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

TY - CONF

T1 - AgriScanNet-18: A Robust Multilayer CNN for Identification of Potato Plant Diseases

AU - Boudjadar, Jalil

AU - Islam, Saif ul

N1 - Conference code: 9

PY - 2024

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

UR - http://www.scopus.com/inward/record.url?scp=85192200365&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-47724-9_20

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

M3 - Paper

SP - 291

EP - 308

T2 - Intelligent Systems Conference

Y2 - 7 September 2023 through 8 September 2023

ER -