Greenhouse farming needs precise monitoring to guarantee
healthy plant growth, while conventional methods are cumbersome, inexact, and
not amenable to steady-state operation. This study outlines an Internet of
Things (IoT)-based vegetable garden monitoring system that automatically
controls irrigation, climate, and plant disease in a greenhouse. The system
incorporates an LDR light sensor, ESP32-CAM, soil moisture sensor, and DHT11
temperature-humidity sensor controlled by an ESP8266. A motor pump is activated
when the soil moisture is less than the threshold value; temperature and
humidity are controlled by a fan, while light is provided through an LED when
light intensity is low. At specified time epochs, the ESP32-CAM shoots plant
pictures that are analysed through a Python-based deep learning algorithm to
determine five plant health classes: yellow leaves, curled leaves, leaf spot,
white fly infestation, and healthy. A web page presents sensor readout in real
time as well as disease classification outcome. Experimental validation
indicates classification accuracy of 94.6 %, ±2°C accuracy in temperature
regulation, ±5% in humidity control, and 92% accuracy in irrigation. Against
previous works that employed IoT to automate greenhouse farming, this solution is
not only superior in control accuracy but also incorporates automatic disease
diagnosis. Despite limitations due to internet reliability as well as dataset
size, the solution provides insights into how IoT, as well as AI, can minimize
manual intervention, facilitate early detection, as well as, promote
sustainable farming.
Author(s) Details
Hanamant R Jakaraddi
Department of MCA, Acharya Institute of Technology, Bangalore-560107,
India.
Hridhik Hareendran
Department of MCA, Acharya Institute of Technology, Bangalore-560107,
India.
Sridevi G
Department of Computer Applications, Acharya Institute of Graduate Studies,
Bangalore-560107, India.
Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-88417-94-5/CH6
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