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.

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.

Agricultural sustainability is dependent on the ability to predict crop yield, which is vital for farmers, consumers, and researchers. Most of the works used the amount of rainfall, average monthly temperature, relative humidity, etc. as inputs. In this paper, an attempt was made to predict the yield of the citrus crop (Washington Navel orange, Valencia orange, Murcott mandarin, Fremont mandarin, and Bearss Seedless lime) using weather factors and the accumulated heat units. These variables were used as input parameters in an artificial neural network (ANN) model. The necessary information was gathered during the growing seasons between 2010/2011 and 2021/2022 under Egyptian conditions. Weather factors were daily precipitation, yearly average air temperature, and yearly average of air relative humidity. A base air temperature of 13.0 °C was used to determine the accumulated heat units. The heat use efficiency (HUE) for cultivars was determined. The Bearss Seedless lime had the lowest HUE of 9.5 kg/ha °C day, while the Washington Navel orange had the highest HUE of 20.2 kg/ha °C day. The predictive performance of the ANN model with a structure of 9-20-1 with the backpropagation was evaluated using standard statistical measures. The actual and estimated yields from the ANN model were compared using a testing dataset, resulting in a value of RMSE, MAE, and MAPE of 2.80 t/ha, 2.58 t/ha, and 5.41%, respectively. The performance of the ANN model in the training phase was compared to multiple linear regression (MLR) models using values of R²; for MLR models for all cultivars, R² ranged between 0.151 and 0.844, while the R² value for the ANN was 0.87. Moreover, the ANN model gave the best performance criteria for evaluation of citrus yield prediction with a high R², low root mean squared error, and low mean absolute error compared to the performance criteria of data mining algorithms such as K-nearest neighbor (KNN), KStar, and support vector regression. These encouraging outcomes show how the current ANN model can be used to estimate fruit yields, including citrus fruits and other types of fruit. The novelty of the proposed ANN model lies in the combination of weather parameters and accumulated heat units for accurate citrus yield prediction, specifically tailored for Egyptian regional citrus crops. Furthermore, especially in low- to middle-income countries such as Egypt, the findings of this study can greatly enhance the reliance on statistics when making decisions regarding agriculture and climate change. The citrus industry can benefit greatly from these discoveries, which can help with optimization, harvest planning, and postharvest logistics. We recommended furthering proving the robustness and generalization ability of the results in this study by adding more data points.

When planning a solar energy conversion system, having sufficiently reliable values of the monthly average daily solar radiation (MADSR) on a horizontal surface is essential. Traditionally, estimates based on other climatological variables for which more information is available have been relied upon to compensate for the lack of direct solar radiation measurements. Solar radiation varies widely, which requires the creation of site-specific forecast models. By using artificial neural network (ANN) models or similar methods using historical datasets, the monthly average daily solar radiation can be easily assessed. To verify the validity of the established ANN model, a series of analyses was performed using the mean squared error, the coefficient of determination (R²), and the mean absolute error. The study used a dataset collected from nine weather stations in Saudi Arabia from 1985 to 2000. The input parameters for the ANN model were the maximum air relative humidity, latitude, the maximum ambient air temperature, longitude, the minimum ambient air temperature, the minimum air relative humidity, sunshine duration, location altitude, and the corresponding month. The R² for the whole test dataset was 0.8449. Furthermore, a sensitivity analysis using the established ANN model showed that site elevation (location altitude) had the most significant effect on MADSR on a horizontal surface, with a contribution value of 14.66%. The analysis results show that the ANN model accurately estimates MADSR on horizontal surfaces regardless of seasonal variations in weather conditions. Furthermore, this work is important not only for its contribution to the shape of information in solar radiation forecasting but also for establishing the practical application of ANNs in renewable energy management. The results of this work will help improve the utilization of solar energy and support sustainable energy efforts. Furthermore, the proposed ANN model is believed to be useful for predicting MADSR on horizontal surfaces in other locations in Saudi Arabia with similar climatic conditions to the study sites. Furthermore, the ANN approach may be functional to the basic strategy of a solar arrangement and is suitable for forecasting other meteorological data.

Mechanical properties of vegetables or crop materials play a noteworthy part in designing new related implements. These properties can be extracted from force–deformation curves. Several factors, such as soil preparation, irrigation, and pre- and post-harvest treatments influence them. The core objective of this investigation work was to analyze force–deformation curves obtained from compression, penetration, and shear tests of potatoes (Spunta cv.) produced with three tillage implements. The potatoes cv. Spunta were cultivated in loamy sand soil under the center-pivot irrigation system. The tillage implements used were a disc harrow plow, chisel plow, and moldboard plow. The trials were performed at a constant planting depth (15 cm) below the soil and a single plowing speed of 5.4 km/h. All data were expressed as an average of five replicates ± standard deviation. The force–deformation curves analysis showed that the modulus of elasticity for potatoes cv. Spunta ranged from 4.32 to 5.8 N/mm, the bioyield force ranged from 84.25 to 114.12 N, and rupture forces ranged from 100.90 to 139.78 N. Furthermore, the results showed that the average values of the elastic and plastic ranges were 3.0 and 2.1 mm, respectively. The mean value of hardness was 1671.53 N·mm. No significant differences were observed with respect to the two planting seasons, but tillage implements had a significant impact on the characteristics extracted from the compression tests. The mean of the maximum forces required to penetrate the potato during the penetration stage were 41.24 N, 44.86 N, and 47.16 N for potatoes produced with the disc harrow plow, chisel plow, and moldboard plow, respectively. Similarly, the means of the maximum forces required to cut the potato in the shear stage were 724.38 N, 761 N, and 773.43 N for the disc harrow plow, chisel plow, and moldboard plow, respectively. The force–deformation curves showed that additional information might be required to obtain a complete description of the potato quality necessary to harvest potatoes cv. Spunta using harvesting and handling equipment with reduced economic loss. An extensive study of the soil characteristics and the above-mentioned properties is also recommended. The results obtained about the mechanical characteristics of potatoes cv. Spunta can be useful in providing information that aids in designing potato harvesting machines and in potato products factories.

One of the key factors for sustainability in agricultural systems, particularly, for cultivation of date palms, is the identification of the performance parameters of the pollen grains of male date palms (Phoenix dactylifera L.). This study was carried out to predict the performance parameters of pollen grains using an artificial neural network (ANN) model. The morphological features of spathe length, spathe weight, number of pinnae per leaf, leaf length, leaf width, length of the pinna part, pinna length, pinna width, length of the spathe stem, and spathe width, as well as the concentrations of minerals such as Mg, N, K, P, and Ca in their leaves were used as inputs to the ANN model. For this purpose, we collected the required data from nine male date palms grown in Saudi Arabia. The ANN model utilized in this work included an input layer with 15 parameters, a hidden layer of 30 neurons, and an output layer with 8 neurons. The ANN model was trained with 27 patterns. Seven patterns were utilized for testing purposes. The coefficient of determination (R²) obtained between the observed and predicted performance parameters’ values using the testing dataset was 0.902 for the number of strands per spathe, 0.967 for strand length, 0.963 for the number of flowers per strand, 0.941 for the number of flowers per spathe, 0.985 for the weight of pollen grains per spathe, 0.810 for the pollen grains’ viability, 0.936 for the pollen grains’ length, and 0.992 for the pollen grains’ width. The length of the spathe stem had the most critical effect on how the ANN model predicted the values of the dependent variables, i.e., the number of strands per spathe, with a percentage of contribution of 17.66%; the weight of pollen grains per spathe, with 17.85%; the pollen grains’ length, with 19.78%, and the pollen grains’ width, with a percentage of contribution of 30.59%. Spathe weight had the most critical influence on strand length and pollen grains’ viability, with percentages of 26.29% and 14.92%, respectively. Leaf width had the most critical effect on the number of flowers per spathe, with a percentage of 12.55%. The elemental concentration of K in the male date palm leaves had the most critical effect on the number of flowers per strand, with a percentage of 13.98%. It was therefore concluded that using a modeling process with the ANN technique can help estimate the performance parameters of male date palms’ pollen grains for different purposes, such as providing a starting point for mathematical analyses associated with the physiological mechanisms of male date palm. Moreover, the outcomes of this research work can be supportive as a practical tool in this field of study.