimensional data for frost event prediction. Then, as part of our model contribution, we preprocessed the data set. The results were comparable to four weather stations in the United States. The results showed that our convolutional neural network model configuration is reliable. |
| format | Article |
| id | doaj-art-5d755b9fdad44fb7987bdd5d31696252 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-5d755b9fdad44fb7987bdd5d316962522025-02-03T05:44:18ZengWileyInternational Journal of Distributed Sensor Networks1550-14772021-10-011710.1177/15501477211053777Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networksSobia Wassan0Chen Xi1NZ Jhanjhi2Laiqa Binte-Imran3Business School Nanjing University, Nanjing, ChinaBusiness School Nanjing University, Nanjing, ChinaCenter for Smart Society 5.0 [CSS5], Faculty of Innovation & Technology, Taylor’s University, Subang Jaya, MalaysiaDepartment of Computer Science, COMSATS University Islamabad, Sahiwal, PakistanClimate change brings many changes in a physical environment like plants and leaves. The flowers and plants get affected by natural climate and local weather extremes. However, the projected increase in the frost event causes sensitivity in plant reproduction and plant structure vegetation. The timing of growing and reproduction might be an essential tactic by which plant life can avoid frost. Flowers are more sensitive to hoarfrost than leaves but more sensitive to frost in most cases. In most cases, frost affects the size of the plant, its growth, and the production of seeds. In this article, we examined that how frost affects plants and flowers? How it affects the roots and prevents the growth of plants, vegetables, and fruits? Furthermore, we predicted how the frost will grow and how we should take early precautions to protect our crops? We presented the convolutional neural network model framework and used the conv1d algorithm to evaluate one-dimensional data for frost event prediction. Then, as part of our model contribution, we preprocessed the data set. The results were comparable to four weather stations in the United States. The results showed that our convolutional neural network model configuration is reliable.https://doi.org/10.1177/15501477211053777 |
| spellingShingle | Sobia Wassan Chen Xi NZ Jhanjhi Laiqa Binte-Imran Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks International Journal of Distributed Sensor Networks |
| title | Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks |
| title_full | Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks |
| title_fullStr | Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks |
| title_full_unstemmed | Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks |
| title_short | Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks |
| title_sort | effect of frost on plants leaves and forecast of frost events using convolutional neural networks |
| url | https://doi.org/10.1177/15501477211053777 |
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