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.

Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves

“…It must be noted a rough comparison is not rigorous as the papers relate to different plants, techniques, and datasets. Pourdarbani et al (2022) investigated the feasibility of using hyperspectral imaging to detect excess nitrogen content in tomato plants. Artificial neural networks and the particle swarm optimization algorithm were proposed and achieved a satisfactory classification accuracy of 92.6% for leaves at different nitrogen levels.…”

“…Artificial neural networks and the particle swarm optimization algorithm were proposed and achieved a satisfactory classification accuracy of 92.6% for leaves at different nitrogen levels. The leaves in this work (Pourdarbani et al, 2022) were classified according to different days of nitrogen application. Sun et al (2013) used VNIR to identify the nitrogen level of lettuce leaves.…”

“…The excellent performance might benefit from the texture information provided by the image. Although the methods mentioned above achieved good results, the samples in these studies were classified according to different nitrogen fertilization levels or different nitrogen fertilization days (Sun et al, 2013;Wang et al, 2018;Pourdarbani et al, 2022). There is a large difference between the fertilization of nitrogen and its actual uptake for the plant.…”

“…Relationships between cotton leaf spectra curves (380-700 nm, 700-1300 nm, and 1300-2500 nm) and nitrogen content contributed to satisfactory predictions for nitrogen content detection. There are indeed many researches on the detection of LNC (Sun et al, 2013;Wang et al, 2018;Gao et al, 2022;Pourdarbani et al, 2022;Tang et al, 2022;Zhang et al, 2022b). Although the studies focusing on the LNC classification achieved good results (Sun et al, 2013;Wang et al, 2018;Pourdarbani et al, 2022), the samples in these studies were classified according to different nitrogen fertilization levels or different nitrogen fertilization days.…”

“…There are indeed many researches on the detection of LNC (Sun et al, 2013;Wang et al, 2018;Gao et al, 2022;Pourdarbani et al, 2022;Tang et al, 2022;Zhang et al, 2022b). Although the studies focusing on the LNC classification achieved good results (Sun et al, 2013;Wang et al, 2018;Pourdarbani et al, 2022), the samples in these studies were classified according to different nitrogen fertilization levels or different nitrogen fertilization days. It should be noted that there is a large difference between the fertilization of nitrogen and its actual uptake for the plant.…”