Abstract:
This study delves into the pivotal role of fruit and vegetable quality in influencing human
health, emphasizing the necessity of prioritizing fresh produce for optimal well-being.
Consumption of high-quality fruits and vegetables offers enhanced nutrition and
antioxidant benefits, contributing to overall health. Conversely, consuming spoiled
produce poses health risks due to exposure to harmful pathogens and potential loss of
essential nutrients. Deep learning emerges as a promising approach for assessing fruit and
vegetable quality, leveraging its capacity to discern nuanced characteristics from complex
datasets. Neural networks, particularly deep learning models, excel in automatically
identifying subtle visual cues indicative of freshness or decay. This technology facilitates
rapid and accurate categorization, essential for ensuring the quality and safety of produce
across various settings, from farms to retail outlets. In this research, we amalgamated and
analyzed two datasets to create a comprehensive dataset. Subsequently, we devised a new
labeling system and applied three image filters for thorough analysis. To classify images
as either good or rotten quality, we proposed a modified ResNet18 model, fine-tuned
through hyperparameter optimization. Our findings showcase the effectiveness of the
proposed model, achieving a remarkable 98% accuracy with an image size of 224x224 and
a batch size of 64 over ten epochs. This model holds potential for assisting health-conscious
individuals in discerning the quality of fruits and vegetables, thereby aiding in informed
dietary choices. Overall, this study underscores the significance of fruit and vegetable
quality in promoting human health and underscores the utility of deep learning in ensuring
produce safety and quality assessment.
