Hnatiuc Mihaela, Ghita Simona, Alpetri Domnica, Ranca Aurora, Artem Victoria, Dina Ionica, Cosma Mădălina, Abed Mohammed Mazin
Electronic and Telecommunication Departament, Constanta Maritime University, 104 Mircea cel Batran, 900663 Constanta, Romania.
Murfatlar Research Station for Viticulture and Enology, 905100 Murfatlar, Romania.
Bioengineering (Basel). 2023 Aug 29;10(9):1021. doi: 10.3390/bioengineering10091021.
The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine's health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions.
物联网（IoT）在农业领域已变得至关重要，它利用遥感和机器学习帮助农民做出高精度的管理决策。这项技术可应用于葡萄栽培，能够监测病害发生并自动预防。本研究旨在利用物联网传感器网络实现一种智能葡萄病害检测方法，该网络可收集环境和植物相关数据。本研究的重点是识别能提供葡萄健康早期信息的主要参数。本文提供了传感器网络、架构及组件的概述。物联网传感器系统部署在罗马尼亚穆尔法特拉尔葡萄栽培与酿酒研究所（SDV）种植园内的试验地块。为了将其与传感器数据进行比较，从而改进葡萄病害识别算法，传统的病害识别方法也在实地应用。利用机器学习（ML）算法分析传感器数据，并将其与传统方法获得的结果相关联，以识别和预测葡萄病害。给出了病害发生结果以及相应的环境参数。使用前馈神经网络的分类系统误差为0.05。本研究将继续利用在其他地区葡萄园测试的物联网传感器所获得的结果。
