Abstract:
The nut is one of the most widely grown and economically important crops in the
world. Nut breeds must be correctly recognized for a variety of applications in
breeding, agriculture, and trade. In recent years, deep learning algorithms have
emerged as powerful tools for image recognition tasks, inspiring researchers to
investigate their potential for nut breed recognition. This release presents extensive
research on the application of deep learning for nut recognition. Nut recognition has
been successfully applied to deep learning models, including VGG16, ResNet50,
MobileNet, Inception V3, and Xception. These models were trained on images of
different nuts and learned to differentiate between different nut breeds based on their
various visual characteristics, including size, shape, color, texture, and skin pattern.
The MobileNet model is the most accurate deep learning model. The accuracy of the
MobileNet model was 95.83%. We don't only judge accuracy. We evaluated a few
parameters, including F1-score, precision, and recall. Extensive testing and
evaluation are used to assess the deep learning models' performance and accuracy.
