Ensuring Fruits and Vegetables Freshness in Sustainable Agricultural Supply Chain Networks: A Deep Learning Approach

Ensuring fruits and vegetables’ freshness within a sustainable agricultural supply chain network (SASCN) is a critical and multifaceted challenge. This demands comprehensive solutions that integrate new technologies, sustainability philosophy, and effective management practices, to name a few. In this regard, computer vision (CV) and machine learning/deep learning (ML/DL) have increasingly been utilized in various sustainable agricultural areas, demonstrating significant efficacy in image analysis and processing. To this end, this research study proposes a novel approach leveraging advanced Deep learning techniques, including convolutional neural network (CNN) for fruit and vegetable quality detection and assessment throughout a SASCN. Therefore, an approach based on CNN is developed, utilizing a varied dataset that includes several types of fruit like Apples, Oranges, Bananas, and Grapes, alongside a single type of vegetable, namely Bitter-gourd. Notably, the proposed approach takes into consideration criteria, namely sustainability, and quality management thereby facilitating a holistic approach to fruit and vegetable quality control. The significant finding results in this study demonstrate the applicability of the developed approach for SASCN. After conducting thorough experimentation and assessment, the effectiveness of the developed approach attained an accuracy rate of 98.39%, indicating its high performance.