Abstract
One of the most used food crops produced worldwide is rice. However, there is a huge possibility that excessive saline levels, particularly at the seedling stage, may negatively impact rice productivity. Therefore, it is essential to identify and create salinity-tolerant rice crop varieties as soon as possible, especially at the seedling stage, to avoid a reduction in rice yield. For the classification of visual signs and the traditional method of standard evaluation system for identification of rice crop salinity stress, human expertise is needed. Most of the time, this can result in errors that in turn result in incorrect classification and also its time consuming. To identify and classify salinity stress in rice seedlings using field images, the study reports the need of deep learning built model over traditional method of assessing rice crop's susceptibility to salt stress during the seedling stage. So to build the classification model, we use the pre-trained VGG 16, one of the deep learning techniques, which is designed in Jupyter Notebook using Python programming. The model is able to classify the rice seedling images according to the scores as Grade1, Grade3, Grade5, Grade7, Grade9 that supports to the fact that there is a huge need of a computer-based classification system for the salinity prediction that could be used as tool for automating the classification process for rice development to assist scientists and agriculturalists in the rice crop management system.
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The data that supports the findings of the article is available with the corresponding author and may be provided on request.
Change history
28 September 2023
A Correction to this paper has been published: https://doi.org/10.1007/s42979-023-02168-3
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This article is part of the topical collection “Advances in Computational Intelligence for Artificial Intelligence, Machine Learning, Internet of Things and Data Analytics” guest edited by S. Meenakshi Sundaram, Young Lee and Gururaj K S.
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Shiragudikar, S.K., Bharamagoudar, G., Manohara, K.K. et al. Insight Analysis of Deep Learning and a Conventional Standardized Evaluation System for Assessing Rice Crop's Susceptibility to Salt Stress during the Seedling Stage. SN COMPUT. SCI. 4, 262 (2023). https://doi.org/10.1007/s42979-022-01656-2
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DOI: https://doi.org/10.1007/s42979-022-01656-2