نشریه مهندسی بیوسیستم ایران
سال پنجاه و سوم شماره 4 (زمستان 1401)

Microbial contamination of date fruit, as one of the important and strategic agricultural products of Iran, is one of the important causes of waste of this product. Therefore, in this research, the effect of cold plasma as one of the new technologies for maintaining the quality and durability of Mazafati date using a sliding rotary arc discharge device, in two treatments of plasma application time in 5 levels (30, 60, 120, 180, and 240 s), with the aim of achieving the optimal time and different gases at 3 levels (air, argon and oxygen), with the aim of determining the best type of gas, was investigated. Microbial load evaluation by microbial counting method on 0, 3, 6 and 9 days after plasma treatment were performed. Sensory evaluation of date samples was performed after 9 days of plasma treatment by 5-point hedonic method. The results of microbial analysis showed a reduction of 0.48 logarithmic cycles of aerobic mesophilic bacteria in air-plasma samples and also a reduction of 0.35 logarithmic cycles of aerobic mesophilic bacteria in oxygen-plasma samples compared to the control sample. The lowest mean growth of aerobic mesophilic bacteria was observed in the sample with plasma treated time of 240 s. Plasma treatment with arc discharge device significantly reduced the microbial load, so air and oxygen with duration of plasma treatment of 240 s had the highest effect in reducing the total count of aerobic mesophilic bacteria. Also, storage time had a significant effect on microbial load and the total number of aerobic mesophilic bacteria on the last day of storage was significantly (p <0.05) higher than other days. Sensory evaluation also showed a significant difference between the sensory characteristics of plasma-treated samples and the control sample. Based on the scores of the evaluators, plasma-treated samples with air in the characteristics of flavor, texture, color and overall acceptance had 59, 45, 8 and 39% more scores than the control sample, respectively, which indicates the effectiveness of the plasma-treatment method in maintaining the quality of Mazafati dates.

Soil moisture is one of the main factors determining the better growth of plants which are widely well-received today, especially in greenhouses. Measuring the soil moisture and the environmental factors has high costs continuously and annually, in addition to being time-consuming. Therefore, one of the intelligent predictive tools that have a vast position in agricultural science is the neural network tool with the least amount of error. In this study, soil moisture and temperature percentage, light, ambient temperature, and humidity in a greenhouse located in northeastern Khuzestan were Measured and recorded during two seasons of winter and spring to control soil moisture by a moisture prediction map based on an artificial neural network. The results show an accurate forecast of soil moisture map in winter and spring between actual values that were measured and values that were predicted with the lowest standard error (1.12 and 1.71) and the highest coefficient of determination (R2) of 0.94 and 0.78, respectively, and the highest coefficient of determination were 0.87 and 0.93, respectively, by the artificial neural network in the experimental stage for winter and spring. Therefore, the remarkable accuracy in the prediction of soil moisture by this software shows its importance and high reliability in agriculture and greenhouses which makes it easier to control soil moisture and less moisture stress occurs for soil and the plant consequently.

Applying agricultural operations under different mechanization systems has different economic, social, and environmental consequences. Conflicts among these dimensions complicate the selection and allocation of sustainable mechanization systems. In this study, a method was used to allocate optimal patterns of spraying and harvesting systems in the Ramhormoz region to achieve agricultural sustainability. Indices included satisfaction, ease of work, health and safety, employment in the machine sector, labor force, diesel fuel consumption, pesticide consumption, farm load intensity, and operating costs. Three spraying systems namely backpack, tractor, and unmanned aerial vehicle (UAV), and three harvesting systems namely two-stage (harvesting and manual feeding to grain combine), direct harvesting with a grain combine harvester, and harvesting with rice combine harvester were included in the model. Combining AHP and TOPSIS methods, the similarity index for social and environmental dimensions was calculated and this value along with the cost of each system was used as coefficients of objective functions. The multi-objective optimization model to achieve sustainable agricultural mechanization was analyzed using a genetic algorithm. Pareto optimal results showed that in the absence of existing machine constraints, the development of the operational capacity of modern systems like spraying with UAV up to 2000 hectares and direct harvesting with rice harvesters up to 1000 hectares will be optimal scenarios for agricultural sustainability. Using the proposed method, not only can sustainable goals be achieved in identifying the best patterns of mechanization systems, but it is also possible to examine the effect of different scenarios under different constraints.

Potato )Solanum tuberosum (is one of the strategic products which is cultivated in most of the regions in Iran. Peeling is one of the primary processes of this product and plays an important role in other processing steps. In this research, a combination of mechanical and chemical potato peelers was designed and manufactured. The peeler consists of a chasse, a motor, a power transfer system, and a drum equipped with three types of blades. Various experiments were carried out to determine the amount of losses and performance evaluation. The experiment was performed at three levels of blade type (knife, brush and carborundum type), four levels of feed rate (1, 2, 3 and 4 kg) and four levels of concentration of the chemical solution (0, 1, 2 and 3%, w/w). The experiment was factorial based on a completely randomized design with three replications. The results of the experiments showed that the effects of blade type, feed rate, and concentration of the chemical solution on the amount of losses and performance evaluation were significant (p>0.01). Maximum rate of peeling was achieved by applying the brush-type blade with a 1 kg feed rate and the 3% concentration of the chemical solution. The results showed that in all three types of blades and at each feed rate, the percentage of peeling increases with the increase in the concentration of sodium hydroxide chemical solution. Also, it was found that the combined chemical and mechanical peeling increases peeling, reduce the peeling losses, and improves the performance of the device.

Microalgae can produce various products such as biofuel, proteins, and beta-carotene. Knowing the optimal conditions to produce these products is very important. In this research, the effect of environmental parameters on the production rate of each product in Dunaliella salina microalgae was investigated. saltiness (1, 2, and 3 M), pH above and below 7, nitrate concentrations of 0.25 and 0.5 g/L, light intensities of 2500 and 5000 lux, and temperature levels of 25 and 30 °C were studied. According to the results of this study, the highest cell density of algae was at 3 M salinity and pH higher than 7, nitrate concentration equal to 0.25 g/L, light intensity 5000 lux, and temperature 25°C. The same conditions of salinity 3 M, pH of more than 7, nitrate concentration of 0.25 M, light intensity of 5000 lux, and temperature of 25°C prevailed for the maximum production of microalgae biomass. Under these conditions, the highest amount of protein was produced in Dunaliella salina microalgae. But for lipid production, the optimal conditions were at a salinity of 2 M, pH more than 7, nitrate concentration of 0.25 g/L, light intensity of 5000 lux, and temperature of 25°C. Optimum production of betacarotene also occurred at 3 M salinity, pH more than 7, nitrate concentration 0.25 g/L, a light intensity of 5000 lux, and temperature of 30°C. By knowing these conditions, it is possible to achieve the maximum amount of production of products such as biofuel, protein, and betacarotene from Dunaliella salina microalgae.