A data-driven approach to turmeric disease detection: Dataset for plant condition classification

Abstract

Turmeric, Curcuma longa, is an economically and medicinally important crop. However, the crop has often suffered from diseases such as rhizome disease roots, leaf blotch, and dry conditions of leaves. The control of these diseases essentially requires early and accurate diagnosis to reduce losses and help farmers adopt sustainable farming methods. The conventional methods of diagnosis involve a visual examination of symptoms, which is laborious, subjective, and rather impossible in large areas. This paper proposes a new dataset consisting of 1037 originals and 4628 augmented images of turmeric plants representing five classes: healthy leaf, dry leaf, leaf blotch, rhizome disease roots, and rhizome healthy roots. The dataset was pre-processed to enhance its applicability to deep learning applications by resizing, cleaning, and augmenting the data through flipping, rotation, and brightness adjustment. The turmeric plant disease classification was conducted using the Inception-v3 model, attaining an accuracy of 97.36% with data augmentation, compared to 95.71% without augmentation. Some of the major key performance metrics are precision, recall, and F1-score, which establish the efficacy and robustness of the model. This work attempts to show the potential of AI-aided solutions towards precision farming and sustainable crop production in developing agriculture disease management. The publicly available dataset and the results obtained are expected to attract more research interest for innovations in AI-driven agriculture.