دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 10 2 2025 06 22 Separating Between Potatoes and Clods Using the Coefficient of Restitution جداسازی سیب زمینی از کلوخ با استفاده از ضریب برجهندگی 1 14 19491 10.22034/jam.2025.64717.1306 FA شمس اله عبداله پور گروه مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه تبریز - تبریز - ایران اصغر محمودی گروه مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه تبریز - تبریز - ایران Journal Article 2024 12 02 Potato is one of the most valuable foods, after wheat, rice and corn, it is the fourth major crop in the world. Potato harvesting is one of the expensive and sensitive steps. In Iran, due to the lack of harvesting machines suitable for the fields of the country (the presence of stones and lumps and lack of humidity control, shortage and high wages of workers) the area under potato cultivation is small. Separating stones and lumps from potatoes and damaging the tubers are two basic and unavoidable problems of most potato harvesting machines. More separation of stones and lumps often leads to more mechanical damage to potato tubers, and less separation increases labor costs. It is serious that in most of the harvesting machines, several workers are placed next to the potato carrying belt and separate the stone from the potato to minimize the damage. The purpose of this study is to investigate the separation of potato from the potato by using the difference in the coefficient of restitution, in potato and clods. In this research, the effect of different factors on the accuracy of separation of potato tubers from clods was investigated. Introduction More than 370 million tons of potatoes are produced in the world every year and it is considered the fourth strategic product of the world after wheat, rice, and corn. Potato is one of the most valuable foods, after wheat, rice, and corn, it is the fourth major crop in the world. In addition to food consumption, it is used to prepare more than 50 kinds of products, including starch, flour, bread, glue, alcohol, cosmetics, canned food, chips, glucose, etc. are used. Potato harvesting is one of the most expensive and sensitive steps. In Iran, the area under potato cultivation is small due to the lack of harvesting machines suitable for the country's fields (the presence of stones and lumps lack of humidity control, shortage, and high wages of workers). Separating stones and lumps from potatoes and damaging the tubers are two basic and unavoidable problems of most potato harvesting machines. More separation of stones and lumps often leads to more mechanical damage to potato tubers, and less separation increases labor costs. It is serious that in most of the harvesting machines, several workers are placed next to the potato carrying belt and separate the stone from the potato to minimize the damage. The purpose of this study is to investigate the separation of potato from the potato by using the difference in the coefficient of restitution, in potato and clods. Materials and Methods In this research, a digester with a diameter of 60 cm was used and the volume of the digester was 0.4 cubic meters. The standard volume of the maximum substrate that can be loaded is 0.325 cubic meters. Digester stirring is done by a mechanical stirrer connected to an electric motor in the central part of the top of the cap. The whole digester has a capacity of three layers of materials, each layer has its own sensors. Acidity and temperature sensors collect the relevant variable status and store and transfer it to virtual memory through the electronic control system. The anaerobic digester system in the bioenergy and recycling laboratory unit has been repaired, and a sample of cow manure was prepared from the animal husbandry unit around Tabriz and transferred to the laboratory as a substrate for conducting research. The experiment was done in three repetitions, and in each repetition, 150 kg of fresh animal waste was poured into the digester tank with 150 liters of water. Then, to add methanogenic microorganisms to the substrate, 10% of the total weight of the tank (substrate), i.e. 30 kg of animal rumen, was prepared and added. Each repetition of this process continued for 30 days, and the temperature inside the tank was kept at the same temperature as the outside environment (30 degrees Celsius) in the first repetition, and at 35 degrees Celsius in the second and third repetitions. Mixing was done automatically for 5 minutes only in the second and third repetitions and every 6 hours, and the mixing speed was set to 100 and 150 rpm, respectively. After the system started working, gas was discharged twice a day (every 12 hours) according to the production rate and pressure. The total amount of methane produced until that day was measured on the meter and the percentage of methane gas produced daily was measured by the methanometer. Also, in this research, using computational fluid dynamics (CFD), the prediction of the kinetic process of biogas production from animal waste and the provision of the appropriate stirring cycle during the anaerobic digestion process was investigated. In the initial stages of the work, data related to an anaerobic digester with an agitator, and mixing speeds of 0, 100, and 150 rpm were recorded for one month, and the measured characteristics were converted into the inputs of the ADM1 model. Then, the initial values that were reported during the start-up stage of the digester were estimated. Results and Discussion The results of investigating the effects of the factors show that the effect of all 5 investigated factors on the separation accuracy is significant at the five percent level. Also, the investigation of the mutual effects of the factors shows that the investigated factors have a 5-way interaction effect on the separation accuracy of the system, in this case, there is no need to investigate the double, triple, and quadruple interaction effects to analyze the results. The best separation percentage was obtained at a rotational speed of 36 rpm and a distance of 34 cm from the cylinder axis to the separator and the steel cylinder. Maintaining high-efficiency values requires a coordinated increase in the linear speed of the conveyor belt with the rotational speed of the cylinder. Conclusion For better separation, the horizontal distance between the axis of the conveyor belt and the rotating cylinder should be adjusted, because it changes the angle of impact of the material on the cylinder, so it is useful in the amount of effective separation. This distance is determined experimentally. By comparing the average results, it can be seen that the rotational speed of the cylinder alone does not have a specific effect on the separation rate, and the tests conducted at all three levels of the desired rotational speed have led to optimal results. In other words, to achieve the desired result for separation, any rotational speed value can be used in combination with suitable values for other factors. It seems that the type of cylinder does not have much effect on the separation accuracy of the system, because the change of the kind of cylinder changes the amount of the coefficient of separation, but what is important for the separation index is the difference of the coefficient of separation of potatoes and lumps. Also, the amount of mechanical damage to the glands is very important. Its goal is to obtain the highest percentage of separation with the least amount of mechanical damage. The results show that the best separation accuracy is achieved with a steel cylinder, but when materials made of rubber and unbreakable plastic were used as treatment, it was observed that there is no significant difference in the results obtained, only the advantage that these materials have over steel is that they reduce the amount of possible mechanical damage. The horizontal distance of the axis of the conveyor belt from the axis of the cylinder is one of the factors that affect the separation accuracy at the level of 1% and 5%. The comparison of the average values of separation percentage shows that this factor has a significant effect on the separation percentage. Changing the horizontal distance of the cylinder axis from the axis of the conveyor belt causes a change in the material collision angle. Increasing the linear speed of the conveyor belt causes the materials to be thrown to a greater distance, and if the rotational speed of the cylinder and the distance of the cylinder from the conveyor belt are adjusted in appropriate values, the distance of the cylinder from the divider will be the determining factor in the efficiency. عملیات برداشت سیب‌زمینی یکی از مراحل پرهزینه و حساس می‌باشد. در ایران به دلیل نبود ماشین برداشت متناسب با مزارع) وجود سنگ و کلوخ و عدم کنترل رطوبت، کمبود و دستمزد بالای کارگر (سطح زیر کشت سیب‌زمینی کم است. جدا کردن سنگ و کلوخ از سیب‌زمینی و آسیب رسیدن به غده‌های سیب‌زمینی، دو مشکل اساسی اکثر ماشین‌های برداشت سیب‌زمینی است. جداسازی همه سنگ و کلوخ‌ها منجر به صدمات مکانیکی بیش‌تر به غده‌های سیب‌زمینی می‌شود و جداسازی کمتر هزینه های کارگری را افزایش می‌دهد. این مشکل به اندازه‌ای جدی است که در اکثر ماشین‌های برداشت، چندین کارگر در کنار نقاله حامل سیب‌زمینی قرار گرفته و اقدام به جداسازی سنگ و کلوخ از سیب‌زمینی می‌نمایند تا صدمات به حداقل برسد. هدف از این مطالعه، تحقیق در مورد جداسازی سیب‌زمینی از کلوخ با استفاده از تفاوت در خاصیت ضریب برجهندگی(Coefficient Of Restitution ) است. بدین منظور از مکانیزمی متشکل از یک استوانه دوار که در مقابل تسمه‌نقاله‌ی حامل مخلوط سیب‌زمینی و کلوخ دوران می‌نماید استفاده شد. مخلوط سیب‌زمینی و کلوخ پس از عبور از روی تسمه نقاله با سطح استوانه برخورد کرده و در اثر اختلاف ضریب برجهندگی، غده‌های سیب‌زمینی و کلوخ‌ها به فواصل متفاوتی پرتاب می‌شوند. عواملی که بر میزان ضریب برجهندگی مؤثرند، عبارتند از: سرعت دورانی استوانه دوار، سرعت خطی تسمه‌نقاله، جنس روکش استوانه دوار و فاصله پرتاب غده‌ها. در این مطالعه تاثیر هر یک از این عوامل بر ضریب برجهندگی و درصد جدایش غده‌ها بررسی شد. تیمارهای مورد مطالعه سرعت خطی تسمه‌نقاله در سه سطح 3/0 ،45/0 و 6/0 متربرثانیه سرعت دورانی استوانه در سه سطح 36،58و 75 دور در دقیقه (جنس روکش استوانه در سه سطح (استوانه فولادی، لاستیکی و پلاستیک نشکن) فاصله افقی محور تسمه‌نقاله و استوانه در سه سطح 6، 9 و 13 سانتی‌متر و فاصله افقی محور استوانه از جداکننده در سه سطح 28، 34 و 40 سانتی‌متر مورد آزمون قرار گرفتند. آزمایش‌ها در قالب طرح کاملا تصادفی با سه تکرار انجام شد.

https://jam.tabrizu.ac.ir/article_19491_e774bf9092c8f146be07ee301d726ee0.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 10 2 2025 06 22 Drying Kinetics of Apple Slices in a Water-Based Solar Dryer Equipped with a Parabolic Trough Collector سینتیک خشک شدن برگه های سیب در یک خشک کن خورشیدی برپایه آب مجهز به سیستم متمرکز کننده سهموی خطی 15 25 19597 10.22034/jam.2025.64427.1304 FA میلاد تیموری عمران گروه مهندسی بیوسیستم، دانشگاه محقق اردبیلی عزت اله عسکری اصلی ارده گروه مهندسی بیوسیستم، دانشگاه محقق اردبیلی علی متولی گروه مهندسی بیوسیستم، دانشگاه علوم کشاورزی و منابع طبیعی ساری ابراهیم تقی نژاد گروه مهندسی بیوسیستم، دانشکده کشاورزی دانشگاه تربیت مدرس Journal Article 2024 11 16 Introduction Today, a high percentage of industrial dryers are convection dryers. Currently, most of the energy used for drying comes from fossil fuels, which have irreparable environmental damage. Solar energy is a clean and renewable energy used for the drying process since ancient times. Over time, various solar dryers were produced and investigated in research. Researches show that most of these dryers are convective and work based on the hot air-drying method. The refractance window dryers are a new generation of dryers with high-quality dried products. Like other dryers, the drying process in these dryers is energy-intensive. This research has tried to provide the energy needed to heat water in an RW dryer system by receiving solar energy through a PTC collector. An attempt has been made to study the drying kinetics of apple slices inside a combined solar dryer. The combined dryer included a refractance window dryer and PTC collector. Materials and Methods In this research, an attempt has been made to study the drying kinetics of apple slices inside the combined dryer included a refractance window dryer and a PTC collector. The drying experiments were divided into three categories. The first group of tests was related to the common drying method (RW), which was performed at three temperature levels (65, 75, and 85°C). To conduct tests in the first category, only city electricity was used to heat water. The second group of experiments was conducted as combined drying by solar energy (PRW). In this method, three temperature levels (65, 75, and 85°C) were used. Fick's second law was used to measure the effective diffusion coefficient. Mathematical models were used to describe the drying curves of the thin layer to investigate the drying kinetics. Experimental data obtained from the drying of apple slices were described using Logarithmic, Henderson and Pabis, Newton, Modified Page, and Midili models. Results and Discussion The drying time was 140-320 min in the first method, 160-260 min in the second method, and 240 min in the third method. In the first method, the results showed by increasing the temperature from 65 to 85°C, the drying time decreased by 56.25% (from 320 to 140 minutes) and by 38.46% (from 260 minutes to 160 minutes) in the second method. The fitting of the used models with the real data showed that all the used models had good accuracy in determining the process of exiting moisture from the apple slices. Among the investigated models, the Midili model was more accurate than other models due to having higher R2 and lower SSE and RMSE. The lowest and highest effective moisture diffusivity coefficients obtained in the experiments conducted by RW-65 and RW-85 methods as 8.93×10-13 and 4.82×10-12, respectively. Also, the effective moisture diffusion coefficient in the solar method was obtained as 1.74×10-12 in SRW. Conclusion In fitting the data with the Midili model, the mean values of R2, SSE, and RMSE were obtained as 0.963, 0.031, and 0.062, respectively. After the Midili model, modified Page, Henderson, logarithmic and Newton models were more accurate in describing the exit of moisture from the product, respectively. Also, the results showed with increasing the water temperature in the system, the effective moisture diffusion coefficient increases. The presented combined solar dryer has a good ability to dry the apple slices and reduced energy consumption compared to non-renewable resources. Acknowledgment This study has been conducted as an interior research project of the University of Mohaghegh Ardabili. خشک‌کن های رفرکتنس ویندو نسل جدیدی از خشک کن ها هستند که کیفیت محصول خشک شده در آنها بالا است. فرایند خشک کردن در این خشک کن ها همانند خشک‌کن های دیگر فرایندی انرژی بر محسوب میشود. در این تحقیق سعی شده تا سینتیک خشک شدن سیب در داخل یک خشک کن خورشیدی ترکیبی مورد مطالعه قرار گیرد. خشک کن ترکیبی شامل یک خشک کن رفرکتنس ویندو و یک کلکتور خورشیدی از نوع سهموی خطی بود. آزمایش ها در سه روش مرسوم (RW)، ترکیبی با انرژی خورشیدی (PRW) و تماما انرژی خورشیدی (SRW) انجام شد. در دو روش اول آزمایشات در سه سطح از دما (65، 75 و 85°C) انجام شد در حالیکه در روش سوم دمای خشک کردن مستقیما به دمای کلکتور خورشیدی بستگی داشت. زمان خشک کردن در روش اول 140-320 min ، در روش دوم 160-260، و در روش سوم 240 دقیقه بود. در میان مدل های بررسی شده مدل میدلی به واسطه داشتن R2 بالاتر و SSE و RMSE پایینتر دقت بیشتری نسبت به دیگر مدل ها داشت. کمترین و بیشترین ضریب نفوذ موثر رطوبت به دست آمده در آزمایشات انجام شده به ترتیب در روش های RW-65 و RW-85 به مقدار 8.93×10 -13 و 4.82×10 -12 بدست آمد. همچنین مقدار ضریب نفوذ موثر رطوبت در روش خورشیدی SRW به میزان 1.74×10 -12 بدست آمد.

https://jam.tabrizu.ac.ir/article_19597_88695014c1766e0927f611ffa071e949.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 10 2 2025 06 22 Economic evaluation of tillage systems on wheat grain yield under different water stress levels ارزیابی اقتصادی اثر سامانه‌های خاک‌ورزی بر عملکرد دانه گندم تحت سطوح مختلف تنش رطوبتی 27 35 19732 10.22034/jam.2025.66206.1321 FA شکوفه ساریخانی خرمی بخش تحقیقات علوم زراعی و باغی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی، شیراز، ابراهیم زارع 2- بخش تحقیقاتی اقتصادی، اجتماعی و ترویجی کشاورزی، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی، شیراز، ایران. سید عبدالرضا کاظمینی گروه تولید و ژنتیک گیاهی، دانشکده کشاورزی دانشگاه شیراز، شیراز، ایران. Journal Article 2025 03 03 Introduction Conservation agriculture has positive effects such as reduced production costs, reduced water and input consumption, and positive environmental impacts, but its acceptance by farmers depends on their belief in the positive effects of this innovation and its alignment with their needs. This belief will be reinforced by increasing the economic efficiency of this system on farms. Benefits and costs of the adoption of conservation practices have been evaluated by a number of researchers. Materials and Methods The field experiment was designed as split plot arrangement in randomized complete block design with three replications with wheat- soybean rotation and conducted during 2017-2020 growing seasons at Zarghan research station of Fars province, Iran. Experimental treatments included; three tillage practices (conventional tillage (CT), reduced tillage (RT), and no-tillage (NT)), which were assigned to main plots and water stress levels included irrigation cutting at pollination, milky and seed dough developmental stages along with full irrigation in subplots. Profitability indexes (Income, Gross margin, Benefit-cost ratio, Sale return percent), and technique for order preference by similarity to ideal solution (TOPSIS) methods were used to select the best treatment. The difference between treatments income and cost compared to control treatment has been calculated and compared by profitability indexes. The differences in the treatment's benefits are due to the different wheat grain yields. In TOPSIS method, characteristics including water cost, weed control cost, production cost, grain yield and gross margin were considered to prioritize treatment(s), according to researchers’ views. Selected criteria were ranked based on mean of scores that allocated by researchers. The first step was decision matrix in which the rows and columns describe criteria and alternatives. Frequently, criteria were not the same in importance for decision makers. Therefore, weighted criteria should be considered by Analytic Hierarchy Process (AHP) method. Decision matrix contained a combination of data in various scales. Therefore, the normalized decision matrix by transformed various attributes dimensions to non-dimensional attributes. The second step was to multiply each column of the matrix by corresponding criterion's weight. The third and fourth steps were to identify ideal and negative ideal solutions and to calculate separation measures for each from them. The last step was to calculate relative closeness to the ideal solution and alternatives were appropriately ranked. Results and Discussion Results of profitability indexes showed that conventional tillage system was the first preference of treatments followed by reduced, and no-tillage systems, respectively. Non-shared production costs under no-tillage system were higher than reduced (12.5 %) and conventional (10. 2 %) tillage systems, because of increasing irrigation, weed control, and fertilizer costs. In the conventional and reduced tillage systems, full irrigation treatment had the highest gross margin that followed by cutting irrigation at seed dough developmental stage. Also, cutting irrigation at seed dough developmental stage under no tillage system had high gross marine by 113000 Rials per hectare. The pair wise comparison indicated that the effective criteria to prioritize the best treatments were gross margin (0.250). Based on relative closeness to the ideal criteria, the first and second preference of treatments were irrigation cutting at seed dough developmental stage (0.84) and full irrigation (0.80) under CT, respectively, which followed by irrigation cutting at seed dough developmental stage (0.78) and full irrigation under RT (0.75) systems during three years. The highest wheat grain yield was obtained in full irrigation under CT and RT systems which were not significantly different from cutting irrigation at seed dough developmental stage under conventional, reduced, and no-tillage systems. The irrigation can be stopped at the end of grain filling without significant reduction in wheat grain yield under water limitation. The highest gross margin was achieved in conventional tillage system; therefore, this system had a priority for farmers. The majority of farmers not pay the actual irrigation water cost. If the water price is included in economic evaluation, the production costs will be reduced under conservation tillage comparison to conventional tillage systems. Application of TOPSIS method for decision making in agricultural experiments provides accurate and reasonable decisions. Conclusion Profitability indexes and multi-criteria-decision making methods were used for ranking treatments according to difference between treatments income and cost, the relative closeness to ideal criteria and maximum distance from negative ideal criteria. Results showed prioritizing the best treatments (water stress under tillage systems) were cutting irrigation, at seed dough developmental stage under CT and RT systems. Therefore, under water limited condition, stopping irrigation at the end of grain filling dose not significantly reduce wheat grain yield. Acknowledgment The authors would like to express their deep gratitude to Fars Agricultural and Natural Resources Research and Education Center and Department of Plant Production and Genetics, School of Agriculture, Shiraz University for all support including ﬁnance, academics, and facilities for this research work. We are also very grateful to Dr. Hormoz Asadi, who collaborated and shared ideas to preparing the article. خاکورزی حفاظتی با حفظ منابع آب و خاک دارای اهمیت است. اما پذیرش این سامانهها توسط کشاورزان منوط به افزایش بازده اقتصادی آنها است. بر این اساس، این پروژه با هدف ارزیابی اثر اقتصادی سامانههای مختلف خاکورزی حفاظتی و مرسوم تحت سطوح مختلف تنش رطوبتی در قالب طرح بلوکهای کامل تصادفی و به صورت کرتهای خرد شده در سه تکراردر سالهای زراعی 199-1396 در ایستگاه تحقیقات کشاورزی زرقان فارس اجرا شد. در این پژوهش، فاکتور اصلی شامل خاکورزی مرسوم، کمخاکورزی و بیخاکورزی و فاکتور فرعی شامل تنش رطوبتی بهصورت قطع آبیاری در مراحل گرده افشانی، شیریشدن و خمیریشدن دانه گندم و آبیاری کامل بهعنوان شاهد بود. جهت انتخاب بهترین تیمار از نظر فنی و اقتصادی از شاخصهای سودآوری (درآمد ناخالص، بازده برنامهای، نسبت فایده به هزینه و درصد بازده فروش محصول) و روش تاپسیس استفاده شد. نتایج ارزیابی اقتصادی نشان داد که سامانه خاک‌ورزی مرسوم بهدلیل عملکرد دانه گندم بالاتر و هزینه تولید کمتر نسبت به سامانه بی‌خاک‌ورزی اولویت دارد. در اولویت‌بندی تیمارها بر اساس معیارهای چندگانه، تیمارهای قطع آبیاری در مرحلة خمیریشدن دانه و آبیاری کامل در سامانههای خاک‌ورزی مرسوم و قطع آبیاری در مرحلة خمیریشدن دانه در سامانة کمخاک‌ورزی بازده برنامهای، درصد بازده فروش و نسبت فایده به هزینه بالاتری داشتند. با توجه به اینکه عملکرد دانة گندم در تیمار تنش آبی در مرحلة خمیریشدن دانه با آبیاری کامل در سامانة مرسوم و کمخاکورزی اختلاف معنی‌داری نداشت، میتوان در شرایط محدودیت آب، آبیاری را در مرحلـة انتهای پرشدن دانه قطع نمود. بهدلیل بالاتر بودن شاخصهای سودآوری سامانة خاک‌ورزی مرسوم، این سامانه برای کشاورزان اولویت دارد. چنانچه قیمت سایه‌ای آب آبیاری در محاسبهها وارد شود، مقدار کاهش هزینه تولید ناشی از آن، یکی از نتایج مطلوب سامانة خاک‌ورزی حفاظتی خواهد بود.

https://jam.tabrizu.ac.ir/article_19732_79002b5cdc8b53249738b8d0e4c8a384.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 10 2 2025 06 22 Evaluating the Performance of Optical Range Finder Sensors in Real-time Measurement of Soil Surface Roughness ارزیابی عملکرد حسگرهای فاصله‌یاب نوری در اندازه‌گیری بلادرنگ زبری سطح خاک 37 57 19733 10.22034/jam.2025.65789.1317 FA نسیم صالحی بابامیری گروه مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه بوعلی سینا - همدان - ایران حسین حاجی آقا علیزاده گروه مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه بوعلی سینا - همدان - ایران مجید دولتی گروه علوم و مهندسی صنایع غذایی، دانشکده فنی و منابع طبیعی تویسرکان، دانشگاه بوعلی سینا، همدان، ایران Journal Article 2025 01 30 Introduction Surface roughness measurements of agricultural soils play a critical role in assessing various factors, including tillage performance, surface water retention, soil resistance to rainfall-induced failure, seedbed preparation, and surface runoff management. Random roughness serves as a reliable vertical index due to its ease of calculation and a margin of uncertainty of approximately ±3 mm, making it suitable for distinguishing roughness classes. Roughness measurement methods can be categorized into contact and non-contact techniques. Traditional methods often employ a stop-and-go approach, which is both tedious and time-consuming. In contrast, optical range finder sensors, when mounted on a moving system, can measure soil surface roughness in real-time, significantly reducing measurement time and increasing efficiency. The purpose of this study is to measure soil surface roughness in real time using optical sensors in greenhouse conditions and compare the accuracy and precision of the two measurement methods in order to choose the appropriate method in precision tillage operations. Materials and Methods Surface roughness measurements of agricultural soils play a critical role in assessing various factors, including tillage performance, surface water retention, soil resistance to rainfall-induced failure, seedbed preparation, and surface runoff management. Random roughness serves as a reliable vertical index due to its ease of calculation and a margin of uncertainty of approximately ±3 mm, making it suitable for distinguishing roughness classes. Roughness measurement methods can be categorized into contact and non-contact techniques. Traditional methods often employ a stop-and-go approach, which is both tedious and time-consuming. In contrast, optical range finder sensors, when mounted on a moving system, can measure soil surface roughness in real-time, significantly reducing measurement time and increasing efficiency. The purpose of this study is to measure soil surface roughness in real time using optical sensors in greenhouse conditions and compare the accuracy and precision of the two measurement methods in order to choose the appropriate method in precision tillage operations. Results and Discussion Following sensor calibration, the relationship between the distances measured by the sensors and the reference pin meter method demonstrated a linear correlation under stationary conditions, with coefficients of determination (R²), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of 0.98, 2.3, and 2.7 for the infrared (IR) sensor, and 1, 0.2, and 0.36 for the laser sensor, respectively. Both range-finder sensors effectively measured distances under stationary conditions (R² > 0.98). The performance of the IR and laser optical sensors was further evaluated on a moving system, revealing a significant effect of measurement methods and surface class (p < 0.01) on the standard deviation (SD) roughness index. The interaction between measurement method and surface class was also significant (p < 0.01). The laser sensor was able to accurately detect roughness classes akin to the pin meter method at speeds below 2.6 kmh-1. However, at speeds exceeding 3.5 kmh-1, the laser sensor could only identify softer roughness classes, failing to measure roughness indices greater than 1.11 cm due to a decrease in data collection rates and the presence of larger clods in rougher classes. The results of variance analysis show that, speed did not have a significant effect on the roughness index. A strong correlation (R² > 0.9) was noted between roughness measurements from the pin meter and laser sensor at forward speeds below 3.5 kmh-1, while this correlation decreased to 0.79 at 4.8 kmh-1. Although the predictive power of the fitted model decreased at forward speeds of 4.8 kmh-1, it was largely successful in predicting the roughness class of the soil. The study suggests that utilizing laser sensors with higher data collection rates could facilitate the detection of roughness classes and enable soil profile mapping akin to the pin meter method, regardless of forward speed. Conversely, the IR method performed well only on wide and regular surfaces and struggled with irregular roughness levels, with R² values of 0.74, 0.69, 0.69, and 0.7 at forward speeds of 1, 2.6, 3.5, and 4.8 kmh-1, respectively. Consequently, at higher speeds, both the laser and IR sensors exhibited reduced compatibility with the pin meter method. The findings emphasize the potential of optical sensors for rapid SSR measurement, paving the way for more efficient practices in precision agriculture. Conclusion Selecting the appropriate range-finder sensor is essential for online SSR measurement. The findings of this research suggest that the rapid measurement of soil surface roughness can replace traditional, labor-intensive methods, streamlining the process and enhancing accuracy in precision tillage operations. Acknowledgement The authors would like to express their gratitude to Bu-Ali Sina University for their support of the present research اندازه‌گیری زبری سطح خاک‌های کشاورزی نقش مهمی در ارزیابی عامل‌های مختلف از جمله: عملکرد ادوات خاک‌ورزی، حفظ آب سطحی، آماده‌سازی بستر بذر و مدیریت رواناب سطحی دارد. روش‌های مرسوم اندازه‌گیری زبری اغلب از رویکرد توقف و حرکت استفاده می‌کنند که هم خسته‌کننده و هم زمان‌بر است. در صورت اندازه‌گیری بلادرنگ زبری خاک، زمان اندازه‌گیری به میزان قابل توجهی کاهش خواهد یافت و راه انجام اقدام‌های کارآمدتر در کشاورزی دقیق هموارتر می‌شود. در این مطالعه عملکرد حسگرهای نوری مادون قرمز و لیزری روی یک سیستم متحرک مورد ارزیابی قرار گرفت. نتایج نشان داد که سرعت دستگاه، تأثیر معنی‌داری بر عملکرد سامانه نداشت. اثر متقابل روش اندازه‌گیری و کلاس زبری در سطح یک درصد معنی‌دار بود. یک ارتباط قوی بین زبری‌ به‌دست آمده از پین‌متر و حسگر لیزری در سرعت‌های پیش‌روی کم‌تر ازkmh-1 5/3 (R² > 0.9) مشاهده شد، با وجود این‌که در سرعت kmh-1 8/4 مقدار ضریب تبیین مدل برازش به 79/0 کاهش یافت؛ اما تا حد زیادی موفق شد زبری واقعی را پیش‌بینی کند. این مطالعه نشان داد که استفاده از حسگرهای لیزری با نرخ جمع‌آوری داده‌های بالاتر می‌تواند تشخیص کلاس‌های زبری را تسهیل کند و ترسیم نقشه پروفیل خاک را شبیه به روش پین‌متر امکان‌پذیر نماید. روش مادون قرمز در سطوح با پستی و بلندی منظم، اختلاف معنی‌داری با روش پین‌متر نداشت ولی در سطوح نامنظم، در سطح یک درصد اختلاف معنی‌داری با روش پین متر داشت. یافته‌های تحقیق حاضر، بر پتانسیل حسگرهای نوری برای اندازه‌گیری سریع زبری خاک تأکید نمود.

https://jam.tabrizu.ac.ir/article_19733_47a5356cbde734708f36242a682dea91.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 10 2 2025 06 22 Variation in Density and Moisture Diffusion Coefficient of Shrimp Affected by Shrinkage During Drying تغییر جرم حجمی و ضریب انتشار رطوبت میگو متاثر از چروکیدگی هنگام خشک‌شدن 59 73 20111 10.22034/jam.2025.66138.1320 FA محمد امین زندپور بخش مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه شیراز - شیراز – ایران سیدمهدی نصیری بخش مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه شیراز - شیراز – ایران مهدی مرادی حسن‌آباد بخش مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه شیراز - شیراز – ایران محمدامین نعمت‌اللهی بخش مهندسی بیوسیستم - دانشکده کشاورزی - دانشگاه شیراز - شیراز – ایران 0000-0001-5780-2723 Journal Article 2025 03 02 Introduction Drying agricultural and aquatic products is a widespread practice globally, aimed at increasing shelf life and preventing microbial growth. During the drying process, heat and mass transfer are dominant phenomena, which are closely linked to the moisture diffusion coefficient. Drying is a thermal process where heat and moisture transfer occur simultaneously, with moisture being reduced as the sample heats up. This process concludes when significant temperature changes, and moisture transfer stops once the moisture content reaches a stable point. The drying process can lead to product shrinkage and pore formation, which can affect the quality and efficiency of drying. Shrinkage occurring alongside moisture release, can impact moisture transfer during drying. In meat processing, muscle shrinkage influences the energy consumption and tenderness of meat products, a key quality trait. Shrinkage and structural changes also contribute to a reduction in food volume during drying. Without considering shrinkage in drying models, results may lack accuracy. Mathematical modeling is a powerful tool for studying moisture transfer phenomena and understanding drying processes. This study examines the effect of shrimp shrinkage on the moisture diffusion coefficient during hot air convection drying. The aim is to assess the impact of shrinkage on moisture diffusion in both linear and nonlinear models and select the most accurate relationship for practical applications. Materials and Methods In this study, 212 Vannamei shrimp (Litopenaeus vannamei) which were packed in a standard packaging size for export, with an average weight of approximately 3 kg per box, were purchased from Marjan-e-Bushehr Company. The frozen shrimp boxes were transferred to a laboratory and stored in freezer for experiments. Required samples from batch were randomly chosen and kept for 12 hours at laboratory temperature before testing and skinned. The moisture content of the shrimp was determined by drying 20 shrimp at 105°C for 24 hours. The shrimp were dried in a convective hot air dryer at temperatures of 40, 50, and 60 °C with air velocity of 1 and 2 m/s. Samples were prepared in two forms: intact shrimp and cuboid shape samples (15×17×30 mm). During the drying process, parameters such as mass, volume, thickness, and image data were collected at intervals. The drying continued until the moisture content of the shrimp reached 17 % (d.b.). The shrinkage of dried samples based on volume and surface area were calculated, and mathematical models were developed to describe the relationship between moisture content and shrinkage. These models, evaluated using MATLAB 2020, were used to calculate the moisture diffusion coefficient (De) for both intact and cuboid shrimp at different drying duration times. The results were analyzed using various error indices to validate the models' accuracy. Results and Discussion The results present the average bulk density values, ranging from 704 to 3374 kg/m³, indicating a significant effect of drying treatments on shrimp volume changes. Previous studies report a bulk density range of 1042 to 1093 kg/m³ for shrimp at 20°C. Drying weakens molecular bonds in shrimp’s protein structures, leading to shrinkage, which ultimately changes its volume and density. Factors like sample geometry, drying method, temperature, and air velocity also influence bulk density. During drying, a consistent trend was observed across all treatments, bulk density increased towards the end as volume reduction slowed. This is due to the decreased moisture content, while shrinkage continues to increase. Analysis showed that polynomial models fit the bulk density data better than linear or exponential ones. Additionally, drying time was strongly affected by temperature, with each degree increase reducing drying time by approximately 45 minutes. The study also explored the use of image processing for assessing shrinkage, which provided more accurate volume measurements compared to surface area-based methods. Moreover, the study suggests that for accurate models of moisture-shrinkage relations, intact samples and volume-based shrinkage measurements using toluene are preferable. However, image processing, being non-destructive, could serve as a suitable alternative. Lastly, moisture diffusion coefficients were affected by the drying conditions, with higher temperatures and air speeds leading to faster drying, as confirmed by other studies. The study also concluded that sample size impacts drying rates, with smaller samples drying faster. Conclusion It was found that the true density of shrimp increased due to greater volume reduction than mass change at the end of the drying process. Drying time was influenced by air temperature, velocity, and sample size (shape). The best models subjected to moisture-shrinkage data were Polynomial D2 and D3. Moisture diffusion coefficient was then calculated based on D3 shrinkage model, and it was found that this coefficient is inversely affected by the shrimp shrinkage. خشک کردن بخشی از راهبرد مهندسی صنایع غذایی برای پاسخ سریع به نیازهای بازار مصرف، کاهش هزینه تولید، افزایش ماندگاری، بازده و کیفیت محصول است. میگو یک محصول دریایی فصلی پر رونق در جنوب کشور برای مصارف خارج فصل یا صادرات، خشک میشود. برای خشک‌کردن صنعتی میگو، دانستن خصوصیات فیزیکی از اهمیت ویژهای برخوردار است. در این پژوهش اثر چروکیدگی میگو هنگام خشکشدن در یک خشککن هوای داغ همرفتی بر تغییرات جرم‌حجمی و ضریب انتشار رطوبت مورد بررسی قرار گرفت. میگوی وانامی (سفید آمریکایی) پرورشی با هوای داغ در دو سطح سرعت یک و دو متر بر ثانیه و سه سطح دمای 40، 50 و60 درجه سلسیوس خشک شد. از 212 عدد میگو، 106 عدد میگو به صورت مکعب مستطیل و 106 عدد میگو به صورت کامل مورد آزمایش قرار گرفت. در هر آزمایش دما و رطوبت هوای ورودی و خروجی خشککن، و جرم، حجم، ضخامت و رطوبت نمونه‌ها اندازهگیری شد. با استفاده از پردازش تصویر عکسهای تهیه شده در زمان آزمایش، مساحت نمونهها در فواصل زمانی مشخص محاسبه شد و بر مبنای این دادهها، چروکیدگی (بر مبنای مساحت) تعیین شد. همچنین، چروکیدگی واقعی با استفاده از تغییرات حجم نمونهها محاسبه گردید. بر دادههای نسبت رطوبت (مبنای خشک) و چروکیدگی مدلهای خطی و غیرخطی نمایی و چند جمله‌ای درجه دو و سه در نرمافزار MATLAB 2020 برازش داده شد و برای هر مدل شاخصهای SSE, RMSE, R2 محاسبه شد. مطابق این شاخصها، مدلهای چند جمله ای درجه دو و سه، بیشترین دقت برای تخمین چروکیدگی را داشتند. از این مدلها در حل معادله فیک استفاده شد و ضرایب انتشار رطوبت برای هر آزمایش به ازای هر مقدار چروکیدگی و نسبت رطوبت تعیین گردید. نتایج نشان داد که شکل نمونه و نوع مدل استفاده شده برای مقادیر چروکیدگی و محتوای رطوبت، بر مقدار ضرایب انتشار رطوبت محاسبه شده موثر بوده اند.

https://jam.tabrizu.ac.ir/article_20111_95cb78e7ea19c81ad2572fec3030222f.pdf

دانشگاه تبریز نشریه مکانیزاسیون کشاورزی 2383-126X 10 2 2025 06 22 Machine Learning Approach to Identify Tomato Bacterial Spot Disease before Symptoms Inclusion Using Hyperspectral Images کاربرد فناوری یادگیری ماشین در شناسایی بیماری لکه باکتریایی گوجه فرنگی 75 89 20342 10.22034/jam.2025.66015.1319 FA علی‌محمد شیرزادی‌فر بخش مهندسی بیوسیستم – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران 0009-0003-1950-0742 حانیه عباسی بخش مهندسی بیوسیستم – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران سید‌مهدی نصیری بخش مهندسی بیوسیستم – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران محمد‌امین زندپور بخش مهندسی بیوسیستم – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران سید‌محسن تقوی بخش گیاهپزشکی – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران محمد‌امین نعمت‌اللهی بخش مهندسی بیوسیستم – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران 0000-0001-5780-2723 فاطمه کاظمی بخش مهندسی بیوسیستم – دانشکده کشاورزی – دانشگاه شیراز – شیراز – ایران صادق زارعی - بخش گیاهپزشکی – دانشکده کشاورزی – دانشگاه صنعتی اصفهان – اصفهان – ایران Journal Article 2025 02 21 Introduction Tomato (Solanum lycopersicum L.) is a self-pollinating plant from the Solanaceae family, which includes over 3,000 economically significant species. It is one of the most consumed vegetables globally and ranks seventh in importance after crops like maize and rice. Native to the coastal plains of South America, tomatoes were domesticated in Mexico and introduced to Iran in the 19th century. Rich in lycopene, beta-carotene, flavonoids, and vitamin C, tomatoes are renowned for their anti-cancer properties. Globally, tomato production for processing is around 41 million tons, with Asia accounting for about 60% of production. In Iran, about 4.9 million tons were produced in the 2017-2018 agricultural year. Tomato plants are hosts to over 200 pests and plant diseases, with bacterial spot disease being one of the most damaging. The bacterial spot disease can persist in seeds and plant residues for up to 16 months. Detection of bacterial spot disease in early stage is crucial for efficient disease management, however traditional methods like field scouting are inefficient and prone to human error. Recent advancements in spectral imaging, such as hyperspectral imaging, offer a non-destructive, efficient way to detect early stage of plant diseases. This research study explores the use of hyperspectral imagery and machine learning algorithms to detect bacterial spot in tomato leaves before symptoms inclusion, improving early intervention and disease management. The objective of this research study aimed on employing three machine learning algorithms to classify healthy plants versus infected ones using spectral reflectance in the range of 400-800 nm. Before employing machine learning algorithms, the preprocessing methods were employed to remove the noise and improve the classifier algorithm 's performance. In order to find the earliest time for identifying bacterial spot disease before symptoms inclusion, the combination of preprocessing methods and classifier algorithms were also evaluated through 7 to 19 days after inoculation. Materials and Methods In this study, tomato seedlings were sourced from a farm in Bavanat, Fars province, Iran, and transferred to a greenhouse in the Faculty of Agriculture Shiraz University for controlled growth conditions. The plants were inoculated with Xanthomonas euvesicatoria pv. perforans at the 4-5 leaf stage. The research aimed to detect bacterial spot disease before symptoms inclusion, using hyperspectral imaging. Hyperspectral images of 100 tomato samples were taken using hyperspectral camera (Partov Afzar Sanat, Zanjan, Iran). Spectral reflectance of tomato plants was collected in the range of 400–800 nm before inoculation and over the seventh to the nineteenth day after inoculation bacterial spot disease. Data analysis was conducted using Python, where spectral data was preprocessed to remove noise using four methods, including standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivatives (FD, SD). Principal Component Analysis (PCA) was applied in order to feature reduction. Three machine learning algorithms including random forest (RF), gradient boosting machine (GBM), and support vector machine (SVM) were employed to classify healthy and infected tomato plants. The model's performance was evaluated based on confusion matrix using accuracy, precision, sensitivity, specificity, and F-measure. Accuracy is defined as the ability of the classifier algorithm to detect the healthy and the infected plants correctly. Sensitivity and specificity are defined as the proportion of the healthy or infected plants correctly classified. In the precision calculation, the number of actual predicted infected is divided by the total number of predicted infected plants that were classified as true or false. F-measure defines the harmonic mean of sensitivity and precision where it reaches its best value at 1.0 (perfect precision and sensitivity) and worst value at 0.0. The classification models were validated using a 70-30 split of training and testing data, and the training process was conducted through ten- fold cross validation to ensure reliable results. Results and Discussion The analysis of spectral reflectance of infected and healthy plants revealed that the bacterial spot disease has a significant effect on the spectral signature infected plants. The most changes on the spectral reflectance of infected plants were happened in range of 740 to 800 nm, which is part of the NIR area and is related to changes from the structure of the leaf tissue. The results showed that FD, SNV, MSC preprocessing methods significantly improved the classification accuracy of healthy and infected plants over the seventh to the nineteenth day after inoculation bacterial spot disease. The FD preprocessing on the 7 and 10 days after inoculation resulted in the highest accuracy (98%), while MSC and SD methods performed best after 14 and 19 days. The RF, SVM, and GBM classification algorithms could classify the infected plants versus healthy plants with 98%, 100% and 100% accuracy respectively. The results indicated that the 7th day after inoculation was the most reliable and earliest time before symptoms inclusion for classifying infected and healthy tomato plants. The highest classification accuracy was achieved with SVM and GBM algorithms and FD preprocessing method on the 7th day (100%), and SVM with MSC on the 19th day (98%). Conclusion The results of this research indicated the ability of machine learning algorithms and hyperspectral imagery for classifying healthy plants versus infected ones with bacterial spot disease in tomato plants before symptoms inclusion. Three classification machine learning algorithms including RF, SVM and RGB could classify infected plants on the 7th day after bacterial spot inoculation with more than 97% accuracy. Therefore, spectral reflectance of potato plant leaves in the range of 400 to 900 nm can be a potential way to identify bacterial spot disease in potato plants in early stage of disease for efficient crop management. یکی از بیماریهای مهم در گوجهفرنگی بیماری لکه باکتریایی است که با تشخیص به موقع و کنترل آن میتوان موجب جلوگیری از گسترش آن در سطح مزرعه و کاهش خسارات اقتصادی شد. در این پژوهش از روش غیرمخرب تصویربرداری فراطیفی در محدوده 800-400 نانومتر به منظور تشخیص بیماری لکه باکتریایی گوجه فرنگی حاصل از باکتری Xanthomonas perforans در مراحل اولیه بیماری و پیش از ظهور علائم ظاهری استفاده شد. عکسهای ابر طیفی و امضای طیفی نمونه ها در روزهای هفتم، دهم، چهاردهم و نوزدهم پس از مایه زنی جمع آوری شد. در این پژوهش از چهار روش پیش پردازش، تصحیح پراکندگی ضربی ، متغیر نرمال استاندارد، مشتق اول و مشتق دوم به منظور حذف نوفه‌ها و استخراج اطلاعات مفید برای تحلیل و طبقه بندی داده ها، استفاده شد. سپس طبقهبندی گیاه سالم و بیمار و با استفاده از سه الگوریتم جنگل تصادفی (RF)، ماشین بردار پشتیبان (SVM) وتقویت گرادیان (GBM ) در فناوری یادگیری ماشین انجام شد. با توجه به نتایج بدست آمده در روز هفتم و دهم روش مشتق اول و در روز چهاردهم و نوزدهم دو روش مشتق دوم و تصحیح پراکندگی ضربی تأثیربسیاری بر کیفیت طبقه-بندی گیاهان سالم و بیمار داشت. نتایج حاصل از طبقهبندی دادهها نشان داد که هر سه الگوریتم RF، SVM و GBM با دقت بیشتر ار 90 درصد به منظور جداسازی دو کلاس گیاه سالم از بیما ارائه کردند. روش SVM با دقتی بالای 97 درصد در تمام روزهای موردنظر، به عنوان بهترین روش به منظور جداسازی گیاهان سالم از گیاهان بیمار بدون علائم ارائه کرد. همچنین روز هفتم پس از مایه زنی ، ترکیب روش SVM با مشتق اول با صحتی معادل 100 درصد به عنوان سریعترین زمان به منظور جداسازی گیاهان سالم از گیاهان بیمار بدون علائم انتخاب شد.

https://jam.tabrizu.ac.ir/article_20342_336406e1f032d444b9669faa4b4851c2.pdf