AgriScanNet-18: A Robust Multilayer CNN for Identification of Potato Plant Diseases

Jalil Boudjadar, Saif ul Islam

Research output: Contribution to conferencePaperResearchpeer-review

3 Citations (Scopus)

Abstract

This paper presents a novel approach for the early detection of potato plant diseases using deep learning techniques. The proposed method, AgriScanNet-18, is a multilayer convolutional neural network (CNN) that uses image-based analysis to identify various plant diseases. By training and evaluating the model on a potato leaf disease dataset, we achieved high accuracy of 99.30% for training and 99.28% for testing. Additionally, we developed a web app that facilitates the diagnosis of potato plant diseases by easily uploading images of leaves. In comparison with state-of-the-art models such as, VGG16, ResNet50, and VGG19, AgriScanNet-18 demonstrated improved identification accuracy of 8.66%, 3.61%, and 7.45%. In addition, Potato plant diseases can be managed and controlled using this technology to increase crop production and profitability.

Original languageEnglish
Publication date2024
Number of pages18
DOIs
Publication statusPublished - 2024
EventIntelligent Systems Conference - Amsterdam, Netherlands
Duration: 7 Sept 20238 Sept 2023
Conference number: 9

Conference

ConferenceIntelligent Systems Conference
Number9
Country/TerritoryNetherlands
CityAmsterdam
Period07/09/202308/09/2023

Keywords

  • Computer Vision
  • Deep Learning
  • Potato Leaf Diseases
  • Receiver Operating Characteristic
  • Residual Neural Network
  • Visual Geometry Group

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