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Coffee Arabica Nutrient Deficiency Detection System Using Image Processing Techniques

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dc.contributor.author Tenaye, Firezer
dc.contributor.author Diriba, Chala
dc.contributor.author Bedaso, Muktar
dc.date.accessioned 2023-11-02T07:42:36Z
dc.date.available 2023-11-02T07:42:36Z
dc.date.issued 2022-10-11
dc.identifier.issn 2574 -1241
dc.identifier.uri https://repository.ju.edu.et//handle/123456789/8740
dc.description.abstract This study mainly focused on the detection of Coffee Arabica nutrient deficiency by using image processing techniques. Coffee nutrition deficiency techniques are very traditional and time taking which means the agronomists simply detect deficiencies by observing the leaves of the coffee and decide by guessing. The study employed ex perimental research design which involves dataset preparation, designing classifica tion model and evaluation. In addition, Python programming languages were used. The researcher has 422 total nutritional deficient Coffee plant leaves image data set, from this data first the researcher split 20 percent for testing which is 84 images and 338 training image data, and further from the remaining training data, the researcher again split20 percent validation data which is 10 images. The three pre-trained deep learning models were used to evaluate the experiments. The evaluation of the system indicated the performance of Mobile Net (0.9882), VGG16 Net (0.6471) and Incep tion_V3 (0.8095). Therefore, testing and training value of Mobile Net model was more accurate than the rest of two models. Finally, the prototype for detection of Coffee nu trient deficiency developed by using Mobile Net deep learning model. For the feature the researchers suggest doing more researchers by using others CNN architectures and more datasets. en_US
dc.language.iso en_US en_US
dc.subject Coffee Arabica en_US
dc.subject Computer Vision en_US
dc.subject Image Processing en_US
dc.subject Nutritional Deficiencies en_US
dc.title Coffee Arabica Nutrient Deficiency Detection System Using Image Processing Techniques en_US
dc.type Article en_US


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