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Title Problem Contribution Findings Limitations Future work
Detecting Crop Health
using Machine Learning
Techniques in Smart
Agriculture System
With the rapid increase in
population, need for food is
necessary and challenging
problem for the entire world.
To increase the production
with limited resources is the
main challenge for farmers.
Detecting crop health is
necessary to increase the
production and timely remedy
of the dieses in crops.
In this research IoT and
machine learning techniques
are applied to detect crop
health. This system supports
data and communication
through IoT based UAV
(unmanned Arial vehicle) and
keep high standard
communication in even bad
communication environment..
 We proposed a new
model for detecting
crop health
 Our model is based
on IoT, UAV and
machine learning to
detect crop health and
predict.
 Our model is
evaluated with the
help of various
machine leaning
techniques i.e linear
regression, decision
trees, k-nearest
neighbor, support
vector machine,
random forest and
neural networks.
 The performance of
our model is
evaluated by the
metrics like recall,
precision and f-
measure
 The potential of our
model has checked in
multi-spectral images
captured through IoT
associated UAV
 Accuracy of
detecting crop help
is still a challenge
 Accuracy depends
on the details of
crop nature,
weather conditions,
soil statistics and
water
 Fast data sharing,
multiuse access
and spectrum
sharing is also
limited in
communication
which also needs to
be improved /
invented.
 The majority of
research and
development in
agriculture field is
based
on smart sensing to
assemble image,
which considers
vegetation indices
(VI) only like NDVI.
 The
recommendation
maps can’t be easily
generated by
using only VIs; rather,
various other metrics
required
to be examined like
meteorological
behavior, soil
properties, level of
soil moisture etc.

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Review of Research Article 1 .docx

  • 1. Title Problem Contribution Findings Limitations Future work Detecting Crop Health using Machine Learning Techniques in Smart Agriculture System With the rapid increase in population, need for food is necessary and challenging problem for the entire world. To increase the production with limited resources is the main challenge for farmers. Detecting crop health is necessary to increase the production and timely remedy of the dieses in crops. In this research IoT and machine learning techniques are applied to detect crop health. This system supports data and communication through IoT based UAV (unmanned Arial vehicle) and keep high standard communication in even bad communication environment..  We proposed a new model for detecting crop health  Our model is based on IoT, UAV and machine learning to detect crop health and predict.  Our model is evaluated with the help of various machine leaning techniques i.e linear regression, decision trees, k-nearest neighbor, support vector machine, random forest and neural networks.  The performance of our model is evaluated by the metrics like recall, precision and f- measure  The potential of our model has checked in multi-spectral images captured through IoT associated UAV  Accuracy of detecting crop help is still a challenge  Accuracy depends on the details of crop nature, weather conditions, soil statistics and water  Fast data sharing, multiuse access and spectrum sharing is also limited in communication which also needs to be improved / invented.  The majority of research and development in agriculture field is based on smart sensing to assemble image, which considers vegetation indices (VI) only like NDVI.  The recommendation maps can’t be easily generated by using only VIs; rather, various other metrics required to be examined like meteorological behavior, soil properties, level of soil moisture etc.