The adverse effects of population growth on food resources have been studied in several studies [
1]. Food security has become more than ever a national security matter of various countries worldwide [
2]. The food and agriculture organization (FAO) also emphasizes food security as a measure to facilitate the access of all people to sufficient, safe, and nutritious food to satisfy dietary needs and appetite preferences for an active and healthy life. With regard to this definition, agricultural policymakers must ensure that there is enough food for the communities’ dietary needs [
3]. Therefore, they should pay more attention to foodstuff forecasting methods [
4]. In 2013, the total harvested area was 218.4, the average yield was 3264 kg per hectare, and the total wheat production was equal to 713 million tons. Wheat is an essential plant in Iran, with more than 50% of entire arable lands allocated to it [
5]. Thus, in-depth insight into the production and energy usage are of utmost importance for food security and energy planning.
Global wheat crop condition, mostly favorable provided by the Agricultural Market Information System (AMIS) [
6], shows that the European Union ranks first followed by China and India, regarding wheat production.
Table 1 represents the global wheat production and its projection in a million tons. It shows that EU ranks first, following by China, India, and USA, respectively [
7].
The total area under wheat cultivation in the 2014 crop year was reported to be 6.4 million hectares. The total wheat harvest of the country was 12 million tons. Eight million tons of this amount was irrigated wheat. The average yield of irrigated wheat is 3.5 tons per hectare [
8]. In 2015, the total cultivated area for wheat was estimated to be 5.7 million hectares. The country produces about 11.5 million tons of wheat. Fars is the second-highest wheat-producing province with 10.19% of Iran’s total wheat production. Irrigated wheat yields an average of 3993.2 kg per hectare in Fars from 2015 to 2016. Agricultural policymakers in Iran believe that the exact amount of wheat production in the country must be assessed. Importing excess wheat leads to a price reduction in the country. This can decrease farmers’ profits or even cause them to be looser. On the other hand, insufficient wheat imports can lead to unmet demands and an increase in the wheat price, so people cannot afford their annual wheat costs. Engineers in various fields are interested in analyzing current and past data for future predictions using a variety of techniques, such as statistics, modeling, time series, and learning machines.
With each publishing, a variety of crop status maps are produced and distributed. A propagation map showing crop conditions in the major wheat-growing areas was created at the Commission’s request. A visual summary of global AMIS crop conditions for the study is now accessible in the area of the wheat production chart (
Figure 1). The standalone crop report and the website provide further crop-specific and seasonal maps.
Figure 1 provides information about the wheat-growing location in Iran, adapted from [
9]. It shows that Khorasan Province in the northeast and Fars Province is the south of Iran are significant locations for wheat production.
The main goal of this study was to predict the total amount of required energy for wheat production. For this purpose, city of Estahban in Fars Province of Iran was nominated as the case study. This research’s contribution is that the ELM and SVR methods are used as methods to forecast energy output to determine the best method with the least forecast error for prediction of energy in wheat production of the Iranian city of Estabhan for the first time. Another contribution that highlights this research is the selected seven Input data used for analyzing the methods.
Literature Review
In recent years, many researchers have analyzed the energy consumption for producing agricultural products. In the past 15 years, neural networks have attracted considerable attention. Artificial neural network (ANN) models are based on the biological activities of neurons. Biological neural networks have turned into a critical modeling technique that is used more than other complex input-output methods. ANNs are good for some tasks but not for others. They can learn from examples and solve nonlinear problems (support vector regression) [
12]. The support vector machine (SVM) method was considered better than ANN in late 1990, because it gave attractive and better solutions in problems. SVR theory is developed from computational theory, but the development in ANNS is more heuristic. While ANNs minimize empirical risk or training error, SVMs minimize structural risk. In SVR, the objective function is convex, so the global optimum is always reached [
13]. SVM is used for discrete data, and SVR is used for continuous data [
14]. In a study by Hosseinzadeh-Bandbafha et al. [
4] on energy consumption and efficiency in dairy farms in Qazvin, Iran, they estimated the greenhouse gas emissions due to energy consumption in these farms and attempted to optimize the energy use of the farms to reduce the emission rate and the total emission produced. Memon et al. [
15] studied the energy consumption pattern in wheat production in Pakistan and concluded that wheat cultivation achieved the highest net energy.
In a study by Zangeneh et al. [
16] on potato production units’ energy consumption in Hamadan, Iran, these units’ energy consumption was determined through in-person interviews with 100 farmers. The farmers were divided into 1: 68 farmers with a high farming machinery level, and 2: consisting of 32 farmers with a low level of farming technology and without machinery. The researchers concluded that farming machinery is necessary to improve the amount of potato production regarding the benefit to cost ratio. Proximal support vector machine (PSVM) and least square support vector machine (LS-SVM) models are derived from SVM that have a higher speed.
Single hidden layer feedforward neural networks (SLFNs) are learning platforms that can widely use feature mappings [
17]. ELM is a complex learning algorithm for SLFNs, randomly selecting the input weights matrix and the hidden layer biases [
18]. Neumann et al. [
19] showed that ELM is appropriate for this purpose.
Nath et al. [
20] published an autoregressive integrated moving average (ARIMA) simulation method analysis on the estimation of wheat production in India. The wheat production in India was projected with a time series simulation method. The optimal ARIMA configuration for the analysis was identified to be ARIMA (1, 1,0). The goal was to predict future wheat production by adapting ARIMA (1,1.0) to our time series data for up to 10 years as accurately as possible. The outcomes of the forecasts suggest that the annual production in 2026 and 2027 would increase. With an estimated annual growth rate of about 4%, wheat production will continue to grow. The long short-term memory (LSTM) neuronal network forecasting model for wheat production in Pakistan was reported by Haider et al. [
21]. This paper is concerned with creating an effective wheat production prediction model utilizing neural networks with the use of (LSTM). In combination with the LSTM model, a smoothing data preprocessing method is used to enhance predictive precision further. Santamaría-Artigas et al. [
22] carried out an evaluation of the near-surface air temperatures arising from the US and Ukraine’s reanalysis. This paper analyses ERA-Interim (ERAI), Japanese 55-year reanalysis, Modern-Era Research and Development Retrospective Analysis Version 2, and NCEP1 and NCEP2 reanalysis works for near-surface air sites. The re-analysis data were first related to measurements from weather stations in the US and Ukraine and then analyzed within a winter wheat yield model. The data was validated in the United States and Ukraine. The evaluation of the weather station results indicated that all the data samples worked properly (r 2 > 0.95) and more recent re-analysis, like ERAI, had smaller root-mean-square deviation (RMSD ~0.9 °C) errors relative to traditional high-resolution datasets, such as NCEP1 (RMSD ~2.4 °C).
The multi-scale and multi-model gridded method for assessing crop production, risk analysis, and impact studies on climate change was introduced by Shelia et al. [
23]. This paper provides an overview of gridded crop models and yield forecasts, together with risk analysis and climate impact analyses, methods, techniques, prototypes, and capabilities for the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). Yazdani [
24] completed an economic and scientific evaluation of environmental components in Tabriz and Isfahan (Iran). In this analysis, various climatic variables, including temperature, precipitation, and freezing, were measured to determine the economic value of the environment. For the Tabriz and Isfahan agriculture areas, four products were selected, namely arable wheat, dry farm wheat, arable barley, and dry farm barley. Ram et al. [
25] examined health identification of wheat crop utilizing pattern recognition and image processing. The writers came to incorporate a form of pattern identification and an approach of image processing. The system allows a farmer to adopt a particular crop trend in order to assess risks sooner. Combining it with the power of the Internet of Things (IoT), individuals without the need of human resources will simplify the process. Ultimately, this work will speed up farming and enable farmers to grow more in less time.
In a study by Ali and Deo [
26], the wheat yield was modeled by a data-intelligent algorithm based on the artificial neural network and genetic programming and minimax probability machine regression (MPMR) results were compared. The criteria used for this comparison were correlation (
r), Willmott’s index (
WI), Nash–Sutcliffe coefficient (
EV), root-mean-square error (
RMSE), and mean absolute error (
MAE). The
r,
WI, and
EV values obtained for station 1 were as follows: for the ANN model,
r ≈ 0.983,
WI ≈ 0.984, and
EV ≈ 0.962; for the MPMR model,
r ≈ 0.957,
WI ≈ 0.544, and
EV ≈ 0.527; and for the genetic programming (GP) model,
r ≈ 0.982,
WI ≈ 0.980, and
EV ≈ 0.955. The
RMSE and
MAE values obtained for the optimal ANN model (
RMSE ≈ 192.02;
MAE ≈ 162.75) were lower than those for the MPMR model (614.46 kg/ha;
MAE ≈ 431.29 kg/ha) and for the GP model (
RMSE ≈ 209.25 kg/ha;
MAE ≈ 182.84 kg/ha). For both stations, ANN outperformed GP and MPMR in terms of RMSE and MAE and the Legates–McCabe index (
LM). These results demonstrated the excellent capability of ANN as a data-intelligent algorithm in the forecasting of wheat yield based on the nearest neighbor scheme. Salim and Raza [
27] conducted a study of the sustainable wheat production nutrient use efficiency (NUE) research. Kamir et al. [
28] did a study on forecasting wheat yields in Australia using weather data, time series for satellite images, and machine learning processes. The machine-learning regression models also showed superior performance compared to the methods based on peak normalized difference vegetation index (NDVI) and harvest index (R
2 < 0.46).
Pantazi et al. [
29] introduced three based models, of supervised Kohonen networks (SKNs), counter-propagation artificial networks (CP-ANNs), and XY-fusion (XY-F) that implement supervised learning for associating high-resolution data on soil and crop, which utilized iso-frequency classes of yield productivity for wheat. They concluded that the SKN model had better accuracy for prediction. Amato et al. [
30] introduced a novel multimedia summarization model from online social networks (OSNs). They focused on the management and sharing of multimedia information. They proposed the summary of the model and heuristics to get a multimedia summary with priority, continuity, variety, and not repetitive features. The results were also validated. Wang et al. [
31] carried out efforts to improve the surface energy balance network’s meteorological feedback using the mesoscale environment analysis and prevision model. Comparisons of data collected at the weather station were carried out to determine the quality of weather research and forecasting (WRF) simulation. The results showed high agreement between the reports of meteorological stations and wind speed (R
2 = 0.628), air temperature (R
2 = 0.8242), relative humidity (R
2 = 0.8089), and surface pressure (R
2 = 0.8915) values obtained from WRF. According to the above notions, this study aimed to improve the accuracy of the amount of wheat production forecast in Estahban using energy input. The exact forecasting of the harvested amount at the end of the harvest season is essential for import or export planning by policymakers in this field. We used SVR and ELM methods to forecast the wheat yield. Consequently, the research questions are as follows.
How much energy is required to produce wheat in Estahban?
Is it efficient to produce wheat regarding energy consumption?
Which method has higher accuracy for prediction?