Skip to main content
Log in

A stacked deep learning approach for multiclass classification of plant diseases

  • Research Article
  • Published:
Plant and Soil Aims and scope Submit manuscript



Plant diseases are one of the main factors affecting food production and reducing production losses, they must be swiftly identified and treated. Deep learning algorithms in association with computer vision techniques have recently found usage in the diagnosis of plant diseases, offering a potent tool with highly accurate results. The objective of this study is to identify a stacking ensemble-based solution by using several algorithms in the process of classifying and diagnosing plant diseases, describing trends, and emphasizing gaps.


The stacking ensemble is made using top four performing deep learning algorithms and multi-layered perceptron as meta classifier. In this regard, we reviewed more than 15 studies from the previous three years that address problems with disease detection, dataset characteristics, researched crops, and pathogens in various ways.


The proposed ensemble model achieved a maximum accuracy of 98.13% compared to the conventional architectures. For comparing the results, various performance metrics are used such as accuracy, loss, precision etc. which outperformed the results of the deep learning algorithms run separately for the data as shown in Table 5.


The suggested framework can help identify the presence of disease in a sample of plant leaves as a preventative strategy as the results were quite promising compared to the results of existing literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


Download references





Author information

Authors and Affiliations



AS: Conceptualization, validation, writing- original, Conceptualization, carrying out the work RD: Conceptualization, carrying out the work, writing reviewing, data analysis, investigation, data curation, and editing ARS: Conceptualization, carrying out the work, writing-reviewing, and editing.

Corresponding author

Correspondence to Rajni Mohana.

Ethics declarations

Conflict of interest

The authors have no relevant financial or nonfinancial interests to disclose. The author has declared no conflict of interest.

Additional information

Responsible Editor: Stephane Compant.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, A., Dalmia, R., Saxena, A. et al. A stacked deep learning approach for multiclass classification of plant diseases. Plant Soil (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: