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One of the most important and effective issues in the future of agricultural technologies is how to deal with climate and environmental changes. Todays, the problem of reducing available water is a threat not only to Iran but also to the whole world. But in the meantime, the countries of the Middle East, including Iran, have a more meaningful share of this threat of drought. According to the published statistics, the share of water consumption per capita in the agricultural sector is much higher than in the urban and industrial sectors. Water consumption share in this sector is announced up to 91%. Meanwhile, the global average amount of water consumption in the agricultural sector is less than 70%. Therefore, it is necessary to provide solutions for reforming agricultural systems that use little water or are suitable for water shortage conditions. Mechanization, as a developmental stage of any cultivation, can lead to corrective agricultural systems suitable for water crisis conditions. In this research, at first, developed agricultural systems were described which aim to reduce water consumption. The position of mechanization in agricultural systems, including sustainable and conservation agriculture, was investigated. In the following, mechanization studies and indicators including water and energy productivity and water supply cost were discussed in the case of water shortage-mechanization crisis. In a sustainable agricultural system, the use of modern, intelligent, and suitable equipment for Iran's conditions, along with the development of modern irrigation methods, can greatly contribute to the water shortage crisis.

Introduction :

Sugarcane is a tropical, perennial grass that forms lateral shoots at the base to produce multiple stems. It is the main source of sugar production and one of the most important sources of energy production in the world. Today, the use of artificial intelligence and data mining findings to help predict product production is considered. Determining the relationship between inputs and outputs of production process using artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and producing results without any prior assumptions. The adaptive neuro-fuzzy inference system (ANFIS), as a form of AI, is a combination of artificial neural network (ANN) and fuzzy systems that uses the learning capability of the ANN to derive the fuzzy if-then rules with appropriate membership functions worked out from the training pairs, which in turn leads to the inference.Particle swarm optimization (PSO) is an algorithm modeled on swarm intelligence, in a search space, or model it finds a solution to an optimization problem and predict social behavior in the presence of objectives. The PSO is a population-based stochastic computer algorithm, modeled on swarm intelligence. Swarm intelligence is based on social psychological principles and it provides insights into social behavior, also helps to many engineering applications. Feature selection is becoming very important in predictive analytics. Indeed, many data sets contain a large number of features, so we have to select the most useful ones. One of the most advanced methods to do that is the genetic algorithm (GA). Genetic algorithms can select the best subset of variables for predictive model. The purpose of this research is to evaluate the applicability of one artificial intelligence technique including adaptive neuro-fuzzy inference system and also combining this technique with particle swarm optimization to increase the accuracy and speed of training of the neuro-fuzzy system in prediction of yield and recoverable sugar percentage (R.S%) of sugarcane.

Materials and Methods:

 In this paper, one main pattern of adaptive neuro-fuzzy inference system (ANFIS) and one synthetic model of adaptive neuro-fuzzy inference system with particle swarm optimization (PSO) were used to predict the studied properties by MATLAB version 2017. Initial data for this study were collected from Debal-Khozaie Agro-industry Company in Khouzestan province, Iran. The actual data for the seven periods of sugarcane harvest from 2010 to 2017 were used for modeling. The studied parameters included a set of agronomic factors, soil characteristics, irrigation and climate in the study area. The test data sets were used for comparison of selected ANFIS and ANFIS with PSO, as well as for the observation values. This comparison was performed by using three statistical indices: Determination Coefficient (R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).

Results and Discussion:

From all of the studied parameters, eleven parameters were selected as the effective features by the binary genetic algorithm (BGA). In feature selection, the function to optimize is the generalization performance of a predictive model. More specifically, in this method, purpose was to minimize the error of the model on an independent data set not used to create the model. The data were randomly divided into two groups: training and testing. Each pattern was modeled separately and then the results were compared. The results showed that the combination of adaptive neuro-fuzzy inference system with particle swarm optimization algorithm (ANFIS-PSO) had better performance in predicting cane yield and recoverable sugar percentage. In ANFIS-PSO model the root mean square error, mean absolute percentage error and coefficient of determination values were found 0.0181, 0.0217, 0.9237 and 0.0086, 0.0138, 0.9847 respectively for two variables of cane yield and recoverable sugar percentage. In relation to the predicted cane yield by the neuro-fuzzy network with particle swarm algorithm, it can be concluded that among the effective factors, with increasing plant age and use of resistant varieties, the amount of yield was decreased and increased, respectively.

Conclusion:

 The hybrid pattern of adaptive neuro-fuzzy inference system with the particle swarm optimization has been directed against the mere neuro-fuzzy system to a more accurate and stronger solution. Indeed, it can be concluded that ANFIS model with the PSO has the ability for precise estimation of sugarcane yield and recoverable sugar percentage.

According to the high importance of sustainable crop production in the agro-industry units, intelligent systems such as artificial neural networks should be used to manage farm units.Therefore, the main purpose of this study was to compare the performance of MLP (Multi-Layer Perceptron) and RBF (Radial Basis Functions) neural network models in order to modeling and estimating of the sugarcane crop yield and investigate the factors affecting it.  

The study was analytical and its database contained of a matrix  elements. Required data for this research were obtained from the Debel Khazaei sugar cane agro-industry farm during the years 2016 to 2019. The input variables and their units were soil electrical conductivity (dS.m-1), Phosphate and Nitrogen chemical fertilizer (kg.ha-1), water consumption (m3.ha-1), also, irrigation times, month of harvest, age of crop, sugarcane variety, soil texture (non-dimensional), respectively. The analysis was performed by MATLAB 2017 software.  

By comparing the error parameters of RMSE (Root Mean Square Error) and the MAPE (Mean Absolute Percentage Error), and according to indexes of R2 (coefficient of determination) and the EF (Model Efficiency) and, in the validation phase the RBF model was the best model with 0.064494 (%), 0.037686, 0.7576 and 0.800409 (non-dimensional) respectively. Also, the RBF model indicated that the sugarcane variety and soil electrical conductivity were the most important factors affecting the sugar-cane yield.  

By selecting the appropriate variety of sugarcane and controlling the amount of electrical conductivity of the soil, the yield per unit area can be increased, resulting in greater productivity of the inputs and more sustainable production.

In this study, to predict sugarcane yield, an Artificial Neural Network based on meta-heuristic algorithms is used as an efficient method to estimate crop yield based on actual data. In order to predict sugarcane yield, effective parameters such as plant characteristics, electrical conductivity of soil and water, maximum temperature and average hours of sunshine during the growing season and on a time scale of seven years were used. Accordingly, four hybrid models were used to build neural networks which including of artificial neural network based on Back Propagation (BP) algorithm, combining neural network with Genetic Algorithm (GA), combining neural network with Particle Swarm Optimization (PSO) algorithm and finally a new approach as combining neural network with GA-PSO. The results show that the neural network performance can be improved using meta-heuristic algorithms and can be increased significantly the prediction power. The Mean Square Error (MSE) and correlation coefficient (R) in the hybrid method of neural network with GA-PSO were obtained 0.00057 and 0.91457 respectively on the test data, which show the superiority of this method to other patterns. In addition, cross validation test of the proposed model by K-Nearest Neighbors showed the accuracy of training and test data to predict sugarcane yield has been equal to 98.5% and 95.5%, respectively.

Potato (Solanumtuberosum L.) is one of the unique and most potential crops having high productivity, supplementing major food requirement in the world. Drying is generally carried out for two main reasons, one to reduce the water activity which eventually increases the shelf life of food and second to reduce the weight and bulk of food for cheaper transport and storage. The quality evaluation of the dried product was carried out on the basis of response variables such as rehydration ratio, shrinkage percentage, color and the overall acceptability. Drying is the most energy intensive process in food industry. Therefore, new drying techniques and dryers must be designed and studied to minimize the energy cost in drying process. Considering the fact that the highest energy consumption in agriculture is associated with drying operations, different drying methods can be evaluated to determine and compare the energy requirements for drying a particular product. Thermal drying operations are found in almost all industrial sectors and are known, according to various estimates, to consume 10-25% of the national industrial energy in the developed world. Infrared radiation drying has the unique characteristics of energy transfer mechanism. Kantrong et al. (2012) were studied the drying characteristics and quality of shiitake mushroom undergoing microwave-vacuum combined with infrared drying. Motevali et al. (2011) were evaluated energy consumption for drying of mushroom slices using various drying methods including hot air, microwave, vacuum, infrared, microwave-vacuum and hot air-infrared. The objectives of this research were to experimental study of drying kinetics considering quality characteristics including the rehydration and color distribution of potato slices in a vacuum- infrared dryer and also assessment of specific energy consumption and thermal utilization efficiency of potato slices during drying process.
A laboratory scale vacuum-infrared dryer, developed at the Agricultural Machinery and Mechanization Engineering Laboratory of Shahid Chamran University of Ahvaz has been used. The dryer consists of a stainless steel drying chamber; a laboratory type piston vacuum pump, which was used to maintain vacuum in the drying chamber; an infrared lamp with power of 250 W which was used to supply thermal radiation to a drying product; and a control system for the infrared radiator.
Sample Preparation
Fresh potatoes were purchased from a local market in Hamadan province. Potatoes were peeled, washed, and cut into sliced with thickness of 1, 2 and 3 mm by a manual slicer. Drying experiments of potato slices were performed in a vacuum chamber with absolute pressure levels of 20, 80, 140 and 760 mmHg; and radiation intensity of infrared lamp was 0.2, 0.3 and 0.4 W cm-2. The mass change of the sample during drying was detected continuously using an electronic weight scale (Lutron, GM- 1500P, Taiwan) with the accuracy of ±0.05 g.
Evaluation of rehydration capacity of dried potato slices
The rehydration tests measured the gain in weight of dehydrated samples (~5 g), dehydrated samples were rehydrated in 200 cc of distilled water at 100°C for 3 minutes.
Evaluation of color
The color of potatoes was measured on five slices selected randomly, and was described by three coordinates in the RGB color space using computer vision.
Evaluation of specific energy consumption
Energy consumption of dying process came from the electrical energy consumed by the operation of the vacuum pump and the infrared lamp. Specific energy consumption was defined as the energy required for removing a unit mass of water in drying the potato slice.
Evaluation of thermal utilization efficiency
Thermal utilization efficiency is defined as the latent heat of vaporization of moisture of sample to the amount of energy required to evaporate moisture from free water. The latent heat of vaporization of water at the evaporating temperature of 100°C was taken as 2257 kJkg-1.
The results of the evaluation of rehydration capacity of potato slices during drying process are shown in Table 1. Statistical analysis (ANOVA, post-hoc Duncan) showed that thickness at probability level of 1% had statistically significant influence on rehydration capacity values of dried potato slices. Moisture of dried slice of potato compared to its fresh was obtained nearly 80% in boiling water (at temperature 100°C) for 3 min. The most color changes of slice after drying was related to green color. According to Table 2 and statistical analysis results showed that factor of thickness was not statistically significant on specific energy. The effect of absolute pressure (p Dried potato slices at a thickness of 1 mm put in boiling water for three minutes; showed the most amount of water absorption ratio that it was able to absorb the value of 86% more than the initial moisture. The lowest rate of color change before and after the drying process is related to the thickness of the thinnest sliced potatoes. Comparison of energy consumption showed that the radiation intensity of 0.4 W cm-2, absolute pressure level of 80 mmHg and slice thickness of 1 mm had shorter drying time in experimental conditions.

In this study, a single layer sliced potato was dried using an infrared lamp heating under vacuum over three levels of radiant power including 100, 150 and 200 W, three slices thickness 1, 2 and 3 mm and the absolute pressure of 20, 80, 140 and 760 mmHg. Samples were dried in triplicate to achieve 6% moisture content (wet based) which is suitable for long-term storage. Drying rate was monitored during experiments. The shrinkage of sliced potato was measured using image processing. The results showed that increasing infrared radiation and reducing absolute pressure at the same slice thickness, could cause drying time to be reduced but shrinkage increased. Thickness of the slices and infrared radiation power had a significant impact (P