Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants

Abstract

Chemical fertilizers are widely applied in agriculture to achieve high yield, enhance produce quality and build resistance to diseases; in our case the plant being tomato (Solanum lycopersicum L. var. Royal). However, the acidity, size and taste of tomato fruits could change with excess nitrogen (N) application. The present study aims at the early detection of nitrogen-rich tomato leaves using hyperspectral imaging techniques in the visible and near infrared (Vis-NIR) spectrum, in order to improve plant nutrition composition at an early growth stage. A 30% over-dose of nitrogen was applied to half of the tomato pots. Five leaves were randomly collected from each pot for 3 days (classes D0, D1, D2 and D3), and images were captured with a hyperspectral camera. A metaheuristic approach of artificial neural networks and the firefly algorithm (ANN-FA) was used to determine the most discriminative wavelengths. Afterwards, a combination of ANN and particle swarm optimization (ANN-PSO) was used to classify tomato leaves into the four classes. The training/classification process was repeated 200 times, and results indicated that the proposed approach was able to detect the excess of nitrogen even at the first day (D1), with a precision of 92.9%. Considering all the classes, the average correct classification rate was 92.6%, while the best execution achieved 95.5% accuracy. Thus, the method showed a high performance for practical uses.