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Author: search.filters.author.Han, Kadipa Aung MyoSubject: search.filters.subject.convolutional neural network

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    Banana Plant Nutrient Deficiencies Identification using Deep Learning
    (2023-01-01)
    Han, Kadipa Aung Myo
    ;
    Maneerat, Noppadol
    ;
    Sepsirisuk, Kasemsuk
    ;
    Hamamoto, Kazuhiko
    This paper presents nutrient deficiency multi-class classification in banana plant data sets using a deep convolutional neural network. In this paper, healthy and eight nutrient deficiency classes were studied. The performance was evaluated in different situations of two public data sets. The proposed method can provide sensitivity and specificity in Raw Images, Raw Images with combination, Augmented Images, and Augmented Images with the combination. Furthermore, nearly 88% of the F1-score was outperformed.
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    Classification of nutrient deficiency in black gram using deep convolutional neural networks
    (2019-07-01)
    Han, Kadipa Aung Myo
    ;
    Watchareeruetai, Ukrit
    This paper investigates the use of various deep convolutional neural networks (CNNs) with transfer learning to identify nutrient deficiencies from a leaf image. Experiments were conducted with a dataset containing 4,088 images of black gram (Vigna mungo) leaves grown under seven different treatments, i.e., complete nutrient treatment and six nutrient deficiency treatment, including calcium (Ca), iron (Fe), magnesium (Mg), nitrogen (N), potassium (K), and phosphorus (P) deficiencies. Experimental results indicate that a deep CNN model known as ResNet50 was the best among all experimented models with a test accuracy of 65.44% and a F-measure of 66.15%. In addition, We found that the ResNet50 model obviously outperformed a block-based method and the human performance reported in a literature.