Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method.

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Efficiency of Nitrogen Use in Sunflower.

The large variation in the response of sunflower to nitrogen fertilization indicates the need for studies to better adjust the optimum levels of this nutrient for production conditions. Our objectives were to analyze the agronomic yield of sunflower cultivars as a function of nitrogen fertilization; indicate the cultivar with high nitrogen use efficiency; and measure the adequate N dose for sunflower through nutritional efficiency. The completely randomized block design with split plots was used to conduct the experiments. The treatments included five nitrogen rates being allocated in the plots and the four sunflower cultivars. To estimate the nutrient use efficiency in the sunflower, we measured agronomic efficiency (AE), physiological efficiency (PE), agrophysiological efficiency (APE), apparent recovery efficiency (ARE), and utilization efficiency (UE). The results indicate that all cultivars had a reduction in AE due to the increase in N doses in the first crop. For PE, the highest values were observed for Altis 99 during the 2016 harvest. In that same harvest, Altis 99 had the highest APE. The dose of 30 kg ha-1 provided greater ARE for all cultivars in both crops, with greater emphasis on BRS 122 and Altis 99. The cultivation of cultivars Altis 99 and Multissol at a dose of 30 kg ha-1 in is recommended semiarid regions.