Abstract:
In this study greenhouse tomato production was inve
stigated from energy consumption
and greenhouse gas (GHG) emission point of views. M
oreover, artificial neural networks
(ANNs) and adaptive neuro-fuzzy inference systems (
ANFIS) were employed to model
energy consumption for greenhouse tomato production
. Total energy input and output
were calculated as 1,316.14 and 281.1 GJ ha
-1
. Among the all energy inputs, natural gas
and electricity had the most significant contributi
on to the total energy input. Evaluations
of GHG emission illustrated that the total GHG emis
sion was estimated at 34,758.11 kg
CO
2
eq ha
-1
and, among all the inputs, electricity played the
most important role, followed
by natural gas. Comparison between ANN and ANFIS mo
dels showed that, due to
employing fuzzy rules, the ANFIS-based models could
model output energy more
accurately than ANN models. Accordingly, correlatio
n coefficient (R), Root Mean Square
Error (RMSE) and Mean Absolute Percentage Error (MA
PE) for the best ANFIS
architecture were calculated as 0.983, 0.025, and 0
.149, respectively, while these
performance parameters for the best ANN model were
computed as 0.933, 0.05414, and
0.279, respectively.
Keywords
: Neuro-fuzzy, Greenhouse crop production
,
GHG emission.
