首页|Murdoch University Details Findings in Machine Learning (Early Frost Detection I n Wheat Using Machine Learning From Vertical Temperature Distributions)
Murdoch University Details Findings in Machine Learning (Early Frost Detection I n Wheat Using Machine Learning From Vertical Temperature Distributions)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Murdoch, Australia, by NewsRx journalists, research stated, "Frost damage significantly reduces global wheat production. Temperature development in wheat crops is a complex and dynamic proc ess." Financial support for this research came from Murdoch University Digital Agricul -ture Connectivity PhD scholarship. The news correspondents obtained a quote from the research from Murdoch Universi ty, "During frost events, a vertical temperature gradient develops from soil to canopy due to the heat loss from the soil and canopy boundary. Understanding the se 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 i nfrared thermal (IRT) images from fieldgrown wheat crops and extracted the temp eratures from the canopy, plant and ground layers. We analysed these temperature s and applied four machine learning (ML) models to detect coldness scales leadin g to frost nights with different degrees of severity. We implemented a gated rec urrent unit, convolutional neural network, random forest and support vector mach ines 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-fro st 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 co nverge. 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 con ducive to frost formation of a certain severity degree. Our results show that th e ML models can determine the coldness scales automatically with 93% -98% accuracy across the four models."
MurdochAustraliaAustralia and New Ze alandCyborgsEmerging TechnologiesMachine LearningMurdoch University
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