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    Wyświetlanie 1-2 z 2
    Tytuł:
    Enzyme-assisted turmeric oil extraction from turmeric rhizomes
    Autorzy:
    Chandra, Avinash
    Rekhi, Heena
    Dharmender
    Gautam, A.K.
    Arya, R.K.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173425.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    turmeric oil
    enzyme assisted
    diastase
    cellulase
    pectinase
    olejek z kurkumy
    diastaza
    celulaza
    pektynaza
    Opis:
    An enzyme-assisted modified steam distillation process was adopted to extract turmeric oil from Curcuma longa L. rhizomes. The diastase, xylose, cellulase, pectinase, and lipase enzymes were used for the pre-treatment of fresh turmeric rhizome to obtain a higher yield by rapturing biological cells. The quantitative and qualitative analysis of turmeric oil was performed by GC–MS. The various influencing parameters for the extraction of turmeric oil such as an enzyme, incubation/pre-treatment time, distillation time have been studied in the present work. The obtained turmeric oil by enzymatic pretreatment process is richer in bioactive/medicinal components than in the other traditional methods. The maximum yield was obtained with cellulase enzyme, which is 25–27% higher than the yield obtained by the traditional hydro distillation process. The detailed qualitative and quantitative analyses are also presented. The present method can be considered energy-efficient, effective, economical, and eco-friendly.
    Źródło:
    Chemical and Process Engineering; 2022, 43, 2; 183--191
    0208-6425
    2300-1925
    Pojawia się w:
    Chemical and Process Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Early detection of major diseases in turmeric plant using improved deep learning algorithm
    Autorzy:
    Devisurya, V.
    Devi Priya, R.
    Anitha, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/2173642.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    artificial intelligence
    computer vision
    turmeric leaf diseases detection
    sztuczna inteligencja
    wizja komputerowa
    wykrywanie chorób liści kurkumy
    Opis:
    Turmeric is affected by various diseases during its growth process. Not finding its diseases at early stages may lead to a loss in production and even crop failure. The most important thing is to accurately identify diseases of the turmeric plant. Instead of using multiple steps such as image pre-processing, feature extraction, and feature classification in the conventional method, the single-phase detection model is adopted to simplify recognizing turmeric plant leaf diseases. To enhance the detection accuracy of turmeric diseases, a deep learning-based technique called the Improved YOLOV3-Tiny model is proposed. To improve detection accuracy than YOLOV3-tiny, this method uses residual network structure based on the convolutional neural network in particular layers. The results show that the detection accuracy is improved in the proposed model compared to the YOLOV3-Tiny model. It enables anyone to perform fast and accurate turmeric leaf diseases detection. In this paper, major turmeric diseases like leaf spot, leaf blotch, and rhizome rot are identified using the Improved YOLOV3-Tiny algorithm. Training and testing images are captured during both day and night and compared with various YOLO methods and Faster R-CNN with the VGG16 model. Moreover, the experimental results show that the Cycle-GAN augmentation process on turmeric leaf dataset supports much for improving detection accuracy for smaller datasets and the proposed model has an advantage of high detection accuracy and fast recognition speed compared with existing traditional models.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 2; art. no. e140689
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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