*3.5. Variables' Relative Importance in the Yield Estimation Using Garson's Algorithm*

The importance of the predictor variables (NDVI, NDRE, WDRVI, EXG, TGI, VARI, density, and canopy cover) with respect to the predicted variable (yield) is shown in Figure 9; this was calculated using Garson's algorithm. The results show that the density is the most important predictor for the 47 and 79 DAS with a relative importance of 0.269 and 0.295, respectively; the WDRVI index (0.175) was the second best predictor in importance for the 47 DAS; while the NDVI index (0.184) for the 79 DAS. The VARI index was the least important predictor in the yield estimation for the 47 and 79 DAS with 0.058 and 0.031 relative importance, respectively. *Agriculture* **2020**, *10*, x FOR PEER REVIEW 22 of 27

**Figure 9.** Relative importance of the predictors in the corn grain yield estimation. (**a**) Relative importance of the predictors for the 47 DAS; (**b**) relative importance of the predictors for the 79 DAS. **Figure 9.** Relative importance of the predictors in the corn grain yield estimation. (**a**) Relative importance of the predictors for the 47 DAS; (**b**) relative importance of the predictors for the 79 DAS.

In the present study, the corn grain yield was estimated designing a neural network model based on vegetation indices, canopy cover, and plant density. The relative importance of the predictor

## **4. Conclusions**

In the present study, the corn grain yield was estimated designing a neural network model based on vegetation indices, canopy cover, and plant density. The relative importance of the predictor variables was also analyzed. The information obtained through the digital processing of images taken by unmanned aerial vehicles allowed to monitor the crop development. The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson's algorithm. The canopy cover, digitally estimated by object-oriented classification and using the TGI index, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for the 47 and 79 DAS, respectively. The WDRVI, plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, MAE = 0.028 t ha−<sup>1</sup> , RMSE = 0.125 t ha−<sup>1</sup> ) in the corn grain yield estimation at 47 DAS, with the WDRVI and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the NDVI, NDRE, WDRVI, EXG, TGI, and VARI indices, as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−<sup>1</sup> , RMSE = 0.425 t ha−<sup>1</sup> ) in the corn grain yield estimation, with the density and the NDVI being the variables with the highest relative importance with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−<sup>1</sup> , RMSE = 0.449 t ha−<sup>1</sup> ). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield; it also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allow the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

At the time of establishing agricultural crops, different techniques, tools, and management are used during the crop development, so it is desirable to carry out future trials for different climates, soils, management, and varieties to have a broader database and with greater parameters to be used in the modeling of crop yields through the use of neural networks.

**Author Contributions:** Conceptualization, H.F.-M.; Data curation, H.G.-M.; Formal analysis, H.G.-M.; Investigation, H.G.-M. and H.F.-M.; Supervision, H.F.-M.; Writing—original draft, H.G.-M.; Writing—review & editing, H.F.-M., R.A.-H., A.K.-G., L.T.-C., O.R.M.-V., and M.A.V.-P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** We thank the financial support of the Colegio de Postgraduados and the National Council of Science and Technology of Mexico (CONACyT) for making this study possible.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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