Physiological Responses of Minimally Processed Sweet Potatoes Grown with Increasing Doses of Phosphate Fertilizer.

The present work aimed to study oxidative damage and protection, phenylpropanoid metabolism, and the quality of minimally processed colored sweet potatoes cultivated with increments in P2O5 fertilization. Sweet potato was cultivated with 0, 60, 120, 180, and 240 kg ha-1 of P2O5. The roots were harvested, and the P content in the roots and leaves was quantified. The roots were minimally processed and kept for 20 days at 5 °C. In general, the roots that were fertilized with P2O5 showed a higher content of the analyzed variables. The highest P dosage in the soil increased the P content in roots and leaves and the agro-industrial yield. Roots cultivated with P2O5 showed a higher content of hydrogen peroxide, phenolic compounds, vitamin C, yellow flavonoids, anthocyanins, and carotenoids, antioxidant capacity by the DPPH method, and higher activity of the enzymes polyphenol oxidase, peroxidase, and phenylalanine ammonia lyase. These results demonstrated the role of phosphorus in protecting against oxidative damage due to the accumulation of bioactive compounds, thus improving the physicochemical quality of minimally processed orange sweet potato.

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.