Nondestructive nitrogen content estimation in tomato (Solanum lycopersicum L) plant leaves by Vis-NIR hyperspectral imaging and regression data models
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 picked up
before and after the application of nitrogen excess and imaged. Leaf
images were captured by a hyperspectral camera, and nitrogen content was
measured by laboratory ordinary destructive methods. Two approaches were
studied: either using all the spectral data in the visible (Vis) and near
infrared (NIR) spectral bands, or selecting only the three most effective
wavelengths by an optimization algorithm. Regression coefficients (R) were
0.864±0.027 for ANN-DE, 0.837±0.027 for PLSR, and 0.875±0.026 for CNN in
the first approach, over the test set. The second approach used different
models for each treatment, achieving R values for all the regression
methods above 0.96; however, it needs a previous classification stage of
the samples in one of the three nitrogen excess classes under
consideration.
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