Authors :
Ediger Mutevani; Monica Gondo; Edmore Tarambiwa
Volume/Issue :
Volume 7 - 2022, Issue 3 - March
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3IJU8o0
DOI :
https://doi.org/10.5281/zenodo.6395400
Abstract :
A greenhouse is a glass or plastic covered area
or a room dedicated for the growing of crops under a
controlled environment with the intention of growing
plants all year round undisturbed by prevailing weather
environment. The issue of seasons that make it difficult to
grow crops is disregarded or completely ignored in such a
scenario. Maximum yield is realized in this instance as a
result of controlled temperature, humidity, soil moisture
to mention but a few parameters that are kept under
check so that crops have optimal conditions for growth at
any one given time. To further buttress the issue of crop
yield it is critical to consider pests and diseases in the
equation. Mitigation efforts to monitor and control pests
and diseases assists in coming up with a quality crop that
translate into a bumper harvest. In order to achieve this
Internet of Things and Embedded systems together with
Machine Learning techniques are used. Sensor readings
of these environmental parameters together with crop
image status are used to train a model that help identify
patterns followed by pests and diseases thus assisting in
decision-making purposes to curtail pests and diseases
spread. Once a disease or pest is identified through a
camera as a result of image processing, an actuator
connected to a tank with chemical spray go high thus
spraying all tomato plants in the greenhouse affected by
red spider mite to correct the anomaly. The greenhouse is
highly automated although minimal human effort cannot
be overlooked. Sensor readings and crop images are
stored in the cloud as big data. With this information,
farmers can plan ahead, gather required chemicals and
inputs in preparation for spraying pests and diseases once
they surface. Stakeholders can view status of the green
house via a smart phone, a laptop or a desktop so long
there is a reliable internet connection.
Keywords :
Greenhouse, Internet of Things, Machine learning, Embedded systems, Image processing.
A greenhouse is a glass or plastic covered area
or a room dedicated for the growing of crops under a
controlled environment with the intention of growing
plants all year round undisturbed by prevailing weather
environment. The issue of seasons that make it difficult to
grow crops is disregarded or completely ignored in such a
scenario. Maximum yield is realized in this instance as a
result of controlled temperature, humidity, soil moisture
to mention but a few parameters that are kept under
check so that crops have optimal conditions for growth at
any one given time. To further buttress the issue of crop
yield it is critical to consider pests and diseases in the
equation. Mitigation efforts to monitor and control pests
and diseases assists in coming up with a quality crop that
translate into a bumper harvest. In order to achieve this
Internet of Things and Embedded systems together with
Machine Learning techniques are used. Sensor readings
of these environmental parameters together with crop
image status are used to train a model that help identify
patterns followed by pests and diseases thus assisting in
decision-making purposes to curtail pests and diseases
spread. Once a disease or pest is identified through a
camera as a result of image processing, an actuator
connected to a tank with chemical spray go high thus
spraying all tomato plants in the greenhouse affected by
red spider mite to correct the anomaly. The greenhouse is
highly automated although minimal human effort cannot
be overlooked. Sensor readings and crop images are
stored in the cloud as big data. With this information,
farmers can plan ahead, gather required chemicals and
inputs in preparation for spraying pests and diseases once
they surface. Stakeholders can view status of the green
house via a smart phone, a laptop or a desktop so long
there is a reliable internet connection.
Keywords :
Greenhouse, Internet of Things, Machine learning, Embedded systems, Image processing.
