Scene and Environment Monitoring Using Aerial Imagery and Deep Learning
IoTI4 Workshop 2019 Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in deep learning techniques have further enhanced the precisio...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
06.06.2019
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Subjects | |
Online Access | Get full text |
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Summary: | IoTI4 Workshop 2019 Unmanned Aerial vehicles (UAV) are a promising technology for smart farming
related applications. Aerial monitoring of agriculture farms with UAV enables
key decision-making pertaining to crop monitoring. Advancements in deep
learning techniques have further enhanced the precision and reliability of
aerial imagery based analysis. The capabilities to mount various kinds of
sensors (RGB, spectral cameras) on UAV allows remote crop analysis applications
such as vegetation classification and segmentation, crop counting, yield
monitoring and prediction, crop mapping, weed detection, disease and nutrient
deficiency detection and others. A significant amount of studies are found in
the literature that explores UAV for smart farming applications. In this paper,
a review of studies applying deep learning on UAV imagery for smart farming is
presented. Based on the application, we have classified these studies into five
major groups including: vegetation identification, classification and
segmentation, crop counting and yield predictions, crop mapping, weed detection
and crop disease and nutrient deficiency detection. An in depth critical
analysis of each study is provided. |
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DOI: | 10.48550/arxiv.1906.02809 |