PDF(Pubmed)
Abstract：
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.
摘要：
物联网（IoT）在农业中具有重要意义，利用遥感和机器学习帮助农民做出高精度的管理决策。这项技术可以应用于葡萄栽培，使监测疾病发生并自动预防成为可能。本研究旨在实现一种智能葡萄病害检测方法,使用收集环境和植物相关数据的物联网传感器网络。这项研究的重点是确定提供有关葡萄树健康的早期信息的主要参数。传感器网络的概述，architecture,并提供了组件。物联网传感器系统部署在位于Murfatlar的葡萄栽培和Enology研究站（SDV）种植园内的实验区中，罗马尼亚。用于疾病识别的经典方法也应用于该领域，为了将它们与传感器数据进行比较，从而改进了葡萄病害识别算法。使用机器学习（ML）算法分析来自传感器的数据，并将其与使用经典方法获得的结果相关联，以识别和预测葡萄树疾病。疾病发生的结果与相应的环境参数一起显示。分类系统的错误，使用前馈神经网络，为0.05。这项研究将继续进行，从位于其他地区的葡萄园中测试的物联网传感器获得的结果。
