Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Areas
2.2. Description of Sampling Sites
2.3. Field Sampling
2.4. Laboratory Analysis of Samples
2.5. Aerial Imaging
2.6. Data Analysis
- n_estimators. Declares the number of trees in the forest. A greater number of trees improves the accuracy of the data (RF learns faster as more decision trees are included) [25]; however, a too-large number of trees can encourage RF’s implementation during the training process. In the model run, the computing process limits were set at 30, 50, 100, 500, 1000, 1500, 2000, 2500, 3000, 3500, and 4000 trees. These thresholds have been used to evaluate RF’s abilities [12,51].
- max depth. Represents the depth of each tree in the forest. If the tree is deeper, it has more divisions and can capture more information from the data. Its value ranges from 1 to 32.
- min_samples_split. Some samples are required to separate a node. Its value ranges from 10% to 100% of the samples.
- min_samples_leaf. A minimum number of samples required in the node. Its value ranges from 10% to 50%.
- max_features. Number of characteristics to consider when looking for the best node division (default is Auto).
3. Results
3.1. Sampling Universe
3.2. Estimate of Nt in Laboratory
3.3. RF Model Optimization
3.4. Validation
3.5. Selection of the Spectral Indices Based on the Parameter of Importance
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Index | Equation | Reference |
---|---|---|
NDRE: sensitive to chlorophyll content when crops are in medium to late stages. | [37] | |
CCCI: based on a two-dimensional approach relating the NDVI [38] used to estimate canopy cover, and the NDRE. | [37] | |
CCCI_simpl: estimates chlorophyll content with minimal sensitivity to other factors that can influence in the canopy signal of N. | [39] | |
CI_rededge: indicates chlorophyll content; value shows low sensitivity to the background effects of the soil. | [40] | |
MCARI/OSAVI: very sensitive to variations in chlorophyll content and highly resistant to variations in Leaf Area Index (LAI); takes into account the effects of the soil. | [41] | |
MCARI/OSAVI RE: very sensitive to variations in chlorophyll content and highly resistant to variations in LAI; takes into account the effects of the soil and RE. | [42] | |
TCARI/OSAVI: reduces the effect of soil contribution and enhances sensitivity to chlorophyll content | [43] | |
TCARI/OSAVI RE: very sensitive to variations in chlorophyll content and highly resistant to variations in LAI; takes into account the effects of the soil and RE. | [42] | |
MTCI: has the advantage of being sensitive to N with relatively lesser effect of water or irrigation level for corn. | [44] | |
GNDVI: estimates photosynthetic activity and is a commonly used vegetation index to determine water and N uptake into the plant canopy. | [45] | |
CI_green: estimates chlorophyll content in leaves using the ratio of reflectivity in the NIR and green bands. | [46] | |
NDVI: determines the green biomass during the early and middle development stages. | [38] | |
RVI: estimates and monitors green biomass, specifically, in the coverage of high vegetation density. | [47] |
RF Estimates | ||||||
---|---|---|---|---|---|---|
Farm and Production Cycle | Maize Hybrid | Number CPS * | NitrogenLab (Nlab) ** | Flight Date | Nitrogen5 (N5) † | Nitrogen13 (N13) ‡ |
Granada: Summer–Autumn 2017 | Pioneer P3201 | 15 | 3.63 | 11 September 2017 | 3.56 | 3.57 |
19 | 2.60 | 5 October 2017 | 2.55 | 2.63 | ||
18 | 2.53 | 10 October 2017 | 2.67 | 2.63 | ||
16 | 3.10 | 20 October 2017 | 3.04 | 2.90 | ||
18 | 2.92 | 30 October 2017 | 2.88 | 2.93 | ||
17 | 2.46 | 22 November 2017 | 2.69 | 2.53 | ||
El Porvenir: Spring–Summer 2018 | Syngenta N83N5 | 10 | 3.36 | 28 April 2018 | 3.39 | 3.42 |
10 | 2.49 | 8 May 2018 | 2.60 | 2.60 | ||
10 | 1.75 | 18 May 2018 | 1.92 | 2.02 | ||
El Porvenir: Summer–Autumn 2018 | Syngenta N83N5 | 10 | 1.58 | 20 September 2018 | 1.79 | 1.79 |
10 | 1.23 | 15 October 2018 | 1.24 | 1.23 | ||
10 | 1.04 | 30 October 2018 | 1.22 | 1.27 |
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López-Calderón, M.J.; Estrada-Ávalos, J.; Rodríguez-Moreno, V.M.; Mauricio-Ruvalcaba, J.E.; Martínez-Sifuentes, A.R.; Delgado-Ramírez, G.; Miguel-Valle, E. Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest. Agriculture 2020, 10, 451. https://doi.org/10.3390/agriculture10100451
López-Calderón MJ, Estrada-Ávalos J, Rodríguez-Moreno VM, Mauricio-Ruvalcaba JE, Martínez-Sifuentes AR, Delgado-Ramírez G, Miguel-Valle E. Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest. Agriculture. 2020; 10(10):451. https://doi.org/10.3390/agriculture10100451
Chicago/Turabian StyleLópez-Calderón, Magali J., Juan Estrada-Ávalos, Víctor M. Rodríguez-Moreno, Jorge E. Mauricio-Ruvalcaba, Aldo R. Martínez-Sifuentes, Gerardo Delgado-Ramírez, and Enrique Miguel-Valle. 2020. "Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest" Agriculture 10, no. 10: 451. https://doi.org/10.3390/agriculture10100451