نشریه ماشین های کشاورزی
سال سیزدهم شماره 2 (پیاپی 28، تابستان 1402)

Robots have been used for material handling for many years, and their applications have greatly expanded with the integration of intelligent technologies. While numerous researchers have proposed various robots for this field, it is crucial to design customized configurations that are suitable for agricultural farms. However, research in our country has been limited to a few mobile agricultural robots. The main focus of this paper is to design and model workspaces and analyze the kinematics of manipulators in agricultural settings.

This article investigates the workspace and kinematics of a robot manipulator to design and manufacture a four-DOF manipulator for farming. This manipulator will be capable of performing a variety of tasks, but the goal of this project is to enable it to load and unload materials and products on the farm as an auxiliary force for the farmer.
When designing and analyzing a manipulator, the first step is to determine the specific task that the robotic arm will perform. For example, consider a scenario where the task involves loading or unloading forage packages from a trailer at a designated location. This task specification forms the basis for further design and analysis, ensuring that the manipulator is appropriately designed to meet the requirements of the task.
An intelligent robotic arm that is attached to a tractor can perform this operation in the shortest possible time without the intervention of human workers. Otherwise, a large number of laborers would be required to move boxes weighing 10 kg over distances of 3 to 4 meters and heights of 1 to 2 meters, which would require a great deal of torque.
At this stage, the design of the arm kinematics model, direct kinematic equations, velocity kinematics, and Jacobian matrix solving were performed. The calculations were carried out using two methods manual calculation and kinematic modeling in MATLAB software for three arm configurations in two simulation tests. The results of both methods were compared.
The workspace analysis of the selected manipulator configurations, as well as the use of arm kinematic performance evaluation indices, were illustrated in graphs.

The issue of moving forage packages on the farm is described below. If a farmer were to move 48 packages of fodder weighing about 10 kg manually (using human workers) in the workspace modeled in Figure 10, each package would take an average of 30 seconds to be moved reciprocally along an unobstructed path. Hence, it would take approximately 24 minutes to move all the packages. However, the linear speed of the final operator of the robot arm during the first test was found to be 1 meter per second, which is 3.7 times faster than the manual work scenario, and the total movement of the packages can be completed in about 6.5 minutes.
Upon analyzing the velocity diagrams of the final performer in both tests, it becomes evident that there is not much variation in speed and acceleration due to the change in configurations. The evaluation of robot workspace indicators was conducted using two methods workspace index and structural length index. These indicators were calculated for all three configurations, and the results indicated that Configuration Type 1 was the most suitable option. Furthermore, the manipulability index of the robot arm was assessed based on the obtained diagrams for all three configurations in the two tests. It was observed that Configuration Type 1 outperformed the other two types in terms of score, indicating its superior performance. This aligns with the suggestion made by Yoshigawa for the first three joints of the Puma robot.
Overall, the results suggest that Configuration Type 1 is one of the most favorable options, ensuring better performance for the final performer.

One of the main considerations when using robots in agriculture is the appropriate kinematic design of joints and links for work operations. Using the example of robots assisting with moving products on the ground, it can be seen that using robots significantly reduces the time required compared to manual labor. Furthermore, in terms of energy consumption and cost within a certain period, the use of robots has economic justification.
Based on the studies conducted, Configuration Type 1 passed the kinematic path in both tests with a higher manipulability index and a more suitable workspace index based on both calculated criteria. Therefore, this configuration is recommended for the design of robots for the operation of moving products on the ground.

Various methods have been performed to control weeds in the world and the use of herbicides is one of them, but public concerns about human health have changed interest in alternative methods. Thermal methods based on flame-weeder, hot air, steam, and hot water have the potential to control weeds, but due to the high cost are not economical. Electromagnetic waves transfer energy into weeds and finally destroy them. The effect of radiation on plant mutation, high consumption of energy, and human health are problems for this approach. Unlike other methods, electrical energy is an ideal and non-chemical method for weeds. This method applies high voltage to weeds, their roots, and soil so that electric currents pass through them, and the vaporization of the liquid content of weeds kills the weeds. To increase the severity of damage to weeds, the development of a feedback mechanism is required. The ultrasonic sensor measuring physical parameters like plant height is a simple method. Some complex sensing systems include optical sensors such as infrared, and machine vision that require high-speed processors and expensive equipment. In this project, as a simple method, the monitoring of the electrical current passing through weeds was used for developing the feedback mechanism and increasing electric damage to weeds.

In this study, the system consisted of a high-voltage device that generated a 15 kV AC voltage to kill weeds, as well as a feedback mechanism that included a sensor to measure the electric current on the input of the weed killer and identify the presence of weeds and their annihilation. All parts were installed on a robotic platform, and an application on a laptop was connected to it via an access point for navigation and data reception. The system was tested in a greenhouse lab with various weeds. Initially, a test was performed to investigate the effect of high voltage on the weeds and establish relationships between the electric currents passing through weeds and their presence (before and after annihilation). During the test, the system was guided along a path and applied high voltage to kill the weeds. The feedback mechanism was then calibrated based on the extracted data on electric current relations. This allowed the system to detect weeds and their annihilation, enabling it to move to the next target once a weed had been eliminated. After calibration, a comparative test was conducted to evaluate the weed-killing efficiency of the two methods (with and without the feedback mechanism), and the results were analyzed using a t-test with p ≤ 0.01.

The observations indicated that the input electric current on the weed killer was dependent on the electric current passing through weeds. When the high-voltage electrode touched a weed, the electric current passed through it increased, and simultaneously, the high electrical energy destroyed the weed. After the removal of the weed, the electric current rapidly decreased. The average energy consumption per weed plant was estimated to be 250 joules, which can be compared with other methods. The final test comparing the use and non-use of the feedback mechanism revealed significant differences (P < 0.01) between the results obtained with and without the mechanism, demonstrating that the feedback mechanism increased the efficiency of weed annihilation. The sensing system used in the developed feedback mechanism is a simple method that is affected by the electrical resistivity of weeds. As such, it did not mistakenly detect other objects as weeds, unlike an ultrasonic mechanism. Based on these results, monitoring the electrical current passing through weeds proved to be a suitable method for developing a feedback mechanism for the weed killer to identify the presence of weeds and their annihilation.

The use of high voltage as a non-chemical and alternative method for weed control has shown promising results. The study revealed that measuring the electric current applied to the weed killer was an effective and straightforward approach to developing a feedback mechanism. This mechanism aids in identifying the presence of weeds and ensuring their elimination by intensifying the damage inflicted on them through the application of high electrical energy. To further enhance the efficiency and speed of weed control, future research should consider integrating an automatic guidance mechanism with the weed killer.

Activated carbon has a wide range of applications as a porous material in the liquid or gas phase adsorption process. The physical process of activated carbon production is divided into two stages thermal decomposition and activation. In this study, only the activation stage has been studied because it is very important in the properties of activated carbon being produced.The production of activated carbon from horticultural waste not only leads to cheap production and supply of many industrial and environmental necessities but also reduces the amount of the produced solid waste. Iran produces about 94,000 tons of pistachio husk annually, which is a good raw material for the production of activated carbon. The profitability index of activated carbon production in Iran is equal to 3.63, which in the case of export, the profitability index will be tripled.Studies have shown that temperature, period, and activation gas flow are the key factors affecting burn-off and iodine number during activated carbon production. Among the various activators tested, steam was found to be the most efficient, with the fastest activation time. For pistachio crops, the minimum iodine number required for economic efficiency is 600 mg g-1, while the highest specific surface area according to the BET test is 1062.2 m2 g-1.

A Mannesmann tube made of 10 mm thick steel was used to construct the rotating reactor. To minimize heat loss during operation, the kiln body was insulated with a ceramic blanket capable of withstanding temperatures up to 1400°C. The kiln had a length and diameter of 190 cm and 48 cm, respectively, and operated at a temperature of 600°C, requiring approximately 25 kWh of energy for heating. CATIA V5 R21 software was employed to design the device, while ANSYS R20 software was used for thermal and mechanical analysis. The rotary reactor was identified as a critical component due to the high levels of thermal and mechanical stress it experiences. To address these issues, a thermal and fluid analysis was conducted, followed by a mechanical analysis using the results from the prior step. Subsequently, experimental tests were performed on the actual model, and the results were analyzed using statistical methods, including the T-student test in IBM SPSS software.The central heating unit and its surroundings were modeled using ANSYS CFX to obtain valuable information on fluid velocity, radiant properties, and heat transfer within the kiln and surrounding area at an operating temperature of 650°C. The analysis revealed uniform steam flow velocity between the kiln and the heating unit. To accommodate longitudinal expansion resulting from heat stress, taller rollers were employed to allow freedom of movement in that direction, while the lateral movement was unrestricted. This arrangement allows the reactor length to increase under varying temperatures. The reactor's end was designed with grooves and pressure plates, incorporating abrasion and compression plates made from refractory fibers to effectively seal the device. Furthermore, telescopic movement of the parts compensates for expansion effects.

The operating temperature of the system was gradually increased to reduce thermal stresses in the reactor shell. This led to a maximum increment in a longitudinal increase of 11.75 mm. Results from five sets of experimental tests and five software analyses demonstrated no significant differences between the experimental and analytical results at a significance level of 5%. Based on the thermal contour analysis, the thickness of the insulation layer was determined to be 5 cm. To control the operating temperature of the device, two methods were employed: adjusting the flame length of the burner and using different types of exhaust outlets. These measures effectively reduced thermal stress on the device.

Thermal and mechanical analysis were useful methods for predicting heat distribution, thermal stresses, and potential dimensional changes in the activated carbon reactor. To compensate for possible alterations in the reactor's length and diameter, abrasive plates and friction washers were implemented. Careful control of fuel input to the burner and regulation of exhaust gas flow helped effectively reduce thermal stresses on the device.

Solar energy is one of the most important sources of renewable energy, and it is used to address problems related to energy needs, including increasing fossil fuels, rising energy transportation costs, higher energy demand worldwide, and greenhouse gas emissions. Solar collectors harness the sun's thermal energy to convert it into useful and usable energy. Solar collectors are divided into several types, including parabolic trough collectors (PTCs), linear Fresnel reflectors (LFRs), solar plates, and central towers. Among these, the most common heat generation systems are linear adsorption technologies. In this study, we examine the use of LFR technology for greenhouse heating during the winter in Shahrekord.

Previous studies (Huang et al., 2014) were used for optical analysis. The Daneshyar model was utilized to calculate the amount of solar energy available at a particular location. Mathematical formulas were employed to calculate the instantaneous energy equilibrium, and a heat transfer resistance model was developed to calculate the heat loss of different parts of the collector. To create a model, the total amount of exergy must first be calculated, which can be done by using the Petlla formula given by Bellos et al. (2019).

The following results were obtained from this study:The proposed mathematical model for calculating solar energy was accurate in terms of daily and instantaneous performance. This model was valid for both clear and cloudy days, making it applicable in a variety of weather conditions.The maximum useful heat production of the current system for February was about 2.5 kW, resulting in an increased liquid temperature of 16 degrees Celsius in the heat tank.The maximum thermal efficiency of the Fresnel collector during the day was 64%, while the average daily efficiency was 56.4%.The most significant parameters that affected the production of useful energy were the position of the sun during the day and the number of cloudy days.The system was capable of heating stored water to 98 degrees per day, available for up to 14 hours.The system under consideration can be used to produce heat up to 1260 watts for 15 hours without heating the tank. The generated heat can be utilized in the food industry for steam production and industrial desalination of water.The decrease in exergy efficiency was due to the reduction in the thermal efficiency of the system and the increase in the thermal difference between the collector and ambient temperatures. Higher values can be achieved by reducing the heat losses, which is a reason to reduce the exergy efficiency of the system.

This paper investigated the daily performance of a linear Fresnel collector with an 18 square meter mirror field, a parabolic collector, and an insulated storage tank with a volume of 250 liters. The investigation included experimental analysis and theoretical formulation of thermal phenomena under the weather conditions of Shahrekord. The mathematical model developed for this system is based on the energy balance in the collector and storage tank. The results show that this is an efficient greenhouse heating system, with an average thermal efficiency of 56%, which is reasonable and competitive with other similar technologies. Additionally, the cost of construction and maintenance of this system is much lower than that of competitors.

The use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, corn oil can be a good alternative to other oils high in saturated fatty acids, as it reduces blood cholesterol levels.This study employed an electrical nasal system to detect the amount of palm oil present in corn oil. The properties extracted from the signals obtained by the device were processed using principal component analysis, artificial neural networks, infusion, and response surface methods. The results were then compared to find the best method for detecting palm oil levels in corn oil.

The required palm oil was obtained from the Nazgol Oil Agro-industrial Plant, while the corn oil was obtained from natural lubrication centers. To prepare samples with different percentages of palm oil, 75 grams of palm oil and corn oil with the specified percentages were mixed and stored in special containers.In the electrical nose system, ten metal oxide semiconductor sensors (MOS) were used to collect output data. Pre-processing operations were performed on this data using RSM, ANFIS, PCA, and ANN methods to estimate the percentage of palm oil in corn oil. The Unscrambler V.9 software, Design Expert 8.07.1, and MATLAB R2013a were used to analyze the results.

Based on the Score plot, PC-1 and PC-2 explain 53% and 25%, respectively, describing the variance between samples for a total of 78 data points. The analysis indicates that sensors 7 and 8 have minimal impact on the detection process and can be removed from the sensor array. When reducing the cost of the olfactory system's sensor array, sensor 6 plays a more significant role than other sensors in detecting corn oil with palm composition.According to the loading diagram of palm percentage in corn oil, the MQ6 sensor had the least effect in classifying different percentages of palm in corn oil and pattern identification. Out of all functional parameters (accuracy, sensitivity, and specificity), the RSM method is deemed more appropriate for determining the percentage of palm in corn oil.Regarding the separation of corn oil and palm oil by ANFIS, RSM, and ANN, the results in Table 3-1 indicate that the RSM method is better suited for classifying corn and palm oil.

In this study, we used an electronic multi-sensor system based on metal oxide sensors to analyze various aromatic compounds in different oil and palm samples and to detect the presence of palm. The system provided comparable information for classifying different samples of palm oils. Using PCA, ANN, ANFIS, and RSM methods, we evaluated the system's performance in differentiating and classifying various oil and palm samples.The results obtained from the loading diagrams for the detection of palm in corn oil indicated that the MQ6 sensor had the least impact on the detection process. Therefore, this sensor can be removed from the sensor array.Additionally, our analysis showed that using the RSM method is more effective in detecting different percentages of palm in corn oil. Overall, our study demonstrates the efficacy of the electronic multi-sensor system in analyzing different oil and palm samples and detecting the presence of palm.

Maize is one of the most important cereal crops worldwide, providing staple food for people globally. Counting maize tassels provides essential information about yield prediction, growth status, and plant phenotyping, but traditional manual approaches are expensive and time-consuming. Recent developments in technology, including high-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs) and advanced machine-learning techniques such as deep learning (DL), have been used to analyze genotypes, phenotypes, and crops.In this study, we modified the YOLOv5s single-stage object detection technique based on a deep convolutional neural network and named it MYOLOv5s. We incorporated BottleneckCSP structures, Hardswish activation function, and two-dimensional spatial dropout layers to increase tassel detection accuracy and reduce overfitting. Our method's performance was compared with three state-of-the-art algorithms: Tasselnetv2+, RetinaNet, and Faster R-CNN. The results obtained from our proposed method demonstrate the effectiveness of MYOLOv5s in detecting and counting maize tassels. 

The High-Intensity Phenotyping Site (HIPS) dataset was collected from the large field at the Agronomy Center for Research and Education (ACRE) of Purdue University, located in West Lafayette, Indiana, USA during the 2020 growing season. A Sony Alpha 7R-III RGB camera mounted on a UAV at a 20m altitude captured high-resolution orthophotos with a pixel resolution of 0.25 cm. The dataset consisted of two replications of 22 entries each for hybrids and inbreds, planted on May 12 using a two-row segment plot layout with a plant population of 30,000 per acre. The hybrids and inbreds in this dataset had varying flowering dates, ranging from 20 days between the first and last variety.This article uses orthophotos taken on July 20th and 24th to train and test the proposed deep network "MYOLOv5s." These orthophotos were divided into 15 images (3670×2150) and then cropped to obtain 150 images (608 × 2048) for each date. Three modifications were applied to the original YOLOv5s to form MYOLOv5s: BottleneckCSP structures were added to the neck part of the YOLOv5s, replacing some C3 modules; two-dimensional spatial dropout layers were used in the defect layer; and the Hardswish activation function was utilized in the convolution structures. These modifications improved tassel detection accuracy. MYOLOv5s was implemented in the Pytorch framework, and the Adam algorithm was applied to optimize it. Hyper-parameters such as the number of epochs, batch size, and learning rates were also optimized to increase tassel detection accuracy.

In this study, we first compared the original and modified YOLOv5s techniques, and our results show that MYOLOv5s improved tassel detection accuracy by approximately 2.80%. We then compared MYOLOv5s performance to the counting-based approach TasselNetv2+ and two detection-based techniques: Faster R-CNN and RetinaNet. Our results demonstrated the superiority of MYOLOv5s in terms of both accuracy and inference time. The proposed method achieved an AP value of 95.30% and an RMSE of 1.9% at 84 FPS, making it about 1.4 times faster than the other techniques. Additionally, MYOLOv5s correctly detected the highest number of maize tassels and showed at least a 17.64% improvement in AP value compared to Faster R-CNN and RetinaNet, respectively. Furthermore, our technique had the lowest false positive and false negative values. The regression plots show that MYOLOv5s provided slightly higher fidelity counts than other methods.Finally, we investigated the effect of score values on the performance of detection-based models and calculated the optimal values of hyperparameters.

The MYOLOv5s technique outperformed other state-of-the-art models in detecting maize tassels, achieving the highest precision, recall, and average precision (AP) values.The MYOLOv5s method had the lowest root mean square error (RMSE) value in the error counting metric, demonstrating its accuracy in detecting and counting maize tassels.We evaluated the correlation between predicted and ground-truth values of maize tassels using the R2 score, and for the MYOLOv5s method, the R2 score was approximately 99.28%, indicating a strong correlation between predicted and actual values.The MYOLOv5s method performed exceptionally well in detecting tassels, even in highly overlapping areas. It accurately distinguished and detected tassels, regardless of their proximity or overlap with other objects.When compared to the counting-based approach TasselNetv2+, our proposed MYOLOv5s method showed faster inference times. This suggests that the MYOLOv5s method is computationally efficient while maintaining accurate tassel detection capabilities.

Rice is one of the most important main food sources in Iran and the world. The correct identification of the type of pest in the early stages of preventive action has a significant role in reducing the damage to the crop. Traditional methods are not only time-consuming but also provide inaccurate results, As a result, precision agriculture and its associated technology systems have emerged. Precision agriculture utilizes information technology such as GPS, GIS, remote sensing, and machine learning to implement agricultural inter-farm technical measures to achieve better marginal benefits for the economy and environment. Machine learning is a division of artificial intelligence that can automatically progress based on experience gained. Deep learning is a subfield of machine learning that models the concepts of using deep neural networks with several high-level abstract layers. This capability has led to careful consideration in agricultural management. The diagnosis of disease and predicting the time of destruction, with a focus on artificial intelligence, has been the subject of much research in precision agriculture. This article presents, in the first step, a trained model of the Chilo suppressalis pest using data received from the smartphone, validated with the opinion of experts. In the second step, we introduce the developed system based on the smartphone. By using this system, farmers can share their pest images through the Internet and learn about the type of pest on their farm, and finally, take the necessary measures to combat it. This operation is done quickly and efficiently using the developed artificial intelligence. In the continuation of the article, the second part introduces the materials and methods, and the third part presents the results. The fourth section also discusses and concludes the research.

Chilo suppressalis is one of the most important pests of rice in temperate and subtropical regions of Asia. The conventional approach employed by villagers to gather the Chilo suppressalis pest entails setting up a light source above a pan filled with water infused with a pesticide. At sunset, these insects are attracted to the light and fall into the water in the pan. This method is known as optical trapping. After catching the pest using optical traps, they are collected from the water surface, and their photo is taken with a mobile phone based on the location of the optical trap.The proposed method in this research consists of three main steps. Firstly, the farmer utilizes the software provided by the extended version known as Smart Farm. The farmer captures an image of the Chilo suppressalis pest and sends it along with its location to the system. The Smart Farm software program carries out image processing and pest range detection operations. The user then verifies the accuracy of the pest detection. In the second step, the images sent by the farmer are processed by the pre-trained model within the system. The model analyzes the images and determines the presence of the pest. Finally, after identifying the type of pest, the results, along with recommended methods for pest control, are sent back to the farmer.In summary, In this method, farmers employ the Smart Farm software to capture and transmit images of the Chilo suppressalis pest. The captured images then undergo image processing and pest range detection as the next steps in the process. The results, including pest identification and control methods, are then returned to the farmer.

The model has been designed with 400 artificial neural network processing units (APCs), achieving accuracy percentages of 88% and 92%. To conduct a more detailed study of the proposed model, the statistical criteria of recall and F-score were used. Based on the calculations, the trained model demonstrated a recall score of 91%. This criterion shows that the model was able to identify a large percentage of what was expected to be identified by the model. Additionally, the F-score, with an acceptable percentage of 88%, confirmed the accuracy of the trained model.

Researchers have always been highly interested in the valuable data freely provided by farmers for their studies and analyses. In this study, an intelligent system was designed for identifying types of pests such as worms and stalk eaters, which can automatically determine the pest type from the image sent by the farmer using artificial intelligence and deep learning. By utilizing the developed system, farmers can be informed of the type of pest present on their farm in the shortest possible time, with minimal required software training.

It is predicted that the world population will grow to 9.3 billion by 2050 and the urban population will increase by 73%, growing from 3.6 billion to 6.3 billion. This huge population requires abundant food production. A plant factory with artificial light (PFAL) is a closed growing system that is insulated against heat and air. The plants grow on shelves under horizontal artificial lighting. The main goal of PFAL is commercial plant production, but mini PFALs do not have commercial goals and are used to produce plants in small domestic sizes. Plants that are less than 30 cm tall, and grow well in relatively low light conditions and at high planting densities, are suitable for the plant factory. Therefore, plants such as rice, wheat, and potatoes are not suitable for cultivation in a plant factory.The main purpose of this research is to study the proper light quality for growing radish plants. All light treatments had a significant effect on biomass, sugar, and photosynthetic pigments of radish. The results showed that the highest amount of chlorophyll a was 0.964 mg g-1 fresh leaf weight and the lowest amount was 0.318 mg g-1 fresh leaf weight. For chlorophyll b, the highest value was 0.666 mg g-1 wet weight and the lowest value was 0.229 mg g-1 wet weight. The highest and lowest carotenoid contents were 74.75 mg g-1 and 30.6 mg g-1 wet weight, respectively. The highest sugar content was 0.717 μg g-1 dry weight and the lowest was 0.02 μg g-1 dry weight. The highest fresh and dry weights of the plant were 0.27 g and 0.014 g, respectively, while the lowest values recorded were 0.155 g and 0.007 g, respectively. In this study, plant length was also examined, but no significant difference was observed between different light treatments. Based on these findings, it can be concluded that the light composition (R2, G0, B1) was the most suitable light regime for use in the designed system.

The plant studied in this investigation was radish. The place of growth was a vertically built system consisting of four floors, each divided into two sections. A controller was required in each section to regulate parameters such as light time, temperature, and moisture. The controllers were designed using Fritzing software and built with parts and sensors like DHT 11, Arduino UNO based on ATMEGA328P, Relay module Arduino, data logging shield, and driver module RC. A programming platform like Arduino was used to write codes for controlling the remaining parameters. This study tested seven different light treatments, plus sunlight as a control, to investigate their effects on radish growth. The light treatments were developed by adjusting the number of three different lights: red, green, and blue. LEDs were installed after designing and constructing the m-PFAL system. Based on previous research conducted in this field, all LED lights were positioned above the shelves to ensure that the plants received an appropriate amount of light in a vertical orientation. Additionally, light reflectors were installed beside the plants to provide proper lighting for the lower leaves. The experimental design involved a completely randomized design with eight treatments and three replications, and all data analysis was conducted through SAS software. The average comparison was performed using the Duncan method at a probability of 1% and 5%.

The results indicate that the light regime (R2, G0, B1) resulted in the highest amount of chlorophyll "a", which was significantly different from both the control and other treatments. The treatment with the lowest amount of chlorophyll "a" was (R1, G0, B0), which did not differ significantly from the control or (R1, G1, B1). The treatment with the highest amount of chlorophyll "b" was (R2, G0, B1), which differed significantly from the control but not from (R2, G1, B0) or (R1, G0, B2). Using a mixed light treatment of blue and red resulted in higher amounts of photosynthesis pigments, especially when the red light was more prevalent. The treatment with the highest wet weight was (R2, G0, B1), which did not differ significantly from natural light. The treatment with the lowest wet weight was the just red light treatment, which was much lower than the other treatments. The dry weight of the radish was 4-6 percent of its wet weight, and the treatment with the highest dry weight was (R2, G0, B1), which did not differ significantly from (R0, G1, B2) or (R1, G0, B0). The treatment with the highest amount of sugar was (R2, G0, B1), which was significantly higher than other optical regimes used and natural light. Because the production of carbohydrates and sugar is directly related to photosynthesis, it can be concluded that the state of photosynthesis was most proper in the (R2, G0, B1) treatment.

This study investigated the optimal light quality for the healthy and rapid growth of radish plants in a plant factory. LED lights can be an excellent alternative to natural light when there are limitations, such as in greenhouses or multi-floor plantings. The results show that the best light mixture was red and blue lights, with more red light than blue light, while the worst light regime was just red color, which had a negative effect on all parameters.

The permissible exposure time to vibration for the operator is one of the key factors in maintaining the operator's health while optimizing machinery and equipment. The tractor studied was the ITM475, manufactured in Iran. The purpose of this study was to calculate the operator's permissible vibration exposure time while using the tractor to ensure the driver can maintain good bodily health.

In this study, experiments were conducted using a 3-axis vibration meter based on the ISO 2631 standard. The obtained data were analyzed through a factorial experiment using 18 treatments and 3 replications. The factors studied were engine rotation speed (at three levels of 1000, 1500, and 2000 rpm), road type (dirt and asphalt), and gear position (at three levels of 1, 2, and 3).

Various total vibration models were obtained for the tractor, and their determination coefficient varied from 90.11% for gear No. 3 on an asphalt road to 100% for gear No. 1 on an asphalt road and gear No. 2 on a dirt road. The maximum whole-body vibration, and consequently the minimum permissible exposure time, was observed for gear No. 3 at an engine rotation speed of 2000 rpm on a dirt road, which was 1.49 and 1.16 hours, respectively.

The maximum whole-body vibration experienced during an 8-hour tractor-driving session was measured at 0.85 m s-2. It is important to note that the permissible exposure time decreases as vibration levels increase, and it reaches a limit of 1.16 hours. To ensure drivers adhere to these permissible exposure times across various driving conditions, measures must be implemented to reduce tractor vibration and minimize its transmission to the driver. By reducing overall tractor vibration and minimizing its impact on the driver, it becomes possible to increase the permissible exposure time for drivers.

The harvesting stage is the most crucial phase in peanut production. In other words, one of the critical stages in producing this product is the harvest stage. Although it has its difficulties, this stage is associated with significant losses, which experts attribute to the high economic value of peanuts. In recent years, farmers in the Moghan Plain have also started considering this product due to the special conditions of the Iranian economy. In 2020, this study investigated three methods of peanut harvesting in two stages: manual, tractor-mounted thresher (semi-mechanized), and harvesting with a pull-type combine. The first stage involves the complete removal of the plants from the soil, while the second stage involves drying and separating the peanut pod from the plant in Moghan.

The experiment followed a split-plot design in the form of randomized complete blocks with four replications. The main plot consisted of soil moisture levels at harvest time, which were tested at three different levels: a1- 21%, a2- 18%, and a3- 15%. The sub-plot involved testing the separation of peanut pods from the plant using three different methods b1- combine harvesting, b2- harvesting with a tractor-mounted thresher, and b3- manual harvesting. The study evaluated important harvest indicators such as quantitative loss (first and second-stage losses), actual field capacity, harvest time, and the number of required laborers. The results led to the identification of the best harvesting system.

The study revealed that the optimal soil moisture content for the initial stage of harvest was 18%. For most parameters, there was a significant difference observed among treatments at the 1% level. The pull-type combine method had the highest farm capacity with a maximum of 0.46 ha per hour, while the manual harvesting method had the lowest capacity with a minimum of 0.006 ha per hour. The total losses ranged between 5.95% and 10.58%, with the manual harvesting method exhibiting the lowest loss and the pull-type combine method showing the highest loss. Furthermore, the manual harvesting method required more labor compared to the other methods.

Based on the obtained results, it is recommended to use a pull-type combine for the early harvesting of peanuts and a manual method for obtaining high-quality peanuts in the Moghan region.