Big Data Analysis Impact of Sensor Values on Indoor Smart Farming For Plant Growth's Prediction

Abstract

Indoor Smart Farming design is one of the solutions for the food needs on a small scale,
especially during the difficult conditions such as the COVID-19 pandemic. By applying IoT and some
semi-automatic control to the planting area, it is hoped that users will be easier to monitor their plants.
Visualization of monitoring can be seen through the websites that are supported by desktop PC and
Android-based microcontroller so that through multiple sensing data uploaded to the storage devices, it
can easily detect the growth status if there are any deviations.
IoT sensor support for monitoring indoor smart farming assistant covers temperature, air humidity, soil
moisture, lighting, and CO2 measurements. The monitoring assistant is also equipped with pictures taking
of the plants so the plant's development can be observed. The aim for this indoor smart farming is the big
data analysis for the sensor's reading values, including the results of image processing, that support for
the visualization of the growth of plant growth from cultivation to harvesting. For small scale indoor
smart farming, this is important for users to easily see the conditions of the plant growth charts and
measurement until the plant is ready to harvest. Plant growth until crop time can be predicted with the
sensor's data captured and analyzed.