Using Image Processing and Transfer Learning Approaches for Local Vegetable Freshness Classification

Abstract

This project makes use of image processing and transfer learning techniques for local vegetable freshness prediction. In an automatic freshness assessment process is proposed where advanced image processing techniques are used to extract important feature of vegetables, namely color, texture, and shape. We use these features to transfer learning by pre-trained deep learning models. The narrative unfolds through the lens of DenseNet201, the chosen protagonist, demonstrating its efficacy with a testing accuracy of 99.40% and minimal loss at 0.03. Results show that this method can discriminate freshness of different levels, it is accurate and reliable. In addition to technical accomplishments, the study also assesses the societal, environmental, and moral considerations in applying this technology to the vegetable industry. It points out the need to respect technology and provide a panoramic view of responsibility beyond classification only. As the concluding chapter sets the stage for future endeavors, the study invites stakeholders to partake in interdisciplinary collaborations, dataset expansions, and optimization strategies. This study not only demonstrates the potential to use advanced technologies in vegetable freshness classification but also provides useful information for future researchers and practitioners in agriculture. By enhancing the efficiency and objectivity of freshness assessment, this project aims to improve quality control, reduce food waste, and support better decision-making processes in the supply chain.