This study aimed to evaluate the efficiency of energy consumption
and economic analysis of different watermelon
cultivation systems in Fars Province of Iran. Watermelon production
systems were classified into five systems, namely,
custom tillage (group 1), conservation tillage (group 2),
traditional planting (group3), semi mechanized planting (group
4), and mechanized planting (group 5). Data were collected
from 317 watermelon producers from different parts of the
province through face to face interviews. Multi-Layer Perceptron
artificial neural networks were used to model the energy flows
of watermelon production. The results showed that the greatest
energy consumption belonged to mechanized planting system
with the value of 81317.72 MJha-1 and with the productivity of
0.61 kgha-1 and energy use efficiency of 1.17. Clustering
function with three inputs (human resources, machines and
diesel fuel) showed that the difference between groups 2 and 4
is more than the other groups. The least energy consumption
belonged to the conservative agriculture as78163.86 MJha-1
and the energy productivity and energy use efficiency about
0.64 kgha-1 and 1.22, respectively. The results of energy
modeling showed that an ANN model with 9-10-1 structure
was determined to be optimal for energy flow modeling of this
system. Generally, it was concluded that the artificial neural
network models can be applicable to prognosticate the energy
flows of watermelon production. From an economic point of
view, the least net profit belonged to traditional planting with
the value of 2618.14,andthemostnetreturnbelongedtomechanizedplantingwiththevalueof2752.88/ha