Cyber Physical Systems (CPS) totally revolutionized the way we interact with the world providing useful services that can support the human being in many aspects of his life. Artificial Intelligence (AI) is another important player for bringing intelligence to CPS and allows the realization of Intelligent Cyber Physical Systems where smart applications can run. However, the constrained hardware of these devices in terms of memory and computing power makes challenging the deployment and execution of powerful algorithms (e.g., deep neural networks). To address this problem, modern solutions involve the use of compression techniques to reduce the memory footprint of deep learning models while saving the accuracy performance. The proposed work focuses on plant disease detection which represents one of the biggest challenges in smart agriculture; in such a context, the possibility to perform a timely diagnosis on crops suspected to be infected can avoid the spread of diseases, thus saving a lot of time and money during the plantation works. In this paper, we realized an intelligent CPS on top of which we implemented an AI application, called Deep Leaf that exploits Convolutional Neural Networks to detect the main biotic stresses affecting crops. To meet the hardware requirements of the Edge device running our application, we propose a novel dynamic compression algorithm based on K-Means for the reduction of models footprint. Experimental results show that our detector is able to correctly classify the plant health condition with an accuracy of 95% and demonstrate the effectiveness of the proposed compression algorithm which is able to maintain the same accuracy of the original 32 bit float model, with an overall memory size reduction of about 85.2%.

网络物理系统 (CPS) 彻底改变了我们与世界互动的方式，提供有用的服务，可以在人类生活的许多方面为人类提供支持。人工智能 (AI) 是将智能引入 CPS 的另一个重要参与者，并允许实现可以运行智能应用程序的智能网络物理系统。然而，这些设备在内存和计算能力方面的受限硬件使得强大算法（例如，深度神经网络）的部署和执行具有挑战性。为了解决这个问题，现代解决方案涉及使用压缩技术来减少深度学习模型的内存占用，同时保持准确度性能。拟议的工作重点是植物病害检测，这是智能农业面临的最大挑战之一；在这种情况下，对疑似感染的作物进行及时诊断的可能性可以避免疾病的传播，从而在种植工作中节省大量时间和金钱。在本文中，我们实现了一个智能 CPS，在此基础上我们实现了一个 AI 应用程序，称为Deep Leaf利用卷积神经网络检测影响作物的主要生物胁迫。为了满足运行我们应用程序的 Edge 设备的硬件要求，我们提出了一种基于 K-Means 的新型动态压缩算法，以减少模型占用空间。实验结果表明，我们的检测器能够以 95% 的准确率正确分类植物健康状况，并证明了所提出的压缩算法的有效性，该算法能够保持与原始 32 位浮点模型相同的准确度，并具有整体记忆尺寸减少约 85.2%。