Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Kongresse
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
Elektrische Antriebe und Energiesysteme 2025 | 26 + 27 März 2025

19. Internationaler MTZ-Fachkongress Zukunftsantriebe

Seien Sie bei der Konferenz dabei! Dort treffen Experten aus der Automobilindustrie, Energieerzeugung und Stromnetzbetrieb auf Vertreter aus Politik und Wissenschaft. Diskutiert werden wichtige Themen der Branche.
Erweiterte Suche
Anmelden
nach oben
Annals of Data Science
Erschienen in:

13.07.2024

Apple Leaf Disease Detection Using Transfer Learning

verfasst von: Ozair Ahmad Wani, Umer Zahoor, Syed Zubair Ahmad Shah, Rijwan Khan

Erschienen in: Annals of Data Science | Ausgabe 1/2025

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant health, reduced production severely impacts the country’s economy. Traditional disease identification methods, relying on human experts, are slow, time-consuming, and impractical for large farms. Our proposed model utilizes a combination of pre-trained Resnet18, Alexnet, GoogLeNet, and VGG16 networks to classify apple tree leaves into categories such as healthy, black rot, apple cedar rust, and apple scab based on images. Various image enhancement techniques were employed to enhance the model’s accuracy. Ultimately, our model achieved an accuracy of 97.25% on the validation dataset, demonstrating excellent performance across various metrics. This suggests its potential for efficient and accurate plant health monitoring in the agricultural sector.
Vorheriger Artikel Partial Label Learning with Noisy Labels
Nächster Artikel Representing a Model for the Anonymization of Big Data Stream Using In-Memory Processing

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Shi Y (2022) Advances in big data analytics: theory, algorithm and practice. Springer, SingaporeCrossRef
2.
Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York
3.
Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef
4.
Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178CrossRef
5.
Khan A, Iqbal et al (2022) Deep diagnosis: a real-time apple leaf disease detection system based on deep learning. Comput Electron Agric 198:107093
6.
Jiang P et al (2019) Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7:59069–59080CrossRef
7.
Srinidhi VV, Sahay A, Deeba K (2021) Plant pathology disease detection in apple leaves using deep convolutional neural networks: apple leaves disease detection using efficientnet and densenet. 5th international conference on computing methodologies and communication (ICCMC). IEEE, 2021
8.
Chuanlei Z et al (2017) Apple leaf disease identification using genetic algorithm and correlation based feature selection method. Int J Agricultural Biol Eng 10(2):74–83
9.
Sinha A, Shekhawat RS (2019) Review of image processing approache for detecting plant diseases. IET Image Process 14:1427–1439CrossRef
10.
Ramesh S, Hebbar R, Niveditha M, Pooja R, Shashank N, Vinod PV (2018) Plant diseases detection using machine learning. In Proceedings of 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, India, 25–28 April; pp. 41–45. ]
11.
Bi C et al (2022) MobileNet based apple leaf diseases identification. Mob Networks Appl 1–9
12.
Singh S et al (2022) Deep learning based automated detection of diseases from apple leaf images. Computers Mater Continua 71:1
13.
Caglayan A (2018) Volumetric object recognition using 3-D CNNs on depth data. IEEE Access 6:20058–20066CrossRef
14.
Lu H et al (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Generation Comput Syst 82:142–148CrossRef
15.
DeChant C et al (2017) Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107.11:1426–1432
16.
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:215232CrossRef
17.
Fuentes A et al (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17.9:2022
18.
Ramcharan A, Baranowski K, Babuali Ahmed (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:293051CrossRef
19.
Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRef
20.
Waghmare H, Kokare R, Dandawate Y (2016) Detection and classification of diseases of grape plant using opposite colour local binary pattern feature and machine learning for automated decision support system. 3rd international conference on signal processing and integrated networks (SPIN). IEEE, 2016
21.
Oo Y, Min (2018) Plant leaf disease detection and classification using image processing. Int J Res Eng 5(9):516–523
22.
23.
Metadaten
Titel
Apple Leaf Disease Detection Using Transfer Learning
verfasst von
Ozair Ahmad Wani
Umer Zahoor
Syed Zubair Ahmad Shah
Rijwan Khan
Publikationsdatum
13.07.2024
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 1/2025
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-024-00555-y

Weitere Artikel der Ausgabe 1/2025

Parameter Estimation for Geometric Lévy Processes with Constant Volatility

Bayesian Estimation of Stress Strength Modeling Using MCMC Method Based on Outliers

A Two-Stage Analysis of Interaction Between Stock and Exchange Rate Markets: Evidence from Turkey

On Modeling Bivariate Lifetime Data in the Presence of Inliers

Non-negative Sparse Matrix Factorization for Soft Clustering of Territory Risk Analysis

A Novel Finite Mixture Model Based on the Generalized t Distributions with Two-Sided Censored Data

Premium Partner

    Bildnachweise