  With the use of deep learning in many areas, how to improve this technology
or how to develop the structure used more effectively and in a shorter time is
an issue that is of interest to many people working in this field. Many studies
are carried out on this subject, it is aimed to reduce the duration of the
operation and the processing power required, except to obtain the best result
with the changes made in the variables, functions and data in the models used.
In this study, in the leaf classification made using Mendeley data set
consisting of leaf images with a fixed background, all other variables such as
layer number, iteration, number of layers in the model and pooling process were
kept constant, except for the filter dimensions of the convolution layers in
the determined model. Convolution layers in 3 different filter sizes and in
addition to this, many results obtained in 2 different structures, increasing
and decreasing, and 3 different image sizes were examined. In the literature,
it is seen that different uses of pooling layers, changes due to increase or
decrease in the number of layers, the difference in the size of the data used,
and the results of many functions used with different parameters are evaluated.
In the leaf classification of the determined data set with CNN, the change in
the filter size of the convolution layer together with the change in different
filter combinations and in different sized images was focused. Using the data
set and data reproduction methods, it was aimed to make the differences in
filter sizes and image sizes more distinct. Using the fixed number of
iterations, model and data set, the effect of different filter sizes has been
observed.
