## 5 Conclusions  
Scientific management of production and consumption of fertilizers is necessary to achieve sustainable agriculture, and to improve food health by improving the optimal use of them, especially nitrogen-based fertilizers. In this paper, we have presented a new  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Product** & **Method** & **Prediction** & **R\({}^{2}\)** \\ \hline Proposed method for N\({}_{30\%}\) & Cucumber & 1D-CNN & Excess nitrogen & 0.962 \\ Proposed method for N\({}_{60\%}\) & Cucumber & 1D-CNN & Excess nitrogen & 0.968 \\ Proposed method for N\({}_{90\%}\) & Cucumber & 1D-CNN & Excess nitrogen & 0.967 \\ ([PERSON] et al. [35]) & Tomato & PLSR & Nitrogen content & 0.702 \\ ([PERSON] et al. [35]) & Tomato & ANN-DE & Nitrogen content & 0.747 \\ ([PERSON] et al. [35]) & Tomato & CNN & Nitrogen content & 0.766 \\ ([PERSON] et al. [22]) & Kiwifruit & ANN & Nutrient content & 0.85 \\ ([PERSON] et al. [36]) & Tomato & ANN & SSC & 0.81 \\ ([PERSON] et al. [37]) & Soybean & CNN & K \& Fe & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison the coefficient of determination, R\({}^{2}\), of the proposed 1D-CNN method with other existing methods.  
methodology for fast and accurate estimation of nitrogen content in cucumber leaves using spectroscopy analysis and convolutional neural networks (CNN).  
Currently, CNNs are one of the most popular methods of machine learning, since they do not require manual feature extraction. Instead, automatic feature extraction makes deep learning models very accurate for computer vision tasks, such as regression models. Therefore, we have presented a new structure of one-dimensional CNN (1D-CNN) to estimate nitrogen content. Different levels of nitrogen fertilizer overdose by 30%, 60% and 90% were added to the cucumbers, and then prediction models were trained for each treatment. The experimental results have shown that the proposed 1D-CNN is able to estimate very accurately the nitrogen content even in the early stages, achieving, for example, for the 30% treatment, a maximum error of 0.086 mg/g and an R\({}^{2}\) of 0.962. For the 60% and 90% classes, the R\({}^{2}\) are 0.968 and 0.967, respectively. The experiments also showed that the applied preprocessing filters, multiplicative scatter correction, Savitzky-Golay, had a positive effect on the accuracy. Thus, this approach can be used to create a fast and non-destructive method to prevent excessive use of fertilizers.  
Conceptualization, R.P., J.L.H.-H. and G.G.-M.; methodology, S.S.; software, M.H.R. and I.H.-M. validation, I.G.-B.; formal analysis, S.S.; investigation, I.H.-M. and G.G.-M.; resources, I.G.-B.; data curation, R.P.; writing--original draft preparation, S.S. and I.H.-M.; writing--review and editing, M.H.R., J.L.H.-H. and G.G.-M.; visualization, M.H.R.; supervision, R.P. and G.G.-M.; project administration, J.L.H.-H.; funding acquisition, I.G.-B. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by University of Mohaghegh Ardabili, Sharif University of Technology, National Technological of Mexico/Campus Chilpancingo and Autonomous University of Guerrero. It was also funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades (MCIU), Ministerio de Ciencia e Innovacion (MICINN) and Agencia Estatal de Investigacion (AEI); as well as European Commission FEDER funds, under grant RTI2018-098156-B-C53.  
Not applicable.  
The data presented in this study are available upon reasonable request to the corresponding authors.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.