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.
