Real-time disease detection on bean leaves from a small image dataset using data augmentation and deep learning methods: Real-time disease detection on bean leaves from a small image dataset using data augmentation..: E. Karantoumanis et al.

Disease detection in agricultural crops plays a pivotal role in ensuring food security and sustainable farming practices. Deep learning models, known for their ability in image analysis, often demand extensive image datasets and annotations to achieve high accuracy. However, in the case of bean crops, the absence of a publicly available dataset has posed a significant challenge for applying deep learning algorithms to accurately predict diseases. Additionally, the manual annotation of images demands substantial time and resources. This paper introduces an innovative approach to tackle these issues. We introduce a solution for real-time disease detection on bean leaves, despite the lack of bean-specific image data. Initially, we generate a small dataset from real images and annotate them. Then, we utilize images from the existing dataset PlantDoc (Singh et al. in: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Association for Computing Machinery, pp 249–253, 2020) from leaves of other plant species. Moreover, to compensate for the limitations of a small image dataset, we employ advanced data augmentation techniques, enriching the training data and enhancing the model’s ability to generalize. Our experimental study shows that data augmentation techniques can improve the accuracy of deep learning methods by up to 37%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.