A quality-based sustainable supply chain architecture for perishable products using image processing in the era of industry 4.0.

In India, approximately one-third of agricultural produce is wasted every year due to the different issues present in the post-harvest supply chain stages. The supply chain management of perishable products becomes complex and challenging due to the inclusion of its perishability dynamics. Quality is the main factor that governs the buying or discarding of perishable products by consumers. Therefore, the main aim of this research work is to develop an accurate and efficient image processing model for the classification of the product (Tomato) based on its quality for managing the supply chain. There are three novelties in this research work. A four-stage supply chain architecture integrated with the image processing system at mandi, and warehouses is proposed (First). This image processing system is developed in two stages. In stage I, the acquired images of tomato during its life cycle are labelled with the help of machine learning algorithms (Second). This labelled data is used in stage II for the development of a classification model to segregate the product into various grades. For this, an optimized architecture of seven-layer Convolutional Neural Network (CNN) model is developed followed by optimization of its hyperparameters simultaneously using Design of Experiments (DOE) technique (Third). The optimized CNN model achieved maximum accuracy of 88.40% and reported an execution time of 7 min. Further, the results of standard hyperparameter optimization techniques like Grid search, Random search, Bayesian, and Hyperband are compared with the proposed DOE technique on the optimized CNN architecture. The work done in this paper enables the supply chain managers to take accurate and rapid decisions for pricing, procurement, storage, and transportation at various stages of the supply chain leading to Industry 4.0. This will result in reduced post-harvest losses and simultaneously achieve the benefits across social, economic, and environmental dimensions of sustainability leading to better supply chain management. • A supply chain architecture integrated with image processing system is proposed. • Decisions related to pricing, procurement, storage and transportation can be taken. • The quality of product is modelled and labelled using Machine Learning algorithms. • A CNN model is developed for quality-based classification and optimized using DoE. • A comparison of DoE with Grid search, Bayesian and Hyperband is presented. [ABSTRACT FROM AUTHOR]