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AgroDetect-Smart Plant Pathology from Leaf Images
  • +2
  • Gulivindala Suresh,
  • Venkata Lalitha Narla,
  • Geetamma Tummalapalli,
  • Sravanthi Peddinti A,
  • Qasem M. Al-Mdallal
Gulivindala Suresh
Aditya Engineering College
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Venkata Lalitha Narla
Aditya Engineering College

Corresponding Author:[email protected]

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Geetamma Tummalapalli
GMR Institute of Technology Department of Electronics and Communication Engineering
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Sravanthi Peddinti A
Aditya Engineering College
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Qasem M. Al-Mdallal
United Arab Emirates University Department of Mathematical Sciences
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Abstract

AgroDetect Smart Plant Pathology is a Web-App-based solution and an innovative system that utilizes image processing techniques to analyze plant diseases based on leaf images. This technology aims to revolutionize agriculture by offering a rapid and precise method of identifying and managing plant diseases, ultimately improving crop yield and reducing losses. The Convolutional Neural Network (CNN) model, augmented with Rectified Linear Unit (ReLU) activation functions, was employed to discern the condition of plants by analyzing leaf images. The accuracy of the performance of the designed models is evaluated using four categories of plant leaves from the PlantVillage database. The average accuracy of the designed models is around 97% and is further compared with state-of-art works and also designed a web application using the Streamlit Python library for user interaction with minimal effort.
05 Feb 2025Submitted to Engineering Reports
06 Feb 2025Submission Checks Completed
06 Feb 2025Assigned to Editor
06 Feb 2025Review(s) Completed, Editorial Evaluation Pending
18 Feb 2025Reviewer(s) Assigned