In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations.

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 (R2) 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.

The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product’s overall quality. Today’s supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R² range, which was 0.9817 to 0.9983, were obtained for physical properties (i.e., berry weight, berry length, and berry diameter as well as berry adherence strength) and chemical properties (i.e., anthocyanin, total soluble solids (TSS), TSS/titratable acidity, total sugars, titratable acidity, reducing sugars, and non-reducing sugars). Meanwhile, we also performed a contribution analysis to analyze the relative importance of CIELab colorimeter parameters of berries L*, a*, and b* to determine the main fruit quality. In terms of relative contribution, berry b* contributed relatively largely to berry weight, berry adherence strength, TSS, TSS/titratable acidity, titratable acidity, total sugars, reducing sugars, and non-reducing sugars and a* contributed relatively largely to anthocyanin, berry length, and berry diameter. The developed ANN prediction model can aid growers in enhancing the quality of Flame Seedless grape berries by selecting suitable agronomical management and other practices to avoid potential quality issues that could affect consumers of them. This research demonstrated how color space coordinates and ANN model may well be utilized to evaluate the Flame seedless grape berries’ quality.

Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, and has good cluster quality. Flame seedless grape marketing value depends upon its desirable appearance, berry, cluster size, and shape. Therefore, it is imperative that the cluster yield and quality are enhanced to ensure profitability. In this study, the prediction of physical characteristics of clusters and berries’ color attributes of Flame Seedless grape grown under different culture practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, total carotenoids content) and multiple linear regression (MLR). The method was based on the development of two indices: the first is called index 1 (%) and was formulated by combing the mineral elements of N, P, K, Ca, and Mg concentrations; and the second is called index 2 (ppm) and was formulated by combing the elements of Fe, Cu, Mn, Zn, and B concentrations in leaf petioles. The results indicated that the established MLR models can obtain variation accuracy, based on values of coefficients of determination (R²) using the test set. The R² values were in the range of 0.9286 to 0.9972 for cluster weight, cluster length, shoulder length, berries’ color attributes (L*, a*, b*, chroma, hue, and color index for red grapes (CIRG)). This study highlighted that during a grown season, leaf mineral elements, total chlorophyll content, and total carotenoids coupled with a MLR model can be used successfully to evaluate the physical characteristics of the cluster and berries’ color attributes of Flame seedless grape. This method is easy, fast and reliable as it retains the physical appearance of the fruits by adjusting the concentration of mineral elements, total chlorophyll content, and total carotenoids in leaves. Moreover, total chlorophyll had the greatest weight of all the predicted quality attributes.

The effect of (NH₄)₂SO₄:Ca(NO₃)₂ ratios applied by fertigation on nutritional status, fruit set, yield, and marketable yield of pomegranate trees cv. Wonderful was evaluated. The trees were provided with five nutrient solutions with the same total nitrogen level (200 units/ha) but with different (NH₄)₂SO₄:Ca(NO₃)₂ ratios (100:0, 90:10, 80:20, 70:30, and 60:40). Increasing the (NH₄)₂SO₄ ratio from 60 to 100% significantly reduced the nitrogen (N), magnesium (Mg), calcium (Ca), and potassium (K) concentrations while significantly increasing P and Fe concentrations in pomegranate leaves. The highest (NH₄)₂SO₄ proportion (100%) induced a reduction in both chlorophyll content and dry matter values in pomegranate leaves. The maximum fruit set (33.65% and 31.40%) and the minimum fruit drop (6.74% and 6.25%) were recorded at the applied ratio of 60% of (NH₄)₂SO₄:40% of Ca(NO₃)₂. The applied proportion of 70% of (NH₄)₂SO₄:30% of Ca(NO₃)₂ provided the minimum fruit sunburn (9.54% and 9.74%) and fruit cracking (6.45% and 5.64%), maximum yield (33.62 and 33.00 kg/tree), and marketable yield (27.41 and 27.93 kg/tree) in the 2019 and 2020 seasons, respectively. Our results provide valuable information about the effects of partial replacement of nitrogen fertilizer from (NH₄)₂SO₄ with nitrogen fertilizer from Ca(NO₃)₂ on the growth characteristics of pomegranate trees cv. Wonderful.

The biodiesel industry is a promising field globally, and is expanding significantly and quickly. To create a biodiesel business that is both sustainable and commercially feasible, a number of studies have been conducted on the use of non-edible oils to produce biodiesel. Thus, this study highlights biodiesel synthesis from non-edible plant oils such as pongamia and jatropha using a glycerol separation technique with an AC high voltage method through the transesterification reaction. In this context, non-edible plant oil has emerged as an alternative with a high potential for making the biodiesel process sustainable. Moreover, the study introduces how the created biodiesel fuel behaves when burned in a diesel engine. The results showed that the optimum conditions for creating biodiesel were a temperature of 60 °C, a potassium hydroxide catalyst percentage by weight of oils of 1%, and a stirring time of 60 min at a 5:1 (v/v) ratio of methanol to oil. A high-voltage procedure was used to separate glycerol and biodiesel using two electrodes of copper with different distances between them and different high voltages. The results showed that, for a batch of 15 L, the minimum separating time was 10 min when the distance between the copper electrodes was 2.5 cm, and the high voltage was 15 kV. The density, kinematic viscosity, and flash point of jatropha oil were reduced from 0.920 to 0.881 g/cm³ at 15 °C, from 37.1 to 4.38 cSt at 40 °C, and from 211 to 162 °C, respectively, for the production of biodiesel. Additionally, the density, kinematic viscosity, and flash point of pongamia oil were reduced from 0.924 to 0.888 g/cm³ at 15 °C, from 27.8 to 5.23 cSt at 40 °C, and from 222 to 158 °C, respectively, for the production of biodiesel. The calorific value of jatropha oil was increased from 38.08 to 39.65 MJ/kg for the production of biodiesel, while that of pongamia oil was increased from 36.61 to 36.94 MJ/kg. The cetane number increased from 21 for oil to 50 for biodiesel and from 32 for oil to 52 for jatropha and pongamia biodiesel, respectively. In order to run an air-cooled, single-cylinder, four-stroke diesel engine at full load, the produced biodiesel fuel was blended with diesel fuel at different percentages—10, 20, and 30%—for jatropha and pongamia methyl esters. The produced engine power values were 3.91, 3.69, and 3.29 kW for B10, B20, and B30, respectively, compared with the engine power value of jatropha methyl ester, which was 4.12 kW for diesel fuel (B00); meanwhile, the values were 3.70, 3.36, and 3.07 kW for B10, B20 and B30, respectively, for pongamia methyl ester. The findings suggest that the biodiesel derived from non-edible oils, such as pongamia and jatropha, could be a good alternative to diesel fuel.