Objective: Weeds are one of the most important factors in reducing the yield of agricultural products, and their non-targeted control, in addition to increasing production costs, leads to adverse environmental consequences caused by excessive use of herbicides. In the meantime, intelligent methods are used to use machine vision and can provide the basis for the accurate detection of weed elfs and the implementation of their local management. The current research was conducted with the aim of designing and evaluating an intelligent model for detecting weeds in bean fields, based on meta-heuristic training and optimization. Method: The research was a combination of experimental and computational study in terms of practical purpose and implementation method. Research data were collected from bean plants and weeds, including field images and standard data. After inappropriate removal, the data were augmented using augmentation methods and used to train, validate, and test the models used. In the first stage, basic performance models including ResNet-18, ResNet-50 and EfficientNet-B0 were compared. Then, a hybrid model based on convolutional neural network and particle swarm optimization algorithm, called PSO-CNN, was designed and evaluated to improve the adjustment of models. Findings: The results showed that EfficientNet-B0 had the best performance among basic models with an accuracy of 0.86. However, the proposed PSO-CNN model performed better and achieved accuracy of 0.90, positive precision of 0.91, recall of 0.89, F1 score of 0.90, and area under the ROC curve of 0.94. The results of the statistical tests also showed that all the models have significantly higher performance than the chance level. Also, the effect of the network architecture on the best decisions and optimizations using the particle swarm algorithm, compared to the basic model, significantly improves the performance indicators. It showed that the most important analysis of the error model in the detection of broadleaf weeds was due to the texture similarity and overlap of the leaves with the bean plant. Results: Overall, the findings indicate that the integration of meta-heuristic concepts and algorithms is useful for the development of intelligent weed detection systems in bean fields. Despite the limitation of primary data volume, the proposed model can be used to design precision agricultural tools, reduce herbicide consumption, and improve production sustainability in the agricultural sector. Keywords: weeds, beans, social, convolutional neural network, particle swarm algorithm, precision agriculture, machine-to-machine.

   One of the most important topics in mycological sciences is the topic of identifying nonedible mushrooms and identifying them from edible mushrooms. Today, the number of people who get poisoned by consuming nonedible mushrooms is increasing. As a result, the detection and separation of edible mushrooms from nonedible ones is of great importance. Considering the research in this field and the need to perform high-cost and somewhat inaccessible tests such as GC, methods should be sought. It was an alternative to these tests. The method of artificial intelligence and electronic nose is a non-destructive and accessible method with a much lower cost compared to GC. In fact, the purpose of this experiment is to study an olfactory system to distinguish edible mushrooms from poisonous mushrooms based on electronic nose technology and to help maintain health and treat diseases and reduce poisoning caused by poisonous mushrooms. Therefore, in this research, an olfactory system was used to detect and identify edible and nonedible mushrooms. ANN methods, PCA principal component analysis, LDA linear discriminant analysis, QDA 2nd degree linear discriminant analysis and SVM support vector machine were used to analyze the data obtained from the olfactory system. The classification of the data obtained from the signals obtained from the sensor array showed that the LDA, QDA and ANN methods have a very good performance in separating mushrooms based on their edible and nonedible nature and high accuracy in classification. Obtained. The use of QDA method to separate and classify different types of edible and nonedible mushrooms was more effective and accurate than LDA method.

Coccinelidae with the English name, Ladybird, belong to the (Colleoptera: Coccinellidae) Both, adult insects and larvae of Coccinellidae are often predators of important agricultural pests and play an important role in controlling Aphids, Scale insects and even mites. In this research, the faunal survey of Coccinellidae and associated parasitoids in Kermanshah province were carried out during a trip to some parts of the province in 2020 to 2022. More than 4250 samples from different plant ecosystems were collected by multiple methods. Their identification was based on new and valid sources and keys. The species identified in the list below include 28 species and belong to 4 subfamilies: Chilocorinae (5 species: 3 genera: 1 tribe), Coccinelinae (11 species: 7 genera: 3 tribe), Scymninae (9 species: 5 genera : 4 tribe), Sticholotidinae (1 species : 1genera : 1 tribe). Four species of parasitoides related to ladybird has also been identified and presented separately in the list below. These two lists are related to a small number of ladybugs and their parasitoids in Kermanshah province. But surely the fauna of ladybugs and their natural enemies is much richer than this, so it is suggested to complete this study with more facilities in the future. In addition, the identification of two species: Nephus species A and B, which are probably new to the world and 4 parasitoid species is under identification prosses. Those has been sent abroad for identification and molecular work. The list of species is as follows: Key words: Biodiversity, Predator, Biological control, Checklist and Distribution Chilocorus bipustulatus (Linnaeus,1758), Exochomus quadripustulatus Linnaeus, 1758, Exochomus undulatus Weise, 1878, Parexochomus pubescens (Kuster, 1848), Parexochomus nigromaculatus (Goeze, 1777), Coccinella septempunctata Linnaeus,1758, Hippodamia variegata (Goeze, 1777), Oenopia coglobata (Linnaeus, 1758), Oenopia onica (Olivier , 1808), Propylae quatuoredecimpunctata Linnaeus, 1758, Adalia bipunctata linnaeus, 1758, Adalia decimpunctata Linnaeus, 1758, Ceratomegilla undecimnotata D.H. Shneider, 1793, Psyllobora vigintiduopunctata (Linnaeus, 1758), Coccinula elegantula (Weise, 1890), Coccinula redmita (Weise 1885), Stethorus gilvifrons Mulsant, 1850, Scymnus flavicolis Redtenbacher, 1843, Scymnus rubromaculatus Goeze,1777, Scymnus apetzi Mulsant, 1846, Scymnus subvillosus (Goeze, 1777), Scymnus pharaonis Motschulsky, 1851,   Clitostethus arcuatus (Rossi, 1794), Hyperaspis pseudopustulata Mulsant, 1853, Diomus rubidus Motscholsky, 1837, Pharoscymnus pharoides (Marsuel, 1868), Nephus sp A, Nephus sp B. Parasitoids: Encyrtidae: Homalotylus turkmenicus Myratseva, 1981, Ooencyrtus sp. Pteromalidae: Pachyneuron muscarum (Linnaeus, 1758) Aphelinidae: Marietta picta (Andre, 1878)  

   in this study, using three electric nose devices to detect and estimate the amount of oil odor per 500 km of car operation, refractometer to determine the concentration of each sample of oil, calorimeter to measure the amount of paint after every 500 km of operation, engine oil operation We estimated. We used PCA, LDA and neural network to >Based on the scoring and loading diagrams of various data (olfactory data, color data and integrated color, odor and brix data in both standard and normal methods) in the PCA principal component analysis method to detect distance or kilometers traveled. The results showed that the variance of the color data of the first and second major components was 98% and 1%, respectively. The most changes were observed in the color data. All samples of oils were well separated and >According to the neural network perturbation matrix, the 0.7372, R2 = 0.8677 and R2 = 0.6045, respectively, which are the values of R2 for the PLS model. For color change, brix and mileage were higher, and in general the data predicted in the PCR model were closer to the actual data Arshak, K., Moore, E., Lyons, G. M., Harris, J., Clfford, S. 2004. A review of gas sensors employed in electronic nose applications. Sensor Review. 24(2): 181-198.    Adibzadeh, A.; Dizaji, H.Z.; Aghilinategh, N. (2020) "Feasibility of Detecting Sugarcane Varieties by Electronic Nose Technique in Sugarcane Syrup". Iranian Biosystems Engineering journal. 51(1), 1-10. (In Farsi).   Https://doi.org/10.22059/IJBSE.2019.287027.665209.    Scott, S.M., James, D., Zulfiqur, A. 2007. Data analysis for electronic nose systems. Microchimica Acta. 156(3-4):  

   This study was performed to evaluate the relative suitability of susceptible and resistant wild mustard (Sinapis arvensis) biotypes to the herbicide terry benuron methyl (granstar) in Hamil and Markazi counties in Islamabad Gharb city, Kermanshah province during 2017-2017 crop in two greenhouse sections. And the laboratory was run. The results of this experiment showed that the resistance of different masses harvested from 9 altitudes (above sea level) did not show a statistically significant difference despite the increase in resistance at altitudes, and the recorded resistance index between 5.55 to 64 / 6 was obtained. The results of the effect of temperature on germination of susceptible and resistant masses of wild mustard showed that the response of susceptible and resistant masses was different at different temperatures and at 20 and 25 ° C sensitive masses had the highest percentage of germination and vigor. They were resistant to the mass. Germination rate also showed significant differences in different temperature treatments and at (5 and 20) ° C the germination rate of resistant masses was significantly higher than sensitive mass. At 25 ° C, the germination rate of the sensitive mass was significantly higher than that of the resistant mass. The results related to the effect of different osmotic potential also showed that in general wild mustard is sensitive to reducing the osmotic potential of soil and the percentage and speed of germination and vigor of sensitive masses at the osmotic potential of 0.1 and 0.2 MPa were the highest and In the osmotic potential of 1 and 1.2 reached zero. Acidity and resistant and sensitive masses of wild mustard have a significant effect on germination percentage and vigor, as well as different amounts of acidity have a significant effect on wild mustard seeds, so that resistant masses prefer acidic to neutral acidity. The results of dose-response experiment showed that the interaction of mass in the amount of trifenuron methyl on the germination percentage of wild mustard seeds was significant. The highest germination percentage occurred at concentrations of 0.25, 0.5, 1 and 2 in resistant masses, and in susceptible masses in control treatment and concentration of 0.25 and germination in both sensitive and resistant masses at concentrations of 8, 16 And 32 went to zero. Given the relative suitability of wild mustard sensitive stands in terms of germination, it seems that if no special management operations are carried out to reduce the germination of these stands, in the future the population of these stands will expand and in this case, the possibility of There are increasing problems by these masses. eywords: Stolactate synthase, Resistance, Relative suitability, Germination

  تجزيه گرمايي زيست توده در محيطبدون اكسيژن يا با اكسيژن اندك را گرماكافت مي‌نامند كه محصول اين فرآيند دي اكسيدكربن، گازهاي سوختي، بخار قيري و جزء جامدي به نام بيوچار است. فرآيند گرماكافتراهي براي تبديل زيست توده به مواد با ارزشتر نظير بيوچار است. بيوچار ماده ايجامد و داراي محتواي كربن بالاست كه رايج ترين مورد استفاده آن در كشاورزي بهعنوان اصلاح كننده خاك است. محققان در سال هاي گذشته تاثير استفاده از بيوچار برخصوصيات فيزيكي وشيميايي خاك را مورد مطالعه قرار داده اند و مشخص شده است كهافزودن بيوچار به خاك كيفيت خاك را بهبود مي‌بخشد. خصوصيات فيزيكي و شيميايي بيوچارتحت تأثير عوامل مختلفي از جمله نوع مواد اوليه، شرايط واحد گرماكافت، سرعت گرمادهي،مدت زمان گرماكافت وللفعوامل متعدد ديگري قرار مي‌گيرد . دامنه گسترده فرآيندگرماكافت منجر به توليد بيوچارهايي   كه ازنظر خواص شيميايي و فيزيكي مختلفي نظير تركيب عنصري و خاكستر، وزن مخصوص، تخلخل،توزيع اندازه منافذ، سطح ويژه، pH، جذب و دفع آب و يون ها و بسياري خواص ديگر متفاوت هستند مي‌شود. هدفاز اين مطالعه، بررسي اثر تغيير دبي هوا و دماي محفظه در گرماكافت اكسايشي بسترثابت بر روي عملكرد بيوچار، محتواي خاكستر، وزن مخصوص و pH بود. بدين منظور يك دستگاه توليد بيوچار اكسايشي بسترثابت با قابليت تغيير در دماي محفظه و دبي هواي خروجي طراحي و ساخته شد.آزمايش‌هادر چهار دبي هواي 20، 25، 30 و 35 ليتر در دقيقه ونيز چهار دماي 350، 400، 450 و500 درجه سانتي‌گراد براي كاه و كلش گندم انجام شد. نتايج نشان داد كه افزايش دبيهواي   خروجي   از محفظه و افزايش دماي محفظه، سبب افزايش ميزانخاكستر وpH شد. درحالي كه تغيير اين پارامترها سبب كاهش وزن مخصوص ظاهري و عملكرد   بيوچارتوليدي شدند.     

  One of the traditional methods of distinguishing a healthy egg from an unhealthy one is by shaking the egg and recognizing its sound. But the detection of egg defects in the traditional way by humans is not accurate. It is essential to use more accurate methods in diagnosing egg health and to use smart tools to reduce the time and increase the efficiency of separating rotten eggs from healthy eggs. The aim of this study was to identify the defects of healthy and unhealthy eggs (including rotten, hatched) using sound waves using fuzzy neural inference system (ANFIS). In this study, the acoustic response of 90 eggs, including healthy, rotten, and hatched eggs, was examined by shaking at 200 and 400 beats per minute. After collecting the audio signals, the time domain analysis of the signals was performed through descriptive statistics and then the frequency domain analysis. After calculating the statistical characteristics and determining the best characteristics, the data were used for classification with ANFIS. Based on the results of the accuracy of the Enfis model in the two reciprocating velocities of the egg-shaking mechanism, it was 0.99 in mode 1 and 0.93 in mode 2, respectively. The results showed that the adaptive-neural-fuzzy inference system can be used well and with high accuracy in detecting rotten and hatched eggs from healthy eggs.

   Integrated Pest Management (IPM) strategies is dependent on continuous monitoring of the pest population, this is not only time-consuming, but also highly dependent on human judgment and costly.On the other hand, traditional methods for identifying insects are time consuming and costly. Due to the expansion of the industry and its rapid growth, human beings have always sought to accelerate their work with greater accuracy. The use of artificial intelligence techniques instead of manual and human decision-making, in addition to increasing productivity, also has a high degree of accuracy. Pistachio is a commercial product, and many manufacturers of this product, are damaged by the insects each year. A group of pistachio's pests mainly feed on pistachio, which Idiocerus stali Fieber, 1868 (Hemiptera: Cicadellidae) is very important for this group. In this research, I. stali was selected as target insect for identification using image processing algorithm. Sticky yellow cards were used for collecting samples. To prepare the image processing algorithm, the color and shape characteristics of the objects were used.A total of 357 color properties and 20 shape's features for identification of I. stali were extracted by image processing algorithm. Color properties were divided into two categories of mean and standard deviation and characteristics related to vegetation indices. The mean and standard deviations are the average of the first, the second and the third component, the mean of the components of the first, second and third, the standard deviation of the first, the second   and the third component of 17 Color spaces such as RGB, HSV, YIQ, YCbCr, CMY, HSI, Improved YCbCr, L*a*b*, JPEG-YCbCr, YDbDr, Y  r, YUV, HSL, XYZ, Luv, LCH and CAT02 LMS. The characteristics of the vegetation indices are including the first component of the normalized RGB, the second component of the normalized RGB, the third component of the normalized RGB, the gray channel, the Excess Green, the Excess Red, Color index for vegetation cover extraction, the difference between the excess green and excess red parameters, Normalized Difference Index, Green Minus Blue Indicator, Red-Blue Contrast, Excess Red Index, Excess Green Indicator, and Excess Blue Indicator. shape's Characteristics used are also including length, width, area, perimeter, logarithm of length to width ratio, Ratio of the object's perimeter to the object surrounded by the rectangle's perimeter, width to length ratio, Area to length ratio, eccentricity, Orientation, Convex Area, Filled Area, Equivalent Diameter, Euler Number, Solidity, Extent, Elongation, Compression, Aspect Ratio and length to perimeter ratio. Artificial Neural Network hybrid method - Particle Swarm Optimization Algorithm (ANN-PSO) was used to select the effective features. The selected effective characteristicscolor space, normalized difference index for LCH color space, gray channel for color space YCbCr, second component index minus third component for color space YCbCr, area and mean of the first, The second and third components of color space Luv. The detection rate of the designed image processing algorithm is 99.72%. Artificial neural networks of multilayer perceptron have the ability to classify insects into two classes of I. stali and Anthaxia Sp. Eschscholtz, 1829 with a precision of 99.59 percent. The results showed the feasibility of the new method for identifying the pest insects without destroying them in the farm and in natural light conditions and in the shortest time and with very high accuracy.

   Abstract   Iran is one of the most susceptible regions of citrus cultivation, which had many advances in recent decades in the order cultivation and production of citrus, so that cultivated area and its amount annual production, of our country determined agreement among the top 10 countries in the world. in this research, after photography from Tangerines (ripe, half-ripe and unripe) using camera canon Pc1339-12.1 mega pixels and preprocessing operations, segmentation and >   Keywords: Citrus, >

  AbstractSignificant increases in the using of fossil fuels in internal combustion engines and the negative effects of emissions from burning these fuels into the environment have led to an increase in interest in alternative energy sources.On the other hand, the addition of MTBE to base gasoline, despite the increase in octane number, is destructive of the environment effects and malignant diseases, and has been eliminated from gasoline in developed countries for many years, but is still in use in our country. This research aims to reduce emissions in the gasoline engine and examines the amount of adsorbent of an alternative fuel, in the form of a mixture of 1-propanol and base gasoline in the gasoline pride engine. This engine has a Siemens fuel system. 1- Propanol was added to the base gasoline in various volumetric percentages (0, 5, 10, 15 and 20), and the rate of emission at 3 speeds of 1000, 2000 and 3000 rpm was measured by the MOTORSCAN emission Test device.This study was a factorial experiment in a completely randomized design with 6 treatments (normal gasoline for control treatment) and 3 replications.data analysis was performed using SAS statistical softwareandChecked out the effect of fuel type treatments and engine speed on exhaust emissions. Comparison of means with Duncans multiple range Test and error assumption 0.05 were used. The results showed that all modes, including fuel type treatments and different engine speeds, had a significant effect on the amount of emissions,Mutual effects of fuel type and engine speed were significant in both CO2 and UHC.The addition of 1- propanol in fuel in up to 20% reduced emissions of CO and UHC, but NOX changes were irregular and generally increased compared to conventional gasoline. The increase in engine speed led to a decrease in CO and UHC and increased NOX and CO2.Key words: internal combustion engine, MTBE, base gasoline, 1- propanol, exhaust emissions.

  Abstract    Today, the   urvey and analysis of uncertainties in any program is considered necessary,So that without considering and analyzing these uncertainties The occurrence of unpleasant situations that their events challenge the programs goals, Not waiting. These studies are conducted within the framework of risk management .In principle, the Monte Carlo simulation method is used as a tool for analyzing and integrating the various combinations of uncertainties. Uncertainty is integral component of hydrological and hydraulic models. Proper assessment of uncertainty in hydrological models may help to avoid high risk decisions, high cost of product-life cycle, over design structures. The aim of this study was to quantify the uncertainty of flood zoning maps for the reach of Seymwreh River. In this area due to heavy rains, floods cause a lot damage. First, the HEC-RAS flood zoning was calibrated based on F factor. Subsequently, using artificial data generation, 30 artificial data series were generated for the period of returns of 2, 5, 10, 25, 50 and 100 years. In Finally, the probability boundaries of the flood plain of the river were determined at the probability level of 90% and 10%, using the probability curve. The results showed that the increase in uncertainty bandwidth will increase as the maximum uncertainty bandwidth of 450 hectares is related to the flood with a return period of 100 years performed according to Monte Carlo simulation method with random sampling of the parameters space (floodplain and channel roughness coefficients). The model was run 500 times, and the results were evaluated comparing with observed area based on the F factor. Response surface curves obtained from sampling Monte Carlo showed that the highest performance of F when the coefficient of roughness for the channel and floodplain is 0/046 and 0/058. Then using the cumulative distribution function of flood zones, an uncertainty was reached at the upper and lower limits.      Keywords: Uncertainty,   Probability flood plain, Mont Carlo, Seymareh river

  Pinus trees die-back is one of the most serious problems that presently affects the Kermanshah pinus trees. To study the cause of die-back pinus trees, infected branches were collected during growing seasons, transferred to the laboratory and processed within 24 hours. Small pieces, approximately 4 mm in size, of discolored tissues were surface disinfected by immersing in 1.5 % solution on NaOCl for 30 sec, rinsed in sterile distilled water and plated on potato dextrose agar (PDA) and malt extract agar (MEA) amended with chloramphenicole (25 ?g/ml). Two hundred eighteen fungal isolates were recovered from pinus trees showing dieback symptoms. The most common fungi isolated from most diseased pinus were include Microsphaeropsi  olivacea (16 isolates), Microsphaeropsi  rotea (eight isolates) and Microdiplodia sp. (seven isolates). Numerous isolates of Penicillium, Aspergillus,.Alternaria, Rhizopus Cladosporium sphaerospermum and Trichoderma were always associated with diseased pinus in Kermanshah province. The fungi were inoculated to detached stem of pinus tree. Pathogenicity of all isolates of M.  olivacea, M.  rotea and Microdiplodia  sp. were confirmed on pinus trees by artificial inoculation on detached stems in the laboratory. Disease symptoms on the detached stems in the laboratory appeared as canker. To confirm of   morphological identification, genomic DNA was extracted and a nuclear rDNA region, containing the internal transcribed spacers 1, 2 and 5.8S gene of rDNA (ITS) were amplified and PCR products were sequenced. Amplicon was purified, sequenced and submitted to the GenBank. The resulting sequence was submitted to a BLAST search to find most similar sequences in GenBank. The search results showed highest similarity of Iranian isolates to other isolates from GenBank.. Voucher specimens deposited in fungal collection of the Department of Plant Protection, Razi University, Kermanshah, Iran

  AbstractGalium aparine is a problematic weed, which has become increasingly difficult to control with herbicides in Iran. The aim of this study was to screen selected putative-resistant populations of G. aparine for resistance to auxinic herbicides 2,4-D+MCPA and ALS–inhibiting herbicides sulfosulfuron, tribenuron-methyl, mesosulfuron-methyl+iodosulfuron-methyl-sodium. Populations of G. aparine were collected from different wheat fields in the west of Kermanshah, where herbicide-use pattern is typical for Iran. Herbicide resistance to premixed herbicide 2,4-D+MCPA was confirmed in several populations. More populations of G. aparine showed cross-resistance to ALS-inhibiting herbicides examined in this research. Some populations were found to have developed multiple resistant to both auxinic and ALS herbicides. Generally, the level of resistance to ALS-inhibitor herbicides was higher than that of auxin analog herbicides.   

     The reservoirs are operated and designed in several aims such as, water demand supply, hydropower energy generation and flood control reduction. Optimization of reservoir operation is one of the most important issues in water basin. In this study, optimization of reservoir operation to obtain the maximum agriculture water supply and hydropower energy production are considered. As the rainfall is the stochastic phenomenon and has the positive effect on reservoir inflow, hydropower optimization is applied by using chance constrained linear programming (CCLP). Maroon Reservoir is located on east western of Khozestan province and the aims of this reservoir are agriculture water supply of Jayazan, Behbahan,Shadegan and Khalafabad, flood controlling and hydropower generation about 150 Megawatt(MW). Monthly inflow of 52 years (1953-2004) is used to modeling of experimental Weibul probability distribution for each month. Annual energy production of Maroon reservoir is obtained by Lingo 16.0 software for a known agriculture water supply (P) and installed capacity 150 MW. Then, the best probability distribution of reservoir inflow is obtained by Easyfit software 6.0 for each month. The results showed that the maximum P are calculated 86% and 88% and also the annual hydropower energy are 114.86 and 111.55 Giga watt-hour(GWh) using experimental Weibul probability distribution and the best probability distribution of reservoir inflow, respectively. Thus, obtaining the best Inflow probability distribution does have insignificant effect on optimization results since in this study it’s less than 2% on agriculture water supply and hydropower generation. So, considering only the experimental Weibul probability distribution of reservoir inflow is sufficient in CCLP approach.