Agricultural Engineering
Year:2020 | Volume:43 | Issue:2|Papers Count:8

Introduction: Edible Button Mushroom (Agaricusbisporus) is one of the crops that is widely used today as a food source. Mushrooms after harvesting due to high humidity, high respiration rate, lack of cuticle and severe enzymatic activity, with persistence and quickly than other vegetables rot and discoloration begins immediately after harvest. To increase shelf life, edible mushroom must undergo processing processes. Drying is one of the most common methods of processing and preserving edible mushrooms. Vacuum-infrared drying is conducted by lowering moisture at low pressure to improve the quality of the high nutritional value product. Since button mushrooms have many applications due to their high nutritional value and medicinal uses, the best drying mode should be chosen to have the least negative effect on the quality properties and ingredients of the powder. Materials and Methods: Fresh edible button mushroom After washing were cut by a cutter at 5 mm thickness and dried using a vacuum-infrared dryer at three temperature levels of 40, 55 and 70 ° C and three vacuum pressure levels of 20, 40 and 60 kPa. Then the dried mushroom slices were milled and powdered using a mill machine for one minute. To homogenize the particle size, the button mushroom powder was sifted by a laboratory sieve with mesh No. 50 (cavity size 0. 5 mm). In this study, the effect of vacuum-infrared drying variables including indoor air temperature and vacuum pressure on the thermal properties (effective moisture diffusion coefficient and drying energy consumption) of button mushroom and chemical (total phenol content) and qualitative (color indices as Δ L *, Δ a * and Δ b*) button mushroom powders were studied. Statistical analysis of data and optimization of drying process were performed using response surface methodology and central composite design (CCD). After determining the optimum point of vacuum-infrared dryer, loose and compacted bulk density, work index, Hassner ratio, angle of repose, and button mushroom powder slides were measured at optimum point and Finally the flow-ability of the edible button mushroom powder was determined. Results and Discussion: The results showed that as the chamber temperature increased, the rate of evaporation of tissue moisture increased, which resulted in a decrease in the drying time of the edible button mushroom thin layers with vacuum-infrared dryer. Effective moisture diffusion coefficient of drying of edible button mushroom thin films ranging from 1. 8 ×10-9 m2/s (40 kPa pressure and temperature 40 ° C) to 8. 9×10-9 m2/s (20 kPa pressure and 70 ° C temperature) was varied. The results showed that the air temperature of the drying chamber had a positive effect on the effective moisture diffusion coefficient. This is because increasing energy and heat consumption increased the activity of water molecules and, as a result, more moisture penetrated outside the product at higher temperatures. The maximum amount of specific energy consumption was 1269. 73 MJ/kg (60 kPa pressure and 40 ° C) and the lowest amount was 408. 36 MJ/kg (40 kPa pressure and 70 ° C). The results showed that at constant pressure with increasing temperature, as the drying time decreased sharply, the amount of specific energy consumption also decreased. The phenolic content of button mushroom powder was in the range of 270 mg/g (20 kPa pressure and 40 ° C) and 1. 3 mg/g (40 kPa pressure and 70 ° C). As the temperature increased, the total phenol content decreased. The results showed that increasing the temperature caused a greater difference between the color indices of L*, a * and b* of button mushroom powder than fresh mushroom. Increase in temperature caused more darkening (decrease in L* index), decrease in redness (decrease in index a*) and decrease in yellowness (decrease in index b*) of mushroom powder. In general, color indices were closer to the values of fresh fungal samples at low temperatures. The optimum drying point of button mushroom was obtained at 40° C and vacuum pressure of 40. 823 kPa. The optimum value of the independent variables including effective moisture diffusion coefficient, specific drying energy consumption, total phenol content and final color indices of edible button mushroom Δ L*, Δ a* and Δ b* were 3. 06×10-9 m2/s, 1088 MJ/kg, 2. 76 mg/g, 15. 28, 2. 55 and 9. 26, respectively. The results showed that drying under lower temperature and medium vacuum pressure increased the desirability index. The flow-ability of edible button mushroom powder was reported to be good. Conclusion: According to the results of drying tests of edible mushrooms, the following results of this study are obtained in infrared vacuum drying: 1-The effect of air temperature on all variables of button mushroom response was significant in vacuum-infrared dryer. 2-The air inlet temperature to the dryer had a negative effect on the specific energy consumption of the drying process and the total phenol content of the button mushroom powder. 3-Increase in air temperature caused a greater difference between the color indices of L*, a* and b* button mushroom powder than fresh mushrooms. 4. The results showed that drying under mild conditions (lower temperature and medium vacuum pressure) increased the desirability index. 5-Flow-ability of edible button mushroom powder was reported to be good.

Introduction: Due to the use of fossil fuels, land use changes, and deforestation, it increases atmospheric carbon dioxide, which affects greenhouse gas emissions, results in global warming, effectively. Since crop production directly depends on climate, agriculture is one of the first sectors affected by climate change. Increasing greenhouse gas emissions leads to warming up and warming has devastating effects on organisms life, damaging natural ecosystems, causing floods, droughts, and disrupting the climate and ecological balance. Total soil organic carbon is the ability of trees and other plants to absorb carbon dioxide from theatmosphere and store it as carbon in wood, roots, leaves, and soil. Total soil organic carbon of plant biomass and Total soil organic carbon under this biomass is the simplest and most economically feasible solution to reduce atmospheric CO2. In this regard, an experiment was carried to investigate the effect of three amendment materials (alfalfa residues, straw and wheat straw, and poultry manure) on some soil characteristics, soil and wheat organ Total soil organic carbon potential. Materials and Methods: To investigate the possibility ofimproving soil carbon sequestration, carbon content of plant and soil and some soil characteristics, an experiment was design in a randomized complete block design (RCBD) in the crop year 2018-2019, in a farm in Dasht-e Aramou, Dare Shahr-Ilam province, in three replications on the wheat plant. Trial factors include two factors, the types and amount of amendment materials (alfalfa residues at 5, 10 and 15 t/ha, straw and wheat straw at 5, 10 and 15 t/ha, poultry manure at three levels of 2, 4 and 6 t/ha and chemical fertilizer is 100 percent fertilizer requirement). The studied traits included root carbon, shoot carbon, root total organic carbon, and shoot total organic carbon, total organic carbon, soil organic carbon percentage, total soil organic carbon, soil nitrogen, soil phosphorus, soil potassium, soil pH and soil Electrical Conductivity (EC). Results and Discussion: The use of amendment materials had a positive effect on most of the studied traits compared with the lack of application of amendment materials. The results showed that the amount of Total soil organic carbon and the percentage of carbon in shoots were higher than roots. The highest total organic carbon, percentage of carbon in plant and soil phosphorus were observed in 6 t/ha poultry manure (M6) while the highestTotal soil organic carbon and soil carbon content was obtained in 15 t/ha straw and wheat straw (G15). Also, the highest amount of soil nitrogen and potassium was obtained in 15 t/ha (Y15) alfalfa residues and the lowest amount in control treatment which were 47and 64 percent higher than the control, respectively. Contrary to all measured traits, pH and EC values were decreased by adding soil amendment materials. The highest was obtained in control treatment and lowest was observed in 15 t/ha (G15) straw and wheat straw which was 4. 4 percent and 50. 8 percent lower than the control, respectively. Conclusion: Gradual degradation of organic matter increases the efficiency of nutrients, the effect of these compounds on the plant's yield and soil properties for several years. The use of high quality plant residues, if combined with optimized management, will have a good result, especially if the timing of the release of nutrients from decomposing plant debris coincides with the need for the crop. Under such conditions, the time gap between the release of elements from plant residues and absorption of elements by the plant will be reduced and by reducing nutrition elements, it will increase absorption efficiency. In general, the effect of fertilizer type and plant residue on the amount of carbon of the plant and soil as well as the amount of nutrients in the soil was significant at 1% level. Among the different treatments, 6 ton/ha of poultry manure had the most effect on total soil organic carbon and carbon storage in plant organs, and treatment of 15 ton/ha wheat straw had the most effect on total soil organic carbon and carbon storage in soil. Alfalfa residue treatment had the most effect on soil phosphorus and potassium content and poultry manure had the most effect on soil nitrogen. Regarding to the lower price of plant residue, it is more appropriate than poultry. Due to availability of poultrymanure in the most parts of the country, it recommends more than other fertilizers.

Introduction: planning is essential for increasing production per unit area of strategic agricultural products. In this regard, land suitability evaluation is one of the most important key steps. While the FAO framework for land suitability evaluation is the most commonly used method, to overcome problems related to vagueness in definition and other uncertainties, methods such as Micro LEIS system models can be useful. Terraza, Cervatana and Almagra are the models of Micro LEIS package that are used to evaluate land suitability for agricultural activity. Considering the limitations in water resources required to produce this strategic product, the present research focuses on evaluation of Khodaafarin region lands suitability by Micro LEIS models for this utilization type. Materials and Methods: This research was carried out in an area of about 16555 ha in Khodaafarin, an important region in agricultural production, located in East Azerbaijan province, northwest of Iran. The major geological formation is composed of Quaternary sediments with sandstone. According to climatic data from Khomarlo synoptic weather forecasting data station, the average annual precipitation and temperature of the region are 281 mm and 14. 7 ° C, respectively, and the soil moisture and temperature regimes are Aridic border to Xeric and Thermic. The study area was divided to 11 land units by geopedology method. In each land unit, a soil profile was dug, described and sampled for physico-chemical analysis. After preparing soil samples in the laboratory, soil physico-chemical routine analyses, which are important in soil classification and evaluation, were completed by standard methods, and then studied soils were classified by the 12th edition of soil taxonomy. To conduct land suitability investigations, three models of Micro LEIS package including Terraza (to determine bioclimatic deficiency), Cervatana (to determine land capability) and Almagra (to determine the suitability evaluation of the studied area) were used. Finally, according to adopted models, land capability and suitability class and sub-class were determined in actual and potential condition for sugar beet, and their maps were prepared by Arc Map 10. 2. Results and Discussion: According to the results obtained from description of soil profiles and physical and chemical analyses, soils can be classified as a different family of Aridisols and Entisols on the basis of USDA soil taxonomy system. Based on the Terraza model, bioclimatic deficiency class for the studied utilization type is C3 (h2-f3) or h2 moisture deficit and f3 frost risk classes, whose combination leads to C3 final class. This class revealed that during the growth period, 2-5 months annually the temperature goes under biological zero and production would decrease by 20 to 40%. Therefore, it is recommended to have safe irrigation and frost risk management. The results of Cervatana model indicated that for slope, climate, erosion and soil limitations with various degrees, 77. 01% of the study area was placed in S2 land capability class. Also, 18. 16% and 4. 83% were classified as S3 and N1 classes, respectively. Among the above-mentioned factors, soil depth limitation was identified as the most restrictive and climate as the mildest one all over the study area. According to S2 class suggested by the Almagra model, 82. 98% of the study area is acceptable for this utilization type. Suitability class for 7. 62% of the study area is S3, which would increase the costs. Also, 9. 4% of the study area is never suitable for sugar beet due to intensive limitation of soil useful depth and texture, which leads to S5 class. The nature of the limiting factors in land units suggests that they might be resolved. Based on this fact, in 19. 64% of study area suitability class can shift to the upper one due to land improvement, while in 22. 99% of the study area it is impossible for such a shift. In other parts, although some of the limitations can be solved, land suitability class remains stable and only their sub-class changes. Conclusion Considering land evaluation and the nature and professional use of Micro LEIS system models, Terrraza, Cervatana and Almagra models should be used hierarchically. Hierarchical use of models can help reduce costs because it is designed as a multiple-stage approach of land evaluation that assesses various biophysical properties of lands step by step. Accommodation of the values related to the studied land properties within the specified range presented in the models indicates that these models are calibrated for evaluating this utilization type in north-west of Iran. Therefore, traditional evaluation systems can be replaced by Micro LEIS system. According to the obtained actual and potential suitability class for sugar beet, it seems that it is a reasonable utilization type in the study area. Land improvement does not have a significant effect on the rise of land suitability class and profitability; nevertheless, it is recommended to have pilot land improvement programs to find factors which might have been ignored and might bring about further limitations.

Introduction: Honeybees play an important role in pollination. However, there are many problems that threaten the life of them. Pollinators can be exposed to insecticides during their application, by contact with residues, or from the ingestion of pollen, nectar or guttation fluid containing insecticide. The increasing use of neonicotinoids means there is a greater potential for pollinators to be exposed over longer periods as systemic insecticides can be found in the pollen and nectar of plants throughout their blooming period (Ellis, 2010). Exposure to insecticides may have lethal or sub-lethal behavioral or physiological effects. The impact of imidacloprid on homing flight was evaluated in field with a 500-m-distance between feeder and hive (Bortolotti et al. 2003). At the concentration of 100 lg kg-1 foragers fed with imidacloprid-added syrup returned to the hive, but this treatment caused a temporary inhibition of the foraging activity, lasting more than 5 h. Foragers fed with 500 and 1000 lg kg-1 of imidacloprid were seen neither at the hive nor at the feeding site, for the 24 h after the treatment (Bortolotti et al. 2003). Decourtye et al (2011) have shown how the RFID device can be used to study the effects of pesticides on both the behavioral traits and the lifespan of bees. In this context, they have developed a method under tunnel to automatically record the displacements of foragers individualized with RFID tags and to detect the alteration of the flight pattern between an artificial feeder and the hive. Fipronil was selected as test substance due to the lack of information on the effects of this insecticide on the foraging behavior of free-flying bees. They showed that oral treatment of 0. 3 ng of fipronil per bee (LD50/20) reduced the number of foraging trips. Therefore, the aim of this study was to monitoring and determination honeybee’ s behavior in exposure to pesticide using data mining techniques. Materials and Methods: Three smart beehive systems developed to monitoring of hive internal conditions. Therefore, each beehive equipped with temperature and humidity (HDC1080, China), vibration (MPU6050, China), and CO2 (CCS811, China) sensors. Data was collected during spraying time for 48 hours and different features of vibration signal in two time-frequency and frequency domains were extracted by MFCC (Mel-Frequency Cepstral Coefficient) algorithm. After that, the most significant features were selected using PCA (Principle Component Analysis) which has been used specifically for extracting information from correlation matrices. Since the spectral dataforms the array of correlated variables containing overlapped information, this approach makes it possible to extractuseful information from high-dimensional data. To choose thenumber of components the cross-validationmethod was used. The extracted principal components wereused as the input variables for the classification model. In this paper, support vector machine with different kernel function including linear, polynomial, MLP, RBF, and quadratic was applied for performing classification. Results and discussion: According to the MFCC of internal vibration results, there were dramatic changes in the range of 1800 to 2200 Hz in the time of spraying; also, Spectrogram of MFCC coefficients for the X component acceleration shown intensity of 350 in the frequency of 2000 Hz and time range of 60 to 120 minutes; besides, humidity (8 to 18 %), the amount of CO2 (450 to 530 ppm) and temperature (35 to 39 C) increased during this time. To reduce the dimensionality of data five PCs with minimum estimated mean squared prediction error (0. 078) were selected based on Monte Carlo method and used in classifier. Among the five kernels (RBF, linear, MLP, Polynomial, Quadratic), RBF could recognize normal and infected colony with identification rate of 100% and 90%, respectively. Conclusions According to the results temperature, humidity, CO2, and vibration sensors can recognize internal condition of bee hive. Vibration features of honey bees movements were extracted using MFCC followed by PCA in frequency-time domain. Five PCs was selected by cross-validation method and RBF kernel was the best kernel with identification rate of 100% and 90% for normal and infected beehive, respectively. Generally, the vibration signals (that were recorded by acceleration sensor) have shown the best result compare to temperature, CO2, and humidity sensors. It is worth nothing that the use of two temperature and humidity sensors is necessary to monitor and control of beehive internal conditions.

Introduction: Seed drills are the planters that plant the seeds in rows in close proximity. The sowing rates of seed drills are regulated by fluted roller seed metering mechanism which may have different seed numbers each time in their grooves. Given the nature of these types of seed, it is not possible to completely prevent the change in seed flow rate. In addition, during sowing with seed drills over a field, seedless areas may remain largely due to unavoidable problems, such as a malfunction of the seed metering mechanism, clogging of seed tubes, emptying of the seed hopper, etc. Due to the closed-loop of the sowing process seed drills, seeds can be placed with an undesirable population per unit area. In this regard, the seed drill performance monitoring system by providing online feedback on the operating status of various parts could optimally improve sowing efficiency. Materials and Methods: At first step, to develop a seed drill monitoring system, an infrared seed sensor was designed to be installed in sowing tubes of seed drills. To establish an equation for mass flow rate estimation, the sensor was evaluated by a roller seed metering system and three types of seeds including chickpea, wheat and alfalfa (respectively, representative of large, medium and fine seeds). It was found that a completely acceptable equation can be made between the voltage and the flow rate of each type of seed. Afterwards, designing and constructing a seed drill performance monitoring system based on developed seed flow sensors was considered. In the proposed monitoring system, the seed flow sensors were installed separately in each seed tube, so that the amount of seed flow rate, the presence or absence of seed flow in the graphical interface can be displayed. The forward speed is measured with the Hall sensor and, taking into account the mass flow rate of the seed, the sowing rate is calculated according to the seed mass sown per unit area. During operation, the system registers sowing data with the location information provided by the GPS module. The overall information from the sowing performance is then recorded simultaneously on the embedded memory card and displayed in the graphical interface. In addition to sowing operations, the proposed system continuously indicate the seed and fertilizer levels of the hoppers measured by ultrasonic sensors. Results and Discussion: The developed monitoring system was constructed and installed on a seed drill, equipped with 13 sowing units. With applications of three levels of ground speed and sowing speed during field experiment, the sensing system is assessed under outdoor operating conditions, including planter vibrations, tractor speed variation, and the dust. The field test resulted in a correlation coefficient of 85 percent between the mean of the weighted data obtained from the scale and the mass flow estimates. The outdoor experiments results appeared to be weaker than laboratory evaluation. Regarding the outdoor operating conditions, the obstruction of the optical elements by the dust seems to have the most adverse effect on the performance of the proposed sensing system. In addition, increased forward ground speed and sowing rate resulted a negative impact on the performance of the developed seed flow sensor. So that with increasing speed and mass flow rate, the passing seed flow becomes denser and more seed remains hidden from the measuring elements. In the case of the hopper level control sensor, ultrasonic sensors had proven to be a suitable and inexpensive practical solution for checking the fertilizer and grain level. Conclusion: There are some suggestions for the development of the sowing monitoring system in future research. When designing an optically based seed sensor, optical elements with a smaller propagation angle are preferred. In this case, the error caused by optical overlap would be minimized. The sowing performance monitoring can be triggered as appropriate feedback received from the forward speed sensor. The flow sensor can therefore only be activated when the tractor is moving and exceeds a predefined threshold. In this case, the environmental effects that affect the performance of the seed sensor can be automatically zeroed when the tractor is stopped. Reduce the wiring between system components by establishing wireless communication protocols, CAN, etc., the use of new operating methods for the modification and cleaning of infrared elements against dust, the development of a graphical interface in Android and iOS systems and the use of tablets and mobiles Phones to display sowing information are some of the issues that could be considered in future system updates and developments.

Introduction: Sour cherry fruit (Prunus cerasus) is one of the most desirable fruit by the consumer due to its precocity and great quality. Pesticides are considered a basic ingredient of modern agricultural. Pesticides have been widely applied to protect agricultural products against detrimental pests, to ameliorate their quality, and increase product efficiency. The evaluation of pesticide residues in fruits has become too much required provisions for consumers, producers and authorities for fruit quality control. Nowadays, monitoring programmes for pesticides in food are carried out worldwide to guarantee consumer health, better management of agricultural resources, and to prohibit economic losses Acetamiprid is the most important pesticides of sour cherry. A possible tactic for defining the pesticide residues, sensing the aromatic volatiles released by fruit using e-nose. The e-noses (Electronic nose) is one of the best non-destructive methods which have shown to be well superseded for conventional methods in food odor detection Materials and Methods: For detection the acetamiprid residue in sour cherry, the e-nose machine was designed and fabricated. The e-nose mainly composed of: data acquisition card (USB self-designed), sensor array, three two-way valves normally closed, vacuum pump, air filter (active carbon), GUI (LabVIEW 2014), power supply, laptop and sample chamber. The main stages of electronic nose work consist of three phases: 1-baseline 2-injection of sample odor into the sensor chamber 3-clearing the sensor array. The fractional method was employed in this research for baseline correction. Acetamipridpesticide Sprayed at 1 Liter per 1000 liters of water on cherry trees before pre-bloom in growth stage. This is a critical time for management of pests. Organic and inorganic healthy samples were collected from multiple trees sprayed and non-sprayed cherry trees and divided into four ripeness grades (RG1 = totally ripe, RG2 = close to ripeness, RG3 = intermediate to ripeness and RG4= unripe), according to the criteria used by expert growers (based on physical size and appearance as well as estimated maturity stages) during June2019. One uncontrolled (PCA) and one controlled (LDA) pattern recognition models were used to classify fruit samples. Results and Discussion: organic and inorganic sour cherries have different response patterns. This indicates that their aromatic compounds are different. Generally, in organic sour cherry MQ3sensor and in inorganic sweet cherry TGS2602 sensor had the highest response and role in detecting organic and inorganic sour cherries. PCA analysis described 89% to 96% of the variance in the diagnosis of organic and inorganic sour cherries. The value of variance in the first and second principal components changed from 63% to 91% and 17% to 26%, respectively. Organic and inorganic sour cherry in RG1, RG2, RG3 and RG4 significantly discriminated. To check the association of each sensor in the acetamipride diagnosis, loading plot, were used. In all of RGs TGS2620, TGS2610, MQ9 and TGS2611 have lowest response and sensors MQ3, TGS813, TGS2602 and TGS826 showed the highest contribution in detection acetamipride residue in sour cherry. For detection of ripeness grades of inorganic sour cherry the amount of variance in the first and second principal components was 81% and 10%, respectively. RG1 and RG2 and RG3 and RG4 overlapped. For organic sour cherry PC1 and PC2 described 63% and 26%, respectively, of the variance between samples. RG2 and RG3 overlapped. Also TGS2610, TGS2611 and TGS2620 have lowest response than to other sensors in detection RGs in organic and inorganic sour cherryLDA could specify acetamipride in sour cherry very well. The accuracy of LDA analysis for residual detection of acetamipride at 4 degrees of maturity was 83. 3-100%. LDA could specify RGs of inorganic sour cherry well, but RG2 and RG3 and RG3 and RG4 have little overlap. The accuracy of the analysis was 95. 83%. For organic sour cherry LDA could to distinguish RGs well, but RG3 and RG4 have little overlap. The accuracy of the analysis was 97. 2% Conclusion: Each two methods can be detected acetamipride, but LDA with correct classification percentage83. 3-100%. are the best methods. According to the study, it can be expressed that the e-nose is a suitable instrument for detecting acetamipride residue of sour cherry and can be used with less time and cost to determine the appropriate harvest time.

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 artifi cial intelligence (AI) has drawn more attention rather than mathematical models to fi nd 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.

Introduction: Herbicides are chemicals that are used to inhibit the growth or to eliminate weeds in agricultural fields to increase the yield of crops in crop production. By the end of the 19th century, with the increasing labor supply problems, the need for chemical methods to control weeds became apparent. It was first reported in France in the 1980s, that sulfuric acid was used in the fight against weeds in sugar beet fields. Nowadays, most of the herbicides used are organic herbicides, which share organic carbon in their chemical structure. The use of herbicides since about a century ago has been suggested as an effective way of eliminating crop competitors, though herbicides that remain in the soil for longer periods of time prolong weed control and thus increase weed management efficiency. On the other hand, their increased stability in soil may be of a magnitude that can damage crops in the following crop rotations. Soil properties can have a significant impact on the stability of herbicides in soil. Materials and Methods: For this purpose, soil samples were taken from 0-30 cm depth from field of experiment No. 2 in College of Agriculture Shahid Chamran University of Ahvaz. After sampling and passing through 2 mm sieve, the physical and chemical properties were measured using standard methods. The pot experiment was conducted in a factorial completely randomized design with 32 treatments including soil salinity (at 2. 5 and 6 dS / m), Ultimo herbicide rate (at 4 concentration levels of 0, 25, 50 and 100% Recommended dose) and planting time (60 and 120 days after herbicide application) with 3 replication. Wheat was selected as the experimental crop and variety was "Mehregan" which has been cultivated in most of Khozestan province. Herbicide was applied and soil was rested for 60 and 120 days then wheat was sown. For germination percentage, wheat seeds were sown directly in soil after germination test. After germination the percentages were recorded and kept in an equal number of plants in the pot. It should be noted that in order to eliminate the effect of nutrient deficiency on plant growth at appropriate intervals, nutrient solution was applied and irrigated according to the need of the plant. After 9 weeks (before flowering stage) the plant was harvested and the growth components including root length, root dry weight, shoot length, shoot weight and nutrient concentration including nitrogen (in plant dry matter), phosphorus, potassium, calcium, magnesium, iron, zinc, copper and manganese were measured in the extract obtained from dry digestion of plant tissue (aerial parts of plant). Statistical analysis was performed using SAS software and mean comparisons were performed by Duncan's multiple range test. Charts were drawn using Excel software. Results and Discussion: According to the results, increasing the level of herbicide decreased the growth parameters of the plant, which is intensive under salinity stress. The results showed that considering 60 days sowing after herbicide application, shoot dry weight in 100% RD herbicide application in salinity of 2. 5 dS/m was 1. 6 g which was not showed significant difference with 50% herbicide application under salinity of 6 dS/m. Therefore, in higher salinity levels lower herbicide dose can damage the plant as much as higher levels of herbicides in lower salinity, and lower levels of herbicides in more soil salinity produce more negative effects. By increasing planting time from 60 to 120 days the residual effects of herbicides on soil decreased and plant showed better yield. By increasing salinity level from 2. 5 to 6 dS / m, all growth components of wheat decreased, except for shoot length and shoot dry weight, this significant decrease was not reported for other components. Conclusion: Therefore, it can be concluded that selection of sowing time after herbicide application in crop rotations is very important and by selecting the correct time can greatly reduce the deleterious effects of consuming more herbicides. Planting wheat at 60 days after application of herbicide compared to 120 days after application of herbicide, decreased the growth components of the plant. Therefore, selection of wheat sowing time in crop rotation 60 days after application of herbicide (especially at 100% recommended dose) is not recommended in maize – wheat cropping system. Also considering soil chemical properties such as salinity as an influencing factor on herbicide behavior in soil can be effective in controlling residual effects of herbicides in soil and plant.