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.

中文翻译：

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工业4.0时代利用图像处理构建基于质量的易腐产品可持续供应链架构

在印度，由于收获后供应链阶段存在的不同问题，每年约有三分之一的农产品被浪费。由于包含易腐烂动态，易腐产品的供应链管理变得复杂且具有挑战性。质量是控制消费者购买或丢弃易腐烂产品的主要因素。因此，这项研究工作的主要目的是开发一种准确、高效的图像处理模型，用于根据产品（番茄）的质量进行分类，以管理供应链。这项研究工作有三个新颖之处。提出了与 mandi 的图像处理系统和仓库集成的四阶段供应链架构（第一）。该图像处理系统分两个阶段开发。在第一阶段，借助机器学习算法对番茄生命周期中获取的图像进行标记（第二）。该标记数据在第二阶段用于开发分类模型，以将产品分为不同的等级。为此，开发了七层卷积神经网络（CNN）模型的优化架构，然后使用实验设计（DOE）技术同时优化其超参数（第三）。优化后的 CNN 模型达到了 88.40% 的最大准确率，执行时间为 7 分钟。此外，还将网格搜索、随机搜索、贝叶斯和 Hyperband 等标准超参数优化技术的结果与优化 CNN 架构上提出的 DOE 技术进行了比较。本文所做的工作使供应链管理者能够在通向工业 4.0 的供应链各个阶段对定价、采购、存储和运输做出准确、快速的决策。这将减少收获后损失，同时实现可持续发展的社会、经济和环境方面的效益，从而实现更好的供应链管理。