Artificial Intelligence (AI) as a complementary technology for agricultural Remote Sensing (RS) in plant physiology teaching
Ali Ahmad – Universidad de Granada - ORCID
Shab E Noor – Universidad de Granada - ORCID
Pedro Cartujo Cassinello – Universidad de Granada - ORCID
Vanessa Martos Núñez – Universidad de Granada - ORCID
Abstract
Agriculture is facing several challenges such as climate change, drought, and loss of fertile land, which could compromise global food safety and security. In this scenario, integration of novel technologies into agriculture could be the possible solution to address these concerns. There are several modern technology tools that can be integrated into agriculture for this purpose. Agricultural remote sensing (RS) technology, being one of the promising tools, has long been used for agriculture, but its potential has not been explored fully. RS involves monitoring and analysis of various crop growth parameters generating huge datasets. But management and interpretation of RS generated data is a complex and costly process. Therefore, artificial intelligence (AI), another promising tool of 5th industrial era, could be used to complement agricultural RS technology to improve data processing and generating visualizing results. Machine learning, a subset of AI, methods have been efficiently employed for disease detection, yield predictions, and biomass estimations. Yet, there remains a huge possibility to develop crop growth and yield simulations, and machine training models from the freely available satellite data. Hence, indicating and instilling this knowledge into young students would result in the novel initiatives in agricultural plant physiology, since most of the parameters analyzed through RS are physiological.
Keyword: Artificial Intelligence (AI)
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