Yazar "Al Heeti, Fatimah" seçeneğine göre listele

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    Comparative analysis of convolutional neural network architectures for classification of plant leaf diseases
    (IEEE, 2022) Al Heeti, Fatimah; Ilyas, Muhammad
    Plants play an essential role in the life of any living organism, human or animal, so protecting this organism from disease is an urgent necessity for the survival of living organisms. The development of science has reduced the time required to discover a disease, allowing us to detect and treat diseases early using artificial intelligence. In this scientific paper, we compared the performance of (Vgg-16, MobileNet, and ConvNext) through time and accuracy among these models. We trained these models by using transfer learning models of convolutional neural networks (CNN) to classify plant diseases (potatoes, tomatoes, and peppers). We used a dataset containing 20,639 images divided into 15 classes of different diseases, we also used the same number of parameter and the same number of layers in vgg16 and mobilenet but in convnext 24 layers.The dataset from the Kaggle site and the work environment on google colab using Python language. The results are shown vgg-16 is a high accuracy of 0.97 during the training process from convnext and mobilenet,but MobilNet is faster in the time 62s.
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    Öğe
    Performance comparison of convolutional neural network models for plant leaf disease classification
    (Institute of Electrical and Electronics Engineers Inc., 2022) Al Heeti, Fatimah; Ilyas, Muhammad
    As food is an essential element in life, modern possibilities must be harnessed to pay attention to it. In this paper, we will discuss the discovery of early plant diseases classification using artificial intelligence technology, we made in this study analysis of convolutional neural networks architecture (vgg-16, mobile net, efficient net) and made a comparison between these model in accuracy and loos data in each model, we used data set from the Kaggle site that contain 20640 picture from different disease of plant (potato, tomato and pepper) this pictures divided on 15 class but unbalanced. at the first we solved the problem of train the models with multi class, we made balanced data and training the work in environment of Google Colab, we used it in train three models vgg-16, mobile net, efficient net, which showed this study that the accuracy of work in efficient net is 98% more than other models and loss data in this model is less than other models.