Revolutionizing vegetable quality assessment: a comparative study through image processing and transfer learning

Abstract

In the realm of vegetable quality assessment, this study presents a meticulous exploration into the classification of freshness states, employing advanced image processing and transfer learning techniques on a bespoke dataset featuring twelve distinct classes. The narrative unfolds through the lens of DenseNet201, the chosen protagonist, demonstrating its efficacy with a testing accuracy of 98.02% and minimal loss at 0.06. Beyond the technical achievements, the study contemplates the societal, environmental, and ethical dimensions of implementing such technology in the vegetable industry. It underscores the significance of responsible technological integration, offering a comprehensive perspective that transcends mere classification metrics. 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 vision advocates for a broader impact, shaping the trajectory of vegetable quality control and aligning with principles of environmental sustainability. This study stands as a formal narrative, weaving together elements of innovation, challenges, and a forward-looking vision for the advancement of vegetable quality assessment in a formal and academic context.