The Methods for Vine Disease Identification

Hnatiuc, Mihaela; Hnatiuc, Bogdan; Sintea, Sorin; Ghiță, Simona; Ranca, Aurora; Artem, Victoria; Savin, Bogdan-Cristian

doi:10.1109/siitme53254.2021.9663649

Cited by 6 publications

(1 citation statement)

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“…In this study, we analyze the different possibilities of disease identification using different environmental parameters. After the data correlation, we can observe as the temperature and humidity of the air influence the disease occurrence [ 26 ]. The present project has an advantage in the comparison between data collected from the sensors and using agronomic protocol [ 19 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

Intelligent Grapevine Disease Detection Using IoT Sensor Network

Hnatiuc,

Ghita,

Alpetri

et al. 2023

Bioengineering

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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.

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Section: Discussionmentioning

confidence: 99%