Skip to main content
Top

13-07-2024

Apple Leaf Disease Detection Using Transfer Learning

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

Published in: Annals of Data Science

Log in

Activate our intelligent search to find suitable subject content or patents.

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

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!

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!

Literature
1.
go back to reference Shi Y (2022) Advances in big data analytics: theory, algorithm and practice. Springer, SingaporeCrossRef Shi Y (2022) Advances in big data analytics: theory, algorithm and practice. Springer, SingaporeCrossRef
2.
go back to reference Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York
3.
go back to reference Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef
4.
go back to reference Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178CrossRef Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178CrossRef
5.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference Sinha A, Shekhawat RS (2019) Review of image processing approache for detecting plant diseases. IET Image Process 14:1427–1439CrossRef Sinha A, Shekhawat RS (2019) Review of image processing approache for detecting plant diseases. IET Image Process 14:1427–1439CrossRef
10.
go back to reference 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. ] 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.
go back to reference Bi C et al (2022) MobileNet based apple leaf diseases identification. Mob Networks Appl 1–9 Bi C et al (2022) MobileNet based apple leaf diseases identification. Mob Networks Appl 1–9
12.
go back to reference Singh S et al (2022) Deep learning based automated detection of diseases from apple leaf images. Computers Mater Continua 71:1 Singh S et al (2022) Deep learning based automated detection of diseases from apple leaf images. Computers Mater Continua 71:1
13.
go back to reference Caglayan A (2018) Volumetric object recognition using 3-D CNNs on depth data. IEEE Access 6:20058–20066CrossRef Caglayan A (2018) Volumetric object recognition using 3-D CNNs on depth data. IEEE Access 6:20058–20066CrossRef
14.
go back to reference Lu H et al (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Future Generation Comput Syst 82:142–148CrossRef 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.
go back to reference 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 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.
go back to reference Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:215232CrossRef Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:215232CrossRef
17.
go back to reference Fuentes A et al (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17.9:2022 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.
go back to reference Ramcharan A, Baranowski K, Babuali Ahmed (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:293051CrossRef Ramcharan A, Baranowski K, Babuali Ahmed (2017) Deep learning for image-based cassava disease detection. Front Plant Sci 8:293051CrossRef
19.
go back to reference Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRef Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318CrossRef
20.
go back to reference 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 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.
go back to reference Oo Y, Min (2018) Plant leaf disease detection and classification using image processing. Int J Res Eng 5(9):516–523 Oo Y, Min (2018) Plant leaf disease detection and classification using image processing. Int J Res Eng 5(9):516–523
Metadata
Title
Apple Leaf Disease Detection Using Transfer Learning
Authors
Ozair Ahmad Wani
Umer Zahoor
Syed Zubair Ahmad Shah
Rijwan Khan
Publication date
13-07-2024
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-024-00555-y

Premium Partner