CNN and Transfer Learning Modeling for Jujube Spices Recognition

Abstract

Jujube make up a major portion of Bangladesh's total fruit production. It might be challenging to tell the differences between the many different species of jujube. The manual examination of jujube' physical qualities, which is time-consuming and prone to human mistakes, is the method of identification most commonly used in traditional methods. In this investigation, we make use of computer vision methods to zero in on particular jujube types that are native to the Bangladeshi region. In our approach, the question is solved with the assistance of a deep convolutional neural network (CNN) and Transfer Learning. Our method obtains an outstanding 98.0% accuracy on a test dataset after being trained on photographs of jujube taken in and around Bangladesh. Our work contributes to the growing body of research on applying computer vision and deep learning techniques to agricultural problems. Further research can be conducted to improve the accuracy of our system by collecting a larger dataset of jujube images, exploring the generalizability of our system to other regions and countries, and investigating the potential for using our system to recognize other fruit crops in Bangladesh or other countries.