Abstract:
In the context of Sri Lanka, where agriculture,
particularly paddy cultivation, plays a crucial
role, farmers face significant challenges due to
weed infestation. Unlike some other countries that
have embraced machine learning technologies to
address these issues, Sri Lanka has yet to adopt
such advanced solutions. To tackle the pervasive
weed problem, a research initiative was
undertaken to develop a mobile application
capable of identifying weed types. The
methodology involved utilizing Convolutional
Neural Network (CNN) pre-trained models,
namely ResNet-50, Inception-v3, and VGG16,
along with the Google Colab platform for training
the dataset. Among the three models, VGG16
demonstrated the highest accuracy, making it the
chosen model to further the research. The primary
goal was to achieve a superior level of accuracy
in detecting weed species in rice fields. The
research team focused on delivering a mobile
application with a high level of accuracy to
identify and classify weeds in paddy fields. The
integration of advanced technologies, such as IoT
and machine learning, aimed to provide Sri
Lankan farmers with an efficient and effective tool
to combat weed-related challenges in their
agricultural practices.
