Mobile-based potato leaf disease identifier using ensemble modeling
Paper Details
Mobile-based potato leaf disease identifier using ensemble modeling
Abstract
Potato leaf diseases pose a significant threat to crop productivity, necessitating accurate, accessible, and real-time diagnostic solutions. This study proposes a mobile-based potato leaf disease identification system using ensemble modeling to improve classification accuracy and support early disease detection in agricultural environments. The system classifies seven categories, including six disease types—bacteria, fungi, nematode, pest, Phytophthora, and virus—and one healthy (normal) class. A dataset of 3,000 potato leaf images was utilized following the Knowledge Discovery in Databases (KDD) framework, including data selection, preprocessing, transformation, data mining, and evaluation. Deep feature extraction was performed using the Inception v3 convolutional neural network to generate high-dimensional image embeddings. These features were classified using Support Vector Machines (SVM) and further enhanced through a stacking-based ensemble approach to improve predictive performance. Experimental results show that the proposed model achieved an overall classification accuracy of 88% and a macro-averaged Area Under the Curve (AUC) of 0.92, demonstrating strong discriminative capability across all classes. The ensemble model outperformed individual classifiers, particularly in distinguishing visually similar disease categories. The system is designed for mobile deployment with both online and offline functionality, making it suitable for real-world agricultural applications, especially in resource-limited settings. This study highlights the effectiveness of integrating deep learning-based feature extraction with ensemble learning techniques for robust plant disease detection and scalable precision agriculture solutions.
Ahmed AA, Reddy GH. 2021. A mobile-based system for detecting plant leaf diseases using deep learning. AgriEngineering 3(3), 478–493.
Dolničar S. 2021. Understanding the importance of food crops in global sustainability and agricultural systems. Journal of Sustainable Agriculture 45(2), 123–135.
Fayyad U, Piatetsky-Shapiro G, Smyth P. 1996. The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 39(11), 27–34.
Iparraguirre-Villanueva O, Guevara-Ponce V, Roque Paredes O, Sierra-Liñan F, Zapata-Paulini J, Cabanillas-Carbonell M. 2022. Convolutional neural networks with transfer learning for pneumonia detection. International Journal of Advanced Computer Science and Applications 13(9), 544–551. https://doi.org/10.14569/IJACSA.2022.0130963
Jafar A, Bibi N, Naqvi RA, Sadeghi-Niaraki A, Jeong D. 2024. Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations. Frontiers in Plant Science 15, 1356260. https://doi.org/10.3389/fpls.2024.1356260
Jha P, Dembla D, Dubey W. 2024. Implementation of machine learning classification algorithm based on ensemble learning for detection of vegetable crops disease. International Journal of Advanced Computer Science and Applications 15(1). https://doi.org/10.14569/IJACSA.2024.0150157
Muzammil Khan M, Ahmad S, Ali R, Hussain T. 2024. Deep learning-based detection of potato leaf diseases using convolutional neural networks. Computers and Electronics in Agriculture 213, 108264.
Shabrina S, Rahman MM, Islam MA. 2023. A labeled dataset for potato leaf disease classification using image-based analysis. Data in Brief 48, 109200.
University of Regina. n.d. Overview of the KDD process. https://www2.cs.uregina.ca/~dbd/cs831/notes/kdd/1_kdd.html
Karen W. Cantilang*, Laarni M. Ladiao, 2026. Mobile-based potato leaf disease identifier using ensemble modeling. J. Biodiv. Environ. Sci., 28(4), 58-64.
Copyright © 2026 by the Authors. This article is an open access article and distributed under the terms and conditions of the Creative Commons Attribution 4.0 (CC BY 4.0) license.