Intelligent Diagnosis of Potato Leaf Diseases using Deep Learning

Potato cultivation faces significant threats from plant diseases, such as early blight and late blight, which detrimentally impact yield and quality. Addressing this challenge, this study presents a novel Convolutional Neural Network (CNN) model designed for automated disease detection in potato pla...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) S. 372 - 377
Hauptverfasser: Harbola, Gaurav, Rawat, Mahesh Singh, Gupta, Amit, Gupta, Richa
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 03.05.2024
Schlagworte:
Accuracy
Computational modeling
Computer architecture
Deep learning
DenseNet
Feature extraction
fine-tuning
Inception
InceptionV3
MobileNet
Social networking (online)
Transfer learning
VGG16
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Potato cultivation faces significant threats from plant diseases, such as early blight and late blight, which detrimentally impact yield and quality. Addressing this challenge, this study presents a novel Convolutional Neural Network (CNN) model designed for automated disease detection in potato plants. Leveraging advanced CNN architectures, particularly MobileNet, this study achieves a remarkable accuracy of 96.6% in disease classification tasks. This innovative approach eliminates the need for manual inspection processes, offering a scalable and efficient solution for early disease identification and mitigation. Finally, this study highlights the transformative potential of deep learning methodologies in enhancing agricultural productivity and disease management strategies.
DOI:10.1109/ICPCSN62568.2024.00065