No abstract

Fruit quality attributes are important factors for designing a market for agricultural goods and commodities. Support vector regression (SVR), MLR, and ANN models were established to predict the mass of ber fruits (Ziziphus mauritiana Lamk.) based on the axial dimensions of the fruit from manual measurements of fruit length, minor fruit diameter, and maximum fruit diameter of four ber cultivars. The precision and accuracy of the established models were assessed given their predicted values. The results revealed that using the validation dataset, the developed ANN (R2 = 0.9771; root mean square error [RMSE] = 1.8479 g) and SVR (R2 = 0.9947; RMSE = 1.8814 g) models produced better results when predicting ber fruit mass than those obtained by the MLR model (R2 = 0.4614; RMSE = 11.3742 g). In estimating ber fruit mass, the established SVR and ANN models produced more precise prediction values than those produced by the MLR model; however, the performance differences between the SVR and ANN models were not clear.

Draft and energy requirements are the most important factors in the activities of farm machinery management owing to their role in matching the tractor with implements for different tillage operations. This study's aim was to model the draft and energy requirements of a moldboard plow based on two novel variables. The first was the soil texture index (STI), which was formed from the clay, sand, and silt contents with a range of 0.03-0.84. The second variable was the field working index (FWI), formed by combining the plow width, plowing speed, soil bulk density, soil moisture content, plowing depth, and tractor power into one dimensionless variable, which had a range of 7.17-82.45. The coefficient of determination (R 2 ) values obtained using a testing dataset were found out to be 0.9134 for energy and 0.8602 for draft requirements. For the draft and energy requirements of the testing data points, the mean absolute errors between the measured values and the values predicted using the artificial neural networks (ANN) model were 0.99 kN and 2.39 kW•h/ha, respectively. Based on comparisons with other results reported using multiple linear regression, it was clear that the predictions by the proposed ANN model were very satisfactory.

Infiltration measurements using a double-ring infiltrometer were conducted on a sandy-loam soil located in Saudi Arabia. The measurements were performed for an undisturbed soil. The effect of sodium adsorption ratio (SAR) and electric conductivity (EC) of the applied water on infiltration rate was examined. The infiltration rate at the initial time was high, in the order 305 > 240 > 137 > 104 > 65 mm/h for SAR of 3.34, 3.52, 4.14, 4.18, and 7.60, respectively. The results showed that 180 min after the initial time of measurement in the sandy-loam soil, the final infiltration rates were in the range of 21.1-44.0 mm/h for the different qualities of water considered in this study, with an average value of 33.8 mm/h. Hence, the infiltration rate is sensitive to the SAR of the applied water. The final infiltration rate (IR f ) and the final cumulative infiltration depth (Z f ) after 180 min could be predicted using the following equations: