Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas

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Published Mar 9, 2025
https://doi.org/10.55043/jaast.v9i2.308
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Vol. 9 No. 2 (2025): ARTICLES IN PRESS

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Verry Riyanto
Bina Sarana Informatika University
Sri Nurdiati
IPB University
Marimin Marimin
IPB University
Muhamad Syukur
IPB University
Shelvie Nidya Neyman
IPB University

Abstract

To identify novel advancements in plant diseases detection and classification systems employing Machine Learning (ML), Deep Learning (DL), and Transfer Learning (TL), this research compiled 111 peer-reviewed papers published between 2019 and early 2023. The literature was sourced from databases such as Scopus and Web of Science using keywords related to deep learning and leaf disease. A structured analysis of various plant disease classification models is presented through tables and graphics. This paper systematically reviews the model approaches employed, datasets utilized, countries involved, and the validation and evaluation methods applied in plant disease identification. Each algorithm is annotated with suitable processing techniques, such as image segmentation and feature extraction, along with standard experimental metrics, including the total number of training/testing datasets utilized, the quantity of disease images considered, and the classifier type employed. The findings of this study serve as a valuable resource for researchers seeking to identify specific plant diseases through a literature-based approach. Additionally, the implementation of mobile-based applications using the DL approach is expected to enhance agricultural productivity.

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Biographies

Verry Riyanto, Bina Sarana Informatika University

Department of Information System

Sri Nurdiati, IPB University

Department of Computer Science

Marimin Marimin, IPB University

Department of Computer Science

Muhamad Syukur, IPB University

Department of Computer Science

Shelvie Nidya Neyman, IPB University

Department of Computer Science

How to Cite
1.
Riyanto V, Nurdiati S, Marimin M, Syukur M, Neyman SN. Deep Learning Approaches for Plant Disease Diagnosis Systems: A Review and Future Research Agendas. J. appl. agricultural sci. technol. [Internet]. 2025Mar.9 [cited 2025Mar.25];9(2). Available from: https://jaast.org/index.php/jaast/article/view/308

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