Computer Science
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Item AUTOMATIC FISH SPECIES IDENTIFICATION USING DEEP LEARNING TECHNIQUE(Hawassa University, 2023-03-17) HABTAMUA ZERIHUNIn recent years, the growing global population has led to an increased demand for animal protein, including fish and other aquatic products. Aquaculture has emerged as a primary method for meeting this demand. There is a need for reliable and accurate methods to identify fish species. However, the accurate identification of fish species remains a challenge as there are various fish species endemic to different regions. This research focuses on addressing this challenge by developing a system for automatic fish species identification using deep learning technique, with a specific emphasis on convolutional neural network (CNN). To accomplish the objective of the research, fish species images were collected from Lake Hawassa. The collected dataset was certified by domain experts from the Centre for Aquaculture Research and Education (CARE) at Hawassa University. A custom dataset was prepared, consisting of a total of 6000 images of six fish species: Oreochromis niloticus, Clarias garipienus, LabeoBarbus intermedius, Barbus paludinosis, Garra quadrimaculata, and Aplocheilichthys. The proposed system for fish species identification implements a preprocessing module that involves image resizing and pixel value normalization to ensure uniformity and enhance training performance. Data augmentation techniques were utilized to generate diverse training examples. For classification, convolutional neural network (CNN) is employed, either trained using Convolutional neural network (CNN) architectures or utilizing pre-trained models such as Inceptionv3, VGG16, and ResNet50. Evaluation metrics were employed with two different dataset ratios: 70/30 and 80/20 and also three pre-trained models were used for comparison. The results demonstrate that our proposed model 70/30 ratio outperforms the pre-trained models in terms of training, testing accuracy, as well as loss. Our model achieved a training accuracy of 100%, validation accuracy of 99.7% and a testing accuracy of 99.5%, indicating better learning and classification capabilities. Additionally, the model achieved a recall, precision and f1 score of 100%. This research contributes to the field of fish species identification. By leveraging deep learning techniques, Particularly CNN, our model achieves better accuracy in automatic fish species identification. It reduces reliance on expert skills, addresses unresolved problems, and contributes to the progress of accurate fish species identificationItem CABBAGE DISEASE CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK(Hawassa University, 2024-04-07) Samrawit FelekeCabbage is a widely cultivated vegetable crop susceptible to various diseases, which can significantly reduce crop yield and quality. To effectively monitor these diseases, accurate and timely classification is essential. This study proposes cabbage plant disease classification using convolutional neural networks (CNN). The proposed method involves preprocessing input images, extracting relevant features using a developed CNN model, and accurately classifying diseases affecting cabbage plants. The cabbage leaf image dataset was collected from Sidama region Hawassa zuriya deneba and east shewa zone of the Oromia region dugda woreda meki town cabbage production areas in Ethiopia. The performance of the proposed method is evaluated on 5700 datasets of cabbage plant images with five types of diseases, and one healthy cabbage class such as Alternaria leaf spot, anthracnose, black rot, cabbageworm, downy mildew, and healthy cabbage. We used the data augmentation method to expand the training dataset. 70% of the dataset was used for training, and the remaining 30% was used for testing and validation. We used existing research to compare the proposed model and obtained better results. The proposed model is evaluated using certain metrics such as precision, recall, F1-score, and accuracy. 96.80% of precision, 96.65% of recall, and 96.67% of F1-score. The proposed model achieved 99.53% training accuracy and 98.51% test accuracy for classified experiment results. The results show that the proposed approach can provide accurate and efficient disease diagnosis for cabbage plants, enabling timely and targeted intervention to prevent crop lossItem ETHIOPIAN COFFEE BEAN DETECTION AND CLASSIFICATION USING DEEP LEARNING(Hawassa University, 2020-06-02) GETABALEW AMTATEEthiopia is the homeland of Coffee Arabica. Coffee is the major commodity export which covers the highest income source of foreign currency. In addition to this, Coffee has a great role in social interaction between peoples and the source of income for the coffee-producing farmers. Ethiopian coffee beans are distinct from each other in terms of quality based on their geographical origins. Classification and grading of those coffee beans are based on growing origin, altitude, bean shape and color, preparation method and others. However, the quality of the coffee beans is determined by visual inspection, which is subjective, laborious, and prone to error and this requires the development of an alternative method which is precise, non destructive and objective. Thus, the objective of this research is to design and develop a model that characterizes and identifies coffee beans of six different origins of Ethiopia (Jimma, Limmu, Nekemte, Yirgacheffe, Bebeka, and Sidama). Coffee beans for this research are collected from the Ethiopian Coffee Quality Inspection and Auction Center (ECQIAC). Image processing and the state-of-the-art deep-learning techniques were employed to automatically classify coffee bean images into nine different class: washed Limmu, unwashed Limmu, washed Sidamo, unwashed Sidamo, washed Yirgacheffe, unwashed Yirgacheffe, unwashed Jimma, unwashed Nekemte, and washed Bebeka. A total of 9836 coffee bean images were used to train, validate and test the CNN model. We have compared the classification result of the model trained on different dataset sizes and hyperparameters. The model was trained on 80% of the dataset, validated on 10%, and tested on 10% of the colorful coffee bean images, with batch normalization has scored 99.89% overall classification accuracy and 0.92% generalization log loss. In conclusion, the result of the study shows that CNN is an effective deep learning technique in the classification of Ethiopian coffee beans