Citation

Shah, Said Khalid and Mohd Su'ud, Mazliham and Khan, Aurangzeb and Alam, Muhammad Mansoor and Ayaz, Muhammad (2025) PLDC‐Net: Potato Leaf Disease Classification Network Based on an Efficient Convolutional Neural Network. Engineering Reports, 7 (7). ISSN 2577-8196

Text PLDC‐Net_ Potato Leaf Disease Classification Network Based on an Efficient Convolutional Neural Network.pdf - Published Version Restricted to Repository staff only Download (23MB)

Abstract

Agriculture plays a key role in the economic growth of a country, and it is a primary source of food production for both humans and animals. Plant diseases, on the other hand, are a major issue that have a significant impact on agriculture. The yearly loss of productivity in agriculture due to these diseases is ∼25%. Detecting plant diseases quickly and accurately is crucial for improving crop yields. In this study, a novel approach named PLDC-Net was developed based on convolutional neural networks (CNNs) with transfer learning to detect multiple diseases of potato crops. The proposed study focused on data balancing, a key aspect of deep learning (DL) useful for improved accuracy and generalization, as usually imbalanced data hinders model generalization. To train the suggested approach, a large set of photos was collected from various online resources, and the balance between the target classes was ensured. Pre-processing and data augmentation techniques were applied before training the model to enhance generalization and reduce overfitting. A pre-trained model, EfficientNet-B1, was used as the backbone of the model to extract the high-level features of leaf images and was fine-tuned with two dense layers, followed by SVM as the output layer for disease type identification. The model was evaluated on another set of unseen images with 98.39% average accuracy. The proposed network may provide a reliable and effective way of identifying potato diseases, hence ensuring food security and reducing agricultural financial losses. This work highlights how CNN algorithms can be fine-tuned and employed to categorize potato illnesses, thereby enabling automatic and effective control of diseases in potato agricultural production.

Item Type:	Article
Uncontrolled Keywords:	Deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	29 Jul 2025 05:06
Last Modified:	29 Jul 2025 05:06
URII:	http://shdl.mmu.edu.my/id/eprint/14378

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