Tomato plant disease detection using transfer learning with C-GAN synthetic images

• Published in: Computers and electronics in agriculture Vol. 187; p. 106279
• Main Authors: Abbas, Amreen, Jain, Sweta, Gour, Mahesh, Vankudothu, Swetha
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2021; Elsevier BV
• Subjects: Conditional Generative Adversarial Network; Data augmentation; Deep learning; Generative adversarial networks; Image classification; Insects; Machine learning; Medical imaging; Plant diseases; Pre-trained DesnseNet121 network; Synthetic data; Synthetic Images; Tomato plant disease detection; Tomatoes; Deep learning; Data augmentation; Conditional Generative Adversarial Network; Pre-trained DesnseNet121 network; Synthetic Images; Tomato plant disease detection
• ISSN: 0168-1699; 1872-7107
• DOI: 10.1016/j.compag.2021.106279

•We propose a deep learning‐based method for tomato plant disease detection.•We generate synthetic images using C‐GAN for data augmentation purposes.•A DenseNet121 model is trained on the original tomato leaf and synthetic images.•The proposed data augmentation technique improves network generalizability.•Proposed method achieves the best accuracy of 99.51% for 5‐class classification. Plant diseases and pernicious insects are a considerable threat in the agriculture sector. Therefore, early detection and diagnosis of these diseases are essential. The ongoing development of profound deep learning methods has greatly helped in the detection of plant diseases, granting a vigorous tool with exceptionally precise outcomes but the accuracy of deep learning models depends on the volume and the quality of labeled data for training. In this paper, we have proposed a deep learning-based method for tomato disease detection that utilizes the Conditional Generative Adversarial Network (C-GAN) to generate synthetic images of tomato plant leaves. Thereafter, a DenseNet121 model is trained on synthetic and real images using transfer learning to classify the tomato leaves images into ten categories of diseases. The proposed model has been trained and tested extensively on publicly available PlantVillage dataset. The proposed method achieved an accuracy of 99.51%, 98.65%, and 97.11% for tomato leaf image classification into 5 classes, 7 classes, and 10 classes, respectively. The proposed approach shows its superiority over the existing methodologies.