JoWUA

Abstract

This study examines the utilization of a Deep Convolutional Neural Network (DCNN) for identifying defects in fruit via digital image processing. The primary challenges encountered in the agricultural and fruit distribution sectors are the inefficiency and variability of the manual inspection process, necessitating the development of an accurate and dependable automated system. This study conducted an experiment comparing the GoogleNet baseline model with the Proposed Method, a modified version of GoogleNet that incorporates transfer learning and final layer optimization to enhance classification performance. The fruit image dataset underwent pre-processing and data augmentation to enhance data variability and improve the model's generalization capability. The data was then divided into three categories, namely training, validation, and test, with 80 % of the data set allocated to training, 10 % to validation, and 10 % to test. The two models performed well in the course of training, although the Proposed Method had a greater advantage. The proposed method achieved a recall, precision, and F1-score of 0.99 and an accuracy of 0.9980. GoogleNet recorded the value of recall, precision, and F1-score as 0.98 and accuracy of 0.9870, respectively. These findings indicate that transfer learning and layer modification of the GoogleNet structure can be used to help fruit defect detection processes achieve improved results. The suggested approach, which is highly precise, may be applied in automated inspection systems within the area of the fruit industry to make the products superior and the processes more productive. This paper also reveals that it is possible to use DCNN and appropriate optimizations so that processing fruit images can be a good idea.