Plant Leaf Disease Detection Using Deep Learning: A Multi-Dataset Approach

Manjunatha Shettigere Krishna, Pedro Machado, Richard I. Otuka, Salisu W. Yahaya, Filipe Neves dos Santos and Isibor Kennedy Ihianle ()
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Manjunatha Shettigere Krishna: Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK
Pedro Machado: Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK
Richard I. Otuka: Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK
Salisu W. Yahaya: Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK
Filipe Neves dos Santos: INESC TEC, Campus da FEUP, Rua Dr Roberto Frias, 4200-465 Porto, Portugal
Isibor Kennedy Ihianle: Department of Computer Science, Nottingham Trent University, Nottingham NG1 4FQ, UK

J, 2025, vol. 8, issue 1, 1-24

Abstract: Agricultural productivity is increasingly threatened by plant diseases, which can spread rapidly and lead to significant crop losses if not identified early. Detecting plant diseases accurately in diverse and uncontrolled environments remains challenging, as most current detection methods rely heavily on lab-captured images that may not generalise well to real-world settings. This paper aims to develop models capable of accurately identifying plant diseases across diverse conditions, overcoming the limitations of existing methods. A combined dataset was utilised, incorporating the PlantDoc dataset with web-sourced images of plants from online platforms. State-of-the-art convolutional neural network (CNN) architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, were employed and fine-tuned for plant leaf disease classification. A key contribution of this work is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. The results demonstrated varied performance across the datasets. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved an accuracy of 73.31%. In cross-dataset evaluation, where the model was trained on PlantDoc and tested on a web-sourced dataset, EfficientNet-B3 reached 76.77% accuracy. The best performance was achieved with the combination of the PlanDoc and web-sourced datasets resulting in an accuracy of 80.19% indicating very good generalisation in diverse conditions. Class-wise F1-scores consistently exceeded 90% for diseases such as apple rust leaf and grape leaf across all models, demonstrating the effectiveness of this approach for plant disease detection.

Keywords: plant disease; machine learning (ML); deep learning (DL); precision farming; DenseNet; ResNet; EfficientNet; convolutional neural network (CNN) (search for similar items in EconPapers)
JEL-codes: I1 I10 I12 I13 I14 I18 I19 (search for similar items in EconPapers)
Date: 2025
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