Department of Food Engineering and Tea Technology, Shahjalal University of Science and Technology, Sylhet, Bangladesh.
Department of Computer Science and Engineering, Shahjalal University of Science and Technology, Sylhet, Bangladesh.
Sci Rep. 2024 Jun 18;14(1):14097. doi: 10.1038/s41598-024-62058-3.
Globally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling the spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy. This study focuses on a pragmatic approach for automatically detecting selected tea foliage diseases based on convolutional neural network (CNN). A large dataset of 3330 images has been created by collecting samples from different regions of Sylhet division, the tea capital of Bangladesh. The proposed CNN model is developed based on tea leaves affected by red rust, brown blight, grey blight, and healthy leaves. Afterward, the model's prediction was validated with laboratory tests that included microbial culture media and microscopic analysis. The accuracy of this model was found to be 96.65%. Chiefly, the proposed model was developed in the context of the Bangladesh tea industry.
从全球范围来看，茶叶的产量和质量从根本上取决于茶叶，而茶叶容易受到病原生物的侵害。准确、早期地识别植物叶片病害是防止和控制阻碍产量和质量的病害传播的关键因素。图像处理技术是农业领域中一种快速发展的工具，可用于高精度地检测各种疾病。本研究基于卷积神经网络（CNN），提出了一种实用的自动检测选定茶叶叶片病害的方法。通过从孟加拉国茶叶之都锡尔赫特地区收集样本，创建了一个包含 3330 张图像的大型数据集。该模型是基于受红锈、褐腐病、灰霉病和健康叶片影响的茶叶构建的。之后，使用包括微生物培养基和显微镜分析在内的实验室测试对模型的预测进行了验证。该模型的准确率达到了 96.65%。该模型主要是在孟加拉国茶叶产业的背景下开发的。
