Secured IoT Based Smart Greenhouse System with Image Inspection

Automated Greenhouse System helps the farmers by controlling the environmental parameters through Internet of Things(IoT), including crop health inspection using image analysis. The Greenhouse is generally affected by two factors: plant disease and weather condition, which leads to the fall in produ...

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Vydáno v:International Conference on Advanced Computing and Communication Systems (Online) s. 1080 - 1082
Hlavní autoři: Sundari.M, Shunmuga, Mathana, J.M, Nagarajan, T.S
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2020
Témata:
Air pollution
Cloud computing
Data security -XTEA-Extended Tiny Encryption Algorithm
Diseases
Green products
Greenhouse System
Humidity
Inspection
IoT
OpenCV
Plant Inspection System
Temperature sensors
ISBN:1728151961, 9781728151960
ISSN:2575-7288
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Shrnutí:Automated Greenhouse System helps the farmers by controlling the environmental parameters through Internet of Things(IoT), including crop health inspection using image analysis. The Greenhouse is generally affected by two factors: plant disease and weather condition, which leads to the fall in production. The weather condition can be controlled through Microcontroller Unit(MCU) and the plant disease can be monitored using image inspection system. The research recommends a cheaper image evaluation framework for the plant disease analysis and fully automated Greenhouse with data security. The prototype of the proposed system consists of Raspberry pi, MSP432, Temperature sensor, Moisture sensor, Humidity sensor and OpenCV Image Inspection System. The actuators and motors are controlled by MCU MSP432 through relays upon reaching predetermined threshold values. The proposed architecture is equipped with embedded data security by implementing Extended Tiny Encryption Algorithm (XTEA). Lastly, the agriculturists can familiarize with the recommended framework through the cloud-centered application. The autonomous framework permits the agriculturists to evaluate and control their greenhouse ecology remotely.
ISBN:1728151961
9781728151960
ISSN:2575-7288
DOI:10.1109/ICACCS48705.2020.9074258