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.
