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Consumers today use green, fresh, perishable products because of their freshness, healthfulness, and sustainability. In this article, we developed the continuous production inventory model for the producer who produces and sells fresh perishable products with the input of green efforts. There are two distinct kinds of product decay to take into account: products whose physical condition gradually deteriorates over time at a constant rate, and products whose freshness quality declines with time. Product demand is influenced by the selling price of the product, its freshness level, and its greening efforts. In order to increase the sales of inventory and enhance the profit from clearing stocks at the end of their life, we have adopted the markdown policy. Due to freshness degradation, a markdown strategy is adopted after a period of product deterioration to boost demand. The objectives of the study are to find out the optimal period for replenishment cycle time, the optimal value of greening efforts, and the optimal markdown percentage such that the producer’s total profit is maximum. A numerical example is used to validate a mathematical formulation that reflects real-world circumstances. A sensitivity analysis of the parameters is done to determine the model's reliability. Some significant managerial results are provided, and the article concludes with a consideration of the future scope of related research.

Chicken is one of the staple foods that is widely enjoyed by all. To obtain the benefits of chicken meat, the level of freshness becomes one of the main keys. In general, the level of freshness of chicken meat is divided into two classes, namely fresh and non-fresh. The difference in the level of freshness can be seen from the color changes of each class. Spoiled chicken (chicken died yesterday) is one type of meat in the non-fresh group. The widespread sale of spoiled chicken meat among the public raises doubts about choosing chicken that is suitable and unsuitable for consumption. Therefore, chicken meat freshness classification is needed to facilitate the selection of chicken meat based on color characteristics. The use of Naive Bayes Classifier algorithm in categorizing fresh and non-fresh classes is done by calculating the probability value of each image channel input. This research was conducted to compare the Naive Bayes, decision tree, and K-NN algorithms in classifying chicken meat based on color characteristics. The results of the study showed that the Naive Bayes classifier algorithm was superior to the decision tree and K-NN algorithms with an accuracy rate of 75%, precision of 79%, and recall of 65%. It is known that 27 images were predicted correctly and 9 images were predicted incorrectly out of a total 36 data. The use of a histogram in this study aims to differentiate chicken meat images from non-meat during the testing process of the model using the Naive Bayes classifier algorithm.