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“LeafCheck” Utilizing Deep Learning Techniques for Tea Leaf Disease Detection Through Image Analysis

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dc.contributor.author Chandraratne, Deuni
dc.date.accessioned 2024-03-01T07:17:34Z
dc.date.available 2024-03-01T07:17:34Z
dc.date.issued 2023
dc.identifier.citation Chandraratne, Deuni (2023) “LeafCheck” Utilizing Deep Learning Techniques for Tea Leaf Disease Detection Through Image Analysis. BSc. Dissertation, Informatics Institute of Technology en_US
dc.identifier.issn 2019740
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/1801
dc.description.abstract "Tea is a commonly grown, highly favored culinary plant with a distinctive flavor and a wealth of nutrients in the agricultural industry. The harvest from tea is often good, and it is significant to global trade and agricultural production. Numerous causes, including bacteria, viruses, fungi, etc., frequently contribute to the diseases that affect tea. As a result, the threat that these diseases provide to farming is significant. Therefore, identifying these leaf diseases is crucial for disease prevention and for raising crop yields of both high quality and quantity. Currently, the process of designing convolutional neural network architectures relies heavily on human expertise and effort. While CNNs have demonstrated remarkable success in various computer vision tasks, the field of deep learning is constantly evolving, and novel models can lead to significant advancements. In this research, the author proposes a novel deep convolutional neural network model while combining the use of image processing tasks and the use of a reinforcement learning algorithm to optimize the decision-making process. An image dataset of tea leaves that included five different disease classes and a healthy leaf class was used to evaluate the performance of the suggested methodology. A dataset of 51,300 images was used for training and validation of the suggested Deep Convolutional Neural Network model. An additional set of 5,700 images was used to examine the classification performance of the proposed algorithm as well as current cutting-edge methods. The gathered data showed that the suggested algorithm has an average accuracy of 97% on the test data, successfully diagnosing and categorizing diseases affecting tea leaves." en_US
dc.language.iso en en_US
dc.publisher IIT en_US
dc.subject Tea leaf disease detection en_US
dc.subject Convolutional neural network en_US
dc.title “LeafCheck” Utilizing Deep Learning Techniques for Tea Leaf Disease Detection Through Image Analysis en_US
dc.type Thesis en_US


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