Technical Paper on Environmental Monitor and Control, Pests and Diseases in the Greenhouse


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

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.

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