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Intelligent Grapevine Disease Detection Using IoT Sensor Network.

Mihaela Hnatiuc1, Simona Ghita1, Domnica Alpetri1

  • 1Electronic and Telecommunication Departament, Constanta Maritime University, 104 Mircea cel Batran, 900663 Constanta, Romania.

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|September 28, 2023
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Summary
This summary is machine-generated.

This study introduces an intelligent grapevine disease detection system using Internet of Things (IoT) sensors and machine learning. The system accurately identifies grapevine diseases by analyzing environmental and plant data, achieving a 0.05 classification error rate.

Keywords:
IoT systemdata correlationdisease identificationprediction algorithmsensors

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Area of Science:

  • Agricultural Technology
  • Viticulture
  • Environmental Monitoring

Background:

  • The Internet of Things (IoT) is increasingly vital in agriculture for precision management.
  • IoT applications in viticulture enable automated disease monitoring and prevention.
  • Early detection of grapevine diseases is crucial for crop yield and quality.

Purpose of the Study:

  • To develop an intelligent grapevine disease detection method using IoT sensor networks.
  • To identify key environmental and plant parameters for early disease prediction.
  • To compare IoT sensor data with classical disease identification methods.

Main Methods:

  • Deployment of an IoT sensor network in Romanian vineyards.
  • Collection of environmental and plant-related data.
  • Analysis of sensor data using Machine Learning (ML) algorithms, including a feedforward neural network.
  • Correlation of ML results with classical disease identification methods.

Main Results:

  • Identification of critical parameters for early grapevine health assessment.
  • A classification system error rate of 0.05 was achieved using a feedforward neural network.
  • Disease occurrence was correlated with specific environmental parameters.

Conclusions:

  • The developed IoT system effectively supports intelligent grapevine disease detection.
  • ML algorithms integrated with sensor data show promise for predicting grapevine diseases.
  • Further testing in diverse vineyard regions is planned to validate the system's robustness.