stdClass Object
(
    [id] => 6908
    [paper_index] => 202204-03-010083
    [title] => CONJECTURE OF CROP ADVANCEMENT STAGES USING NEURAL NETWORKS
    [description] => 
    [author] => Jasmin Pemeena Priyadarshini M, Aastha Agarwal, Shagun Rai, Prateek Agarwal
    [googlescholar] => https://scholar.google.co.in/scholar?cluster=4818191999381553524&hl=en&as_sdt=0,5
    [doi] => 
    [year] => 2022
    [month] => May
    [volume] => 10
    [issue] => 5
    [file] => 711pm_1.EPRA JOURNALS 10083.pdf
    [abstract] => This paper proposes that deep learning is a branch of artificial intelligence. In recent years, the advantages of automatic learning and feature extraction have been a wide concern in academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges in the detection of plant leaf disease using deep learning and advanced imaging techniques. We used the Convolutional Neural Network (CNN). Accuracies ranging from 95% - 100% were obtained.
    [keywords] => Accuracy, CNN,  Leaf disease, MATLAB,  Pre-processing, Segmentation, 
    [doj] => 2022-05-06
    [hit] => 1155
    [status] => y
    [award_status] => P
    [orderr] => 1
    [journal_id] => 3
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Agriculture and Rural Economic Research (ARER)
    [short_code] => IJIR
    [eissn] => 2321-7847
    [pissn] => 
    [home_page_wrapper] => images/products_image/3.ARER.png
)
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