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CC BY V4.0, Open Access
Journal article
Early frost detection in wheat using machine learning from vertical temperature distributions
Computers and electronics in agriculture, Vol.221, 108950
2024
Abstract
Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic process. During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding these temperature gradients is essential for improving frost management strategies in wheat crops. We hypothesise that the relationship between the temperatures of the canopy, plant and ground can be an early indicator of frost. We collected infrared thermal (IRT) images from field-grown wheat crops and extracted the temperatures from the canopy, plant and ground layers. We analysed these temperatures and applied four machine learning (ML) models to detect coldness scales leading to frost nights with different degrees of severity. We implemented a gated recurrent unit, convolutional neural network, random forest and support vector machines to evaluate the classification. Our study shows that in these three layers, temperatures have a relationship that can be used to determine frost early. The patterns of these three temperatures on a frost night differ from a cold no-frost winter night. On a no-frost night we observed that the canopy is the coldest, plant is warm, and the soil is warmest, and these three temperatures did not converge. On the other hand, on a frost night, before the frost event, the canopy and plant temperatures converged as the cold air penetrated through the canopy. These patterns in temperature distribution were translated into an ML problem to detect frost early. We classified coldness scales based on the temperatures conducive to frost formation of a certain severity degree. Our results show that the ML models can determine the coldness scales automatically with 93%–98% accuracy across the four models. The study presents a strong foundation for the development of early frost detection systems.
Details
- Title
- Early frost detection in wheat using machine learning from vertical temperature distributions
- Authors/Creators
- Sayma Shammi - Murdoch University, School of Information TechnologyDean Diepeveen - Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, AustraliaSebastian Zander - Murdoch University, School of Information TechnologyMichael G.K. Jones - Murdoch UniversityFerdous Sohel - Murdoch University, Centre for Crop and Food Innovation
- Publication Details
- Computers and electronics in agriculture, Vol.221, 108950
- Publisher
- Elsevier B.V
- Identifiers
- 991005657666207891
- Copyright
- © 2024 The Author(s).
- Murdoch Affiliation
- Centre for Crop and Food Innovation; School of Information Technology; School of Agricultural Sciences
- Language
- English
- Resource Type
- Journal article
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