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Tea-leaf Disease Identification Using Machine Learning

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dc.contributor.author Paul, Apurbo Kumar
dc.date.accessioned 2022-09-04T05:17:41Z
dc.date.available 2022-09-04T05:17:41Z
dc.date.issued 2022-01-02
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8592
dc.description.abstract Tea plants have a lengthy lifespan, which makes disease management challenges. Tea leaves are commonly afflicted by bacterial, viral, or fungal infections, which significantly decrease tea production. Identifying disease tea leaves is critical to sustaining worldwide tea demand for a vast population. Early disease detection can assist in reducing losses. However, disease tea leaf detection was limited to image backgrounds and image-capture conditions. In the field of disease tea leaf identification, the convolutional neural network (CNN)-based model is the most popular topic of research. However, existing some CNNbased models have low recognition rates on independent datasets and are limited to learning large-scale network parameters. We proposed a novel CNN-based model to identify disease tea leaf in this study by decreasing network parameters. A number of CNNbased models, namely InceptionV3, MobileNetV2, MobileNet, Vgg16, Xception, and InseptionResNetV2, are trained to distinguish between healthy tea leaves and diseased tea leaves using a new dataset of 2000 tea leaf images. Out of a total of 100% data, we used 75% of image data for training and 25% of image data for testing. By using MobileNet we have achieved the highest accuracy which is 98%. To increase the image's overall accuracy, we used the data augmentation approach. In comparison to other approaches, the combination of transfer learning and data augmentation produces the highest efficiency. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Tea leaf en_US
dc.subject Machine learning en_US
dc.subject Disease management en_US
dc.subject Neural networks en_US
dc.title Tea-leaf Disease Identification Using Machine Learning en_US
dc.type Article en_US

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