Our data is about Advanced IoT Agriculture with 30,000 entries and 14 columns that are taken from a
student's
master’s thesis research. The data studies the quality of plants that grow in an IoT greenhouse compared to
other plants
that grow in a traditional greenhouse, then classified them into 6 different classes according to some
significant
features such as:
Average of chlorophyll in the plant (ACHP), which has a main role in plants' nutrition by performing
photosynthesis process
, average root diameter (ARD), which is directly connected to the plants ability to absorb water and
nutrients from the soil,
and plant height rate (PHR), which is measuring the dynamic growth of the plant over time and directly
affected by the plants nutrition.
Since most of our team members are from computer-related majors, we selected this data because it develops a
new technological
footprint in one of the most important life fields which can enhance automation in smart greenhouses.
No doubt it is difficult to classify such problems, especially when we consider the high dimensionality
of data gained from containing many columns and the possibility of unexpected environmental external factors
impact.
However, classifying the data can give meaningful results.