Abstract:
Rice is a highly consumed staple food in Sri Lanka.
From farming phase to distribution phase of
paddy, classification of paddy is becoming vital as
it provides efficiency to the planning, production,
sales and consumption. In Sri Lanka, the
evaluation of the classification of paddy varieties
is typically overseen by the Rice Research and
Development Institute (RRDI). Traditionally,
paddy identification is done manually by human
inspectors, ensuring some level of accuracy but
requiring significant manpower, time, and
subjective judgment. This research seeks to
transform the categorization of paddy varieties in
Sri Lanka. This paper provides an approach to
identifying and classifying paddy variety in paddy
sample with the help of image processing and
CNN model. For this approach, 10 varieties of
paddy samples were collected from Rice Research
and Development Institute. With these samples a
dataset of more than 10,000 images were captured
and used in this research. Image preprocessing
involved cropping, scaling, and noise removal to
standardize the data. Experiments were conducted
with nine different CNN models, iterating through
various architectures and training parameters to
optimize performance. The experiment was
performed on ten rice categories to evaluate the
suggested solution. The accuracy of classification
is of 93.69%.
