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The present work aims to study the development and application of Radial Basis Function (RBF) networks for predicting auger energy consumption based on input energy. The study utilized RBF networks and explored the input energy with treatments 2 (Soil moisture content), 2 (Rotary speeds), 2 (Hole depths) and 4 (Replication) based on field operations. As indicated by the results, energy input differed between the treatments but was not significant. The highest input value in transaction soil moisture content was 14.75 %, rotary speeds of 235 rpm, and hole depths of 40 cm. In comparison, the lower input energy at transaction soil moisture content was 7.9%, rotary speeds of 235 rpm, and hole depths of 20 cm. Input energy in treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9 %,235 rpm, and 20 cm) were 100.204 and 57.135 MJ. ha-1, respectively. The highest input energy shares were recorded for diesel fuel at all treatments. Furthermore, the RBF network with one hidden layer had good convergence. The output results showed 10 and five hidden neurons in a hidden layer with high accuracy for treatment (14.75 %, 235 rpm, and 40 cm) and treatment (7.9%, 235 rpm, and 20 cm). In the treatment (14.75 %, 235 rpm, and 40 cm), the MSE for the training and testing sets was 0.0001 % and 0.01 % for data points with Ordinary RBF (ORBF type). The performance of the 3-10-1 architecture was better than other architectures. Finally, this research concluded that the RBF network method can forecast the input energy and energy expenditures related to the types of treatments.

An experiment was conducted in Nineveh, Iraq. The study evaluates the efficacy of three methods for harvesting potato crops (Semi-Mechanized Harvesting, which includes single-row and two-row harvesting, and manual harvesting). Evaluation through study effects of ways on the undamaged tubers, severely damaged tubers, slightly damaged tubers, qualitative loss, quantitative loss, produced tubers, the time of the harvest process, and harvest costs with the total loss which goes in line with each method). The best performance was obtained for semi-mechanized harvest (2-row harvesters); it recorded the lowest percentage of severely damaged tubers of 0.2 ton. ha-1 and the highest rate of lifted tubers was 24.6 ton. ha-1. The production of the best sample about 25.49 ton. ha-1 while the highest productivity about 2.24 ha. h-1. Moreover, the lowest harvest costs were 4.5$. ha-1, and less time for completion of the harvest process was 0.44 h. ha-1. At the same time, the manual harvesting process has recorded a higher percentage of undamaged tubers was 20.76 ton. ha-1 and a lower percentage of damaged tubers about 3.63 ton. ha-1, a lower percentage of quantitative loss of 0.49 ton. ha-1, and less loss in total quantity of 877 $. ha-1. Finally, in the manual harvesting process, the farmer needs 45 workers to complete per hectare within an hour.

Abstract: This paper presents a new computationally fast and accurate method for evaluating available transfer capability (ATC) based on a curve fitting technique so-called as the cubic-spline interpolation technique. The advantage of the technique used in the computation of ATC is that it has the ability to reduce the time consuming ac power flow computations. The cubic-spline interpolation technique traces the curves of voltage magnitude and power flow variations with respect to the increase of real power transfer. ATC is then determined at the point where the voltage or power flow limits intersect the curves. Prior to the ATC evaluation, contingency ranking and selection techniques are used to define the critical lines in a system that can adversely affect the transfer capability assessment. The effectiveness of the proposed method is verified by illustrating the ATC evaluation on a practical test system. ATC results obtained from the proposed cubic-spline interpolation technique prove that the method is satisfactorily accurate and it is faster than the ATC method using the recursive ac power flow computations. [Copyright &y& Elsevier]