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Automating Citrus Budwood Processing for Downstream Pathogen Detection Through Instrument Engineering
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Mobile application using DCDM and cloud-based automatic plant disease detection.

Parasuraman Kumar1, Srinivasan Raghavendran2,3, Karunagaran Silambarasan1

  • 1Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekaptti, Tirunelveli, Tamilnadu, 627 012, India.

Environmental Monitoring and Assessment
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

Accurate plant disease detection using deep learning models can significantly reduce crop loss. This farm-based module offers real-time diagnosis with 98.78% accuracy, aiding agricultural productivity.

Keywords:
Agricultural product sectorCloud data centerCloud storageDeepLens Classification and Detection Model (DCDM)Mobile cameraPlant diseases

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Data Science

Background:

  • Plant diseases cause significant annual losses to global agricultural produce, estimated at over 18%.
  • Traditional methods for plant disease detection are often inefficient, uncertain, and labor-intensive.
  • There is a critical need for advanced, automated techniques to monitor plant health and identify diseases early.

Purpose of the Study:

  • To develop and evaluate an automated farm-based module for accurate plant disease diagnosis.
  • To leverage cloud computing and deep learning for real-time monitoring and management of farm data.
  • To improve agricultural productivity and reduce crop losses through early disease identification.

Main Methods:

  • Implementation of a farm-based module with cloud data centers and data conversion devices.
  • Utilizing mobile cameras and bots for image acquisition of plant health status.
  • Development of a DCDM deep learning model trained on 40,000 images, with analysis of 10,000 images for disease classification.

Main Results:

  • Real-time diagnosis of plant leaf diseases achieved 98.78% accuracy in a laboratory setting.
  • A cloud-based image diagnostic and classification service provided results in an average of 0.349 seconds.
  • The system successfully recorded plant name, disease type, and images in a cloud database.

Conclusions:

  • The proposed automated system significantly enhances the accuracy and speed of plant disease detection.
  • Cloud-based image analysis and deep learning models offer a scalable solution for agricultural monitoring.
  • This technology has the potential to boost productivity in the agricultural and irrigation sectors by minimizing disease-related losses.