Deep Learning for Recognition of Nuts Breed

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.