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...

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Vydané v:2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN) s. 372 - 377
Hlavní autori: Harbola, Gaurav, Rawat, Mahesh Singh, Gupta, Amit, Gupta, Richa
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 03.05.2024
Predmet:
Accuracy
Computational modeling
Computer architecture
Deep learning
DenseNet
Feature extraction
fine-tuning
Inception
InceptionV3
MobileNet
Social networking (online)
Transfer learning
VGG16
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Shrnutí: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