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

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.