Faculty of Agronomy in Čačak, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia.
Institute of Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany.
Sensors (Basel). 2024 Sep 14;24(18):5965. doi: 10.3390/s24185965.
The widespread use of IoT devices has led to the generation of a huge amount of data and driven the need for analytical solutions in many areas of human activities, such as the field of smart agriculture. Continuous monitoring of crop growth stages enables timely interventions, such as control of weeds and plant diseases, as well as pest control, ensuring optimal development. Decision-making systems in smart agriculture involve image analysis with the potential to increase productivity, efficiency and sustainability. By applying Convolutional Neural Networks (CNNs), state recognition and classification can be performed based on images from specific locations. Thus, we have developed a solution for early problem detection and resource management optimization. The main concept of the proposed solution relies on a direct connection between Cloud and Edge devices, which is achieved through Fog computing. The goal of our work is creation of a deep learning model for image classification that can be optimized and adapted for implementation on devices with limited hardware resources at the level of Fog computing. This could increase the importance of image processing in the reduction of agricultural operating costs and manual labor. As a result of the off-load data processing at Edge and Fog devices, the system responsiveness can be improved, the costs associated with data transmission and storage can be reduced, and the overall system reliability and security can be increased. The proposed solution can choose classification algorithms to find a trade-off between size and accuracy of the model optimized for devices with limited hardware resources. After testing our model for tomato disease classification compiled for execution on FPGA, it was found that the decrease in test accuracy is as small as 0.83% (from 96.29% to 95.46%).
物联网设备的广泛应用产生了大量的数据，推动了人类活动诸多领域（如智能农业领域）对分析解决方案的需求。对作物生长阶段的持续监测可以实现及时干预，例如控制杂草和植物病害以及虫害防治，从而确保最佳的生长发育。智能农业中的决策系统涉及到图像分析，具有提高生产力、效率和可持续性的潜力。通过应用卷积神经网络（CNN），可以根据特定位置的图像进行状态识别和分类。因此，我们开发了一种用于早期问题检测和资源管理优化的解决方案。该解决方案的主要概念依赖于云和边缘设备之间的直接连接，这是通过雾计算实现的。我们的工作目标是创建一个用于图像分类的深度学习模型，该模型可以针对雾计算设备上有限的硬件资源进行优化和适配。这可以提高图像处理在降低农业运营成本和人工劳动方面的重要性。通过在边缘和雾设备上处理卸载数据，可以提高系统的响应能力，降低与数据传输和存储相关的成本，并提高整体系统的可靠性和安全性。所提出的解决方案可以选择分类算法，在针对有限硬件资源的设备进行优化的模型的大小和准确性之间找到平衡点。在对针对 FPGA 执行的番茄疾病分类测试模型进行测试后，发现测试准确性的下降幅度很小，仅为 0.83%（从 96.29%降至 95.46%）。
