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    Nondestructive nitrogen content estimation in tomato plant leaves by Vis-NIR hyperspectral imaging and regression data models

    , Article Applied Optics ; Volume 60, Issue 30 , 2021 , Pages 9560-9569 ; 1559128X (ISSN) Pourdarbani, R ; Sabzi, S ; Rohban, M. H ; García Mateos, G ; Arribas, J. I ; Sharif University of Technology
    The Optical Society  2021
    The present study aims to estimate nitrogen (N) content in tomato (Solanum lycopersicum L.) plant leaves using optimal hyperspectral imaging data by means of computational intelligence [artificial neural networks and the differential evolution algorithm (ANN-DE), partial least squares regression (PLSR), and convolutional neural network (CNN) regression] to detect potential plant stress to nutrients at early stages. First, pots containing control and treated tomato plants were prepared; three treatments (categories or classes) consisted in the application of an overdose of 30%, 60%, and 90% nitrogen fertilizer, called N-30%, N-60%, N-90%, respectively. Tomato plant leaves were then randomly... 

    One‐dimensional convolutional neural networks for hyperspectral analysis of nitrogen in plant leaves

    , Article Applied Sciences (Switzerland) ; Volume 11, Issue 24 , 2021 ; 20763417 (ISSN) Pourdarbani, R ; Sabzi, S ; Rohban, M. H ; Hernández‐hernández, J. L ; Gallardo‐bernal, I ; Herrera‐miranda, I ; García‐mateos, G ; Sharif University of Technology
    MDPI  2021
    Accurately determining the nutritional status of plants can prevent many diseases caused by fertilizer disorders. Leaf analysis is one of the most used methods for this purpose. However, in order to get a more accurate result, disorders must be identified before symptoms appear. Therefore, this study aims to identify leaves with excessive nitrogen using one‐dimensional convolutional neural networks (1D‐CNN) on a dataset of spectral data using the Keras library. Seeds of cucumber were planted in several pots and, after growing the plants, they were divided into different classes of control (without excess nitrogen), N30% (excess application of nitrogen fertilizer by 30%), N60% (60% overdose),...