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| 7 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Deep Learning has been successfully applied to image recognition, speech recognition,
and natural language processing in recent years. Therefore, there has been an incentive to apply
it in other fields as well. The field of agriculture is one of the most important fields in which the
application of deep learning still needs to be explored, as it has a direct impact on human well-being.
In particular, there is a need to explore how deep learning models can be used as a tool for optimal
planting, land use, yield improvement, production/disease/pest control, and other activities. The
vast amount of data received from sensors in smart farms makes it possible to use deep learning as a
model for decision-making in this field. In agriculture, no two environments are exactly alike, which
makes testing, validating, and successfully implementing such technologies much more complex
than in most other industries. This paper reviews some recent scientific developments in the field of
deep learning that have been applied to agriculture, and highlights some challenges and potential
solutions using deep learning algorithms in agriculture. The results in this paper indicate that by
employing new methods from deep learning, higher performance in terms of accuracy and lower
inference time can be achieved, and the models can be made useful in real-world applications. Finally,
some opportunities for future research in this area are suggested.
Description
Keywords
Agriculture Deep Learning Smart Farm Support Decision-Making Algorithms