Department of Civil Engineering, University of Patras, 26500 Patra, Greece.
Department of Natural Resources and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece.
Sensors (Basel). 2021 Jan 5;21(1):314. doi: 10.3390/s21010314.
The health diagnosis of agricultural structures is critical to detecting damages such as cracks in concrete, corrosion, spalling, and delamination. Agricultural structures are susceptible to environmental degradation due to frequent exposure to water, organic effluent, farm chemicals, structural loading, and unloading. Various sensors have been employed for accurate and real-time monitoring of agricultural building structures, including electrochemical, ultrasonic, fiber-optic, piezoelectric, wireless, fiber Bragg grating sensors, and self-sensing concrete. The cost-benefits of each type of sensor and utility in a farm environment are explored in the review. Current literature suggests that the functionality of sensors has improved with progress in technology. Notable improvements made with the progress in technology include better accuracy of the measurements, reduction of signal-to-noise ratio, and transmission speed, and the deployment of machine learning, deep learning, and artificial intelligence in smart IoT-based agriculture. Key challenges include inconsistent installation of sensors in farm structures, technical constraints, and lack of support infrastructure, awareness, and preference for traditional inspection methods.
农业结构的健康诊断对于检测混凝土裂缝、腐蚀、剥落和分层等损坏至关重要。由于频繁接触水、有机废水、农用化学品、结构荷载和卸载，农业结构容易受到环境退化的影响。已经采用了各种传感器来对农业建筑结构进行精确和实时监测，包括电化学、超声、光纤、压电、无线、光纤布拉格光栅传感器和自感知混凝土。本综述探讨了每种类型传感器在农场环境中的成本效益和实用性。现有文献表明，随着技术的进步，传感器的功能得到了改善。技术进步带来的显著改进包括测量精度的提高、信噪比和传输速度的降低，以及在智能物联网农业中部署机器学习、深度学习和人工智能。主要挑战包括在农场结构中不一致地安装传感器、技术限制以及缺乏支持基础设施、意识和对传统检查方法的偏好。
