Effect of frost on plants, leaves, and forecast of frost events using convolutional neural networks

Sobia Wassan, Chen Xi, Nz Jhanjhi () and Laiqa Binte-Imran

International Journal of Distributed Sensor Networks, 2021, vol. 17, issue 10, 15501477211053777

Abstract: 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.

Keywords: Convolutional neural network; frost event; ensemble; prediction; error rate (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:17:y:2021:i:10:p:15501477211053777

DOI: 10.1177/15501477211053777

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