Siam A K M Fazlul Kobir, Nirob Md Asraful Sharker, Bishshash Prayma, Ghosh Apurba, Noori Sheak Rashed Haider
Department of CSE, Daffodil International University, Bangladesh.
Data Brief. 2025 Feb 27;59:111435. doi: 10.1016/j.dib.2025.111435. eCollection 2025 Apr.
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
姜黄，即姜黄属植物，是一种具有经济和药用价值的重要作物。然而，这种作物经常遭受根茎病、叶斑病和叶片干枯等病害。控制这些病害本质上需要早期准确的诊断，以减少损失，并帮助农民采用可持续的种植方法。传统的诊断方法包括对症状进行目视检查，这种方法费力、主观，而且在大面积区域几乎无法实施。本文提出了一个新的数据集，该数据集由1037张原始图像和4628张增强图像组成，这些图像代表了姜黄植株的五类情况：健康叶片、干枯叶片、叶斑病、根茎病根部和根茎健康根部。对该数据集进行了预处理，通过调整大小、清理以及通过翻转、旋转和亮度调整对数据进行增强，以提高其对深度学习应用的适用性。使用Inception-v3模型进行姜黄植株病害分类，在进行数据增强时准确率达到97.36%，而未进行增强时准确率为95.71%。一些主要的关键性能指标是精确率、召回率和F1分数，这些指标确立了模型的有效性和稳健性。这项工作试图展示人工智能辅助解决方案在发展农业病害管理中对精准农业和可持续作物生产的潜力。公开可用的数据集以及所获得的结果有望吸引更多对人工智能驱动农业创新的研究兴趣
