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

Jalil Boudjadar; Saif ul Islam

doi:10.1007/978-3-031-47724-9_20

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

Jalil Boudjadar, Saif ul Islam

Department of Electrical and Computer Engineering - Software Engineering & Computing systems

Research output: Contribution to conference › Paper › Research › peer-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 language	English
Publication date	2024
Number of pages	18
DOIs	https://doi.org/10.1007/978-3-031-47724-9_20
Publication status	Published - 2024
Event	Intelligent Systems Conference - Amsterdam, Netherlands Duration: 7 Sept 2023 → 8 Sept 2023 Conference number: 9

Conference

Conference	Intelligent Systems Conference
Number	9
Country/Territory	Netherlands
City	Amsterdam
Period	07/09/2023 → 08/09/2023

Keywords

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

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10.1007/978-3-031-47724-9_20

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title = "AgriScanNet-18: A Robust Multilayer CNN for Identification of Potato Plant Diseases",

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.",

keywords = "Computer Vision, Deep Learning, Potato Leaf Diseases, Receiver Operating Characteristic, Residual Neural Network, Visual Geometry Group",

author = "Jalil Boudjadar and Islam, {Saif ul}",

year = "2024",

doi = "10.1007/978-3-031-47724-9_20",

language = "English",

pages = "291--308",

note = "Intelligent Systems Conference, Intellisys ; Conference date: 07-09-2023 Through 08-09-2023",

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AU - Boudjadar, Jalil

AU - Islam, Saif ul

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Y1 - 2024

N2 - 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.

AB - 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.

KW - Computer Vision

KW - Deep Learning

KW - Potato Leaf Diseases

KW - Receiver Operating Characteristic

KW - Residual Neural Network

KW - Visual Geometry Group

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

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Keywords

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