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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      1 Jan 2024, Volume 39 Issue 1   
    Article

    AUGMENTING SMART FARMING: SEVERITY-CENTRIC BLAST PADDY LEAVES ASSESSMENT
    V. Mary Rajam Vandana, Dr. Viji Vinod
    Journal of Data Acquisition and Processing, 2024, 39 (1): 1553-1565 . 

    Abstract

    Paddy leaf rice blast disease is a devastating rice disease which causes severe yield losses and threatening rice production globally. Rice blast caused by the fungus Magnaporthe oryzae, pose a threat to both the above and below-ground parts of paddy plants. It's crucial to identify signs of disease, like rice blast severity and understand its effective management strategies. Deep learning (DL) methods have shown to be successful in solving this problem for intricate prediction problems. The study presents a unique Spatial Stacked Deep Convolutional Neural Network (SS-DCNN) method for forecasting the impact of a rice blast severity that utilizes DL principles. Using a prominent disease dataset from Kaggle, it was implemented a three-step preprocessing methodology, including k-means based segmentation, high-pass filtering, and bicubic interpolation, to ensure data quality and enhance images. To extract meaningful features, it employs two methods, Histogram of Oriented Gradients (HOG) and Accelerated-KAZE (AKAZE), which aid in reducing dimensionality while retaining essential features. The proposed model is applied to finish the classification task, naturally accounting for temporal correlations in the data. This is especially important for estimating the intensity of the rice blast severity, as past trends have a big impact on results. The study, which focuses on rice blast severity forecasting, is carried out with the help of Python tools. Several measures of accuracy are used to evaluate the suggested model's efficiency. Their all-encompassing strategy seeks to improve rice blast severity prediction measures relative to current approaches.

    Keyword

    Python, Paddy Leaf disease prediction, Dataset, Spatial Stacked Deep neural Network (SS-DNN), k-means based segmentation, High pass filter, Bi-cubic interpolation, Histogram of Oriented Gradients (HOG), Accelerated-KAZE (AKAZE)


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ISSN 1004-9037

         

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