Reduce Labor Costs Research Articles

Embedded System – Based Pesticides Spraying Robot

The agriculture sector is undergoing a transformation with the integration of robotics and automation. One notable development is the emergence of autonomous pesticide spraying robots, designed to enhance precision, efficiency, and sustainability in crop protection practices. This abstract explores the technological advancements, operational mechanisms, and potential implications of these innovative robots. Autonomous pesticide spraying robots leverage cutting-edge technologies such as artificial intelligence, machine learning, and advanced sensors to navigate fields, identify crop areas, and precisely apply pesticides. Equipped with GPS, LiDAR, and cameras, these robots can map fields, detect obstacles, and optimize spraying patterns in real-time, reducing pesticide usage while ensuring effective pest control. Furthermore, the integration of data analytics enables continuous improvement in spraying strategies, tailored to specific crop types, environmental conditions, and pest pressures. The deployment of autonomous spraying robots offers several benefits to farmers, including increased operational efficiency, reduced labor costs, and minimized environmental impact. By eliminating the need for human operators and optimizing pesticide application, these robots contribute to safer working conditions and reduced chemical exposure risks. Moreover, their ability to operate autonomously for extended periods enhances productivity and scalability in large-scale agricultural operations. Additionally, there is a need for comprehensive training and education to enable farmers to effectively integrate these technologies into their existing practices while maximizing their benefits. modern agriculture, offering precision, efficiency, and sustainability in crop protection. As technology continues to evolve and adoption grows, collaboration among stakeholders is essential to address challenges and unlock the full potential of these innovative solutions for the benefit of farmers, consumers, and the environment.

Using artificial intelligence to improve poultry productivity – a review

Abstract A recent study investigated the potential applications of artificial intelligence (AI) in poultry farming. One area where AI can be helpful is in the early detection of diseases. By analyzing data from various sources, such as sensor readings and health records, AI algorithms can identify potential disease outbreaks or health risks in flocks, allowing farmers to take timely preventive measures. Another area where AI can be applied is in controlling the environmental conditions of farms. By analyzing data from sensors that monitor temperature, humidity, ventilation, and lighting conditions, AI algorithms can help farmers create a comfortable and healthy environment for birds, improving their growth and reducing their stress. AI can also optimize the management of healthcare supplies for poultry. By analyzing the nutritional requirements of birds and the availability and prices of different ingredients, AI algorithms can help farmers optimize feed formulations, reducing waste and environmental impacts. Finally, the study explored the use of robots in poultry care. Robots can be used for cleaning, feeding, and monitoring individual birds. By automating these tasks, farmers can reduce labor costs and improve the efficiency of their operations. Overall, the study highlights the potential benefits of using AI and robotics in poultry farming, including early disease detection, improved environmental conditions, optimized feed formulations, and increased automation.

Algorithms in the Margins: Organized Community Resistance to Port Automation in the Los Angeles Harbor Area

Public deliberations on artificial intelligence and machine learning (AI/ML) provoke strong interest in automation, or the perceived displacement of human labor due to general technological advances. Social science scholarship on well-compensated technology workers and a growing global underclass suggests automation remains largely a myth, with emerging technologies generating new forms of computational and emotional work. But little research has examined automation from the vantage point of community members at perceived greatest risk of displacement, such as those sustained by generations of logistics labor and physically located in racialized, underserved areas. In the United States and around the globe, logistics work is directly targeted by “automation engineers” to reduce labor costs on behalf of industry interests, posing a severe threat to port workers and their respective communities. This article examines organized community resistance to automation within the Los Angeles Harbor Area, drawing on four public hearings following the Port of LA’s planned integration of autonomous vehicles for the movement of cargo (i.e., automated straddle carriers). We find community mobilizations widely sought to enlarge public consideration of AI/ML to encompass matters of societal well-being and the collective good. These include shifting from the legality of AI integration to underscore public morality in light of anticipated harm, in addition to emphasizing the invisible social arrangements surrounding the Port and nearby community life. Our research reveals how lay people may reassert their own importance when confronted by technoscientific injustice, such as by reframing the future of work toward an insistence on “the future of our communities.”

Partially observable deep reinforcement learning for multi-agent strategy optimization of human-robot collaborative disassembly: A case of retired electric vehicle battery

The burgeoning electric vehicle (EV) industry has precipitated a commensurate surge in the consumption of EV batteries, which are currently labor-intensive and inefficient for the recycling and disassembly of EV batteries. However, it is a potential trend to enhance the efficacy and safety of the disassembly of EV batteries based on human-robot collaboration (HRC) method. Because of the uncertainty of retired EV battery disassembly and the inefficiency of the existing disassembling sequence, it is difficult to be fully accomplish through HRC disassembly. The collaborative disassembly of EV batteries by humans and robots can be conceptualized as agents engaging with and learning from the environment, and modeled as a multi-agent Markov game process. This paper aims to address the challenge of HRC in the disassembly of EV batteries by recognizing the dual attributes of partial observability and non-smoothness in the suitable disassembly scenario. A partially observable multi-agent reinforcement learning environment is constructed, incorporating the structural aspects of the EV battery and the disassembly task. The framework is extended to the QMIX-HRC algorithm on the QMIX architecture (as a value-based multi-agent deep reinforcement learning algorithm), specifically designed to tackle the sequence problem in human-robot collaborative disassembly of EV batteries. The optimization results would yield a task sequence to offer maximal global co-benefit during the exploration iteration, facilitating a reduction in labor costs and an enhancement of co-efficiency. The viability of the QMIX-HRC disassembly strategy would be verified through the eventual disassembly sequence of a simulated battery pack through a real human-robot collaborative disassembly station.

Formation of an Effective System of Currency Regulation and Control in Russia

In the course of the conducted research, specific problems of the system of currency regulation and control related to negative trends in the Russian foreign exchange market in modern realities have been identified. Based on the evolution of the global and domestic system of currency regulation and control, trends and priority directions of its development have been identifi ed. Measures have been formulated to improve the system of currency regulation and control and increase their effectiveness, which can be used to develop drafts of existing regulatory legal acts and to form the main directions of monetary policy in our country. The categorical apparatus of currency issues has been supplemented on the basis of a comprehensive review of the system of currency control and currency regulation from various points of view. The concepts of "currency control", "currency regulation" and "currency off ense" have been clarifi ed. The consolidation of the basic concepts at the legislative level,the introduction of a currency monitoring system will minimize the number of currency violations and reduce labor costs when working out violations. The study confirmed the need to develop alternative channels of work in connection with the shutdown of the SWIFT system and the promotion of integration processes within the EAEU, which will eliminate many economic risks and ensure Russia’s resistance to sanctions. The proposed innovations will help to develop foreign economic relations between the countries more efficiently, dynamically and qualitatively.

Cattle Body Intelligent Measurement based on Improved CenterNet

Traditional cattle body measurement methods have limitations such as manual contact measurement and low efficiency. To improve the efficiency of measuring cattle bodies and reduce labor costs, an improved intelligent measurement network based on CenterNet was proposed. In this study, we used the DenseNet-100 to replace the ResNet-101 to alleviate the vanishing gradient problem. We also improved the cattle body size measurement efficiency by reducing the network parameters. In addition, we conducted a comparative experiment on the cattle posture dataset collected by ourselves to verify the feasibility of the network. The experiment confirmed that the proposed intelligent measurement based on improved CenterNet outperforms the traditional networks and other advanced object measurement networks in accuracy and measurement efficiency, and can effectively solve most of the error problems caused by manual measurement. Besides, our network has good applicability and strong stability, which can meet the requirements of the evaluation of cattle body size measurement indicators.

Design of Yellow Peach Quality Identification System Based on PlC and Machine Vision

China is the main producer and exporter of canned fruit, leading the world inhe processing and consumption of canned fruits. Most of the fruit can processing lines are semi-automated, while the fruit quality is mostly identified by naked eye and thus needs to be automatically identified again before the sealing of the cans on the production line for prevention of inferior pulp from entering the canned bottles. Based on this, this paper designs a fruit quality identification system for PLC canned yellow peach production line based on machine vision taking canned yellow peaches as an example, and analyzes the difficulties in identifying the control of sub-packaging. Software programming and hardware design were carried out for the visual inspection of canned yellow peaches, and the simulation and implementation of the system were completed. This system reduces labor costs, improves the production environment, and provides favorable solutions to the construction of semi-automated production workshops that meet the needs of enterprise development. Its promotion is of significant value.

Evaluation of the safety and efficacy of the novel Mycoplasma gallisepticum vaccine, Vaxsafe MG304, after spray-vaccination of 1-day-old specific pathogen-free chicks

Mycoplasma gallisepticum causes chronic respiratory disease in poultry. A novel vaccine, Vaxsafe MG304 (the ts-304 strain), has greater protective efficacy in chickens than the Vaxsafe MG (strain ts-11) vaccine when delivered by eye drop at 3 weeks of age. Applying this vaccine in the hatchery to 1-day-old birds, using mass administration methods, would improve animal welfare and reduce labour costs associated with handling individual birds. This study assessed the protection provided by vaccination with Vaxsafe MG304 after administration to 1-day-old chicks. Chicks were administered a single dose of the vaccine to assess the efficacy of either a high dose (107.0 colour changing units, CCU) or a low dose (105.7 CCU) after eye drop or spray (in water or gel) administration against experimental challenge with virulent M. gallisepticum strain Ap3AS at 7 weeks of age. The vaccine was able to colonise the palatine cleft of chicks after vaccination by eye drop (at both doses) or by spray (in water or gel) (at the high dose). The high dose of vaccine, when delivered by eye drop or spray, was shown to be safe and induced a serological response and protective immunity (as measured by tracheal mucosal thickness and air sac lesion scores) against challenge. Vaccination of 1-day-old chicks with Vaxsafe MG304 by eye drop induced protective immunity equivalent to vaccination at 3 weeks of age. Vaxsafe MG304 was also protective when applied by both coarse- and gel spray methods at the higher dose and is therefore a suitable live attenuated vaccine for use in 1-day-old chicks.

Research on fault diagnosis of industrial robots based on generative adversarial network

Abstract-In today's industrial production, companies have to introduce various types of industrial robots in order to reduce labor costs, shorten product processing time, and obtain greater profits. This move has caused the installed capacity of industrial robots to increase year by year. However, this also makes industrial robots have more and more frequent failures and an increasing amount of failures in actual production. The occurrence of industrial robot failures is unavoidable, and the amount of data in the healthy state and the amount of data in the fault state are extremely unbalanced. Whether the fault diagnosis model is efficient and accurate is the key to providing reliable theoretical and technical support for the health management of industrial robots. Therefore, this paper constructs an imbalanced data fault diagnosis model based on Wasserstein Generative Adversarial Network (WGAN) for fault diagnosis of industrial robots in data balanced and unbalanced states. We evaluate the quality of generated data through the following three aspects: the change in loss values ​​of the discriminator and generator, the degree of overlap between the generated data and real data samples, and the classification accuracy of the classification algorithm. The results show that the data generated by the WGAN model is of high quality and can be used as extended data for the fault data set. Next, we used the deep residual network designed in this article to perform fault diagnosis on the balanced data set expanded with the generated data. The experimental results confirmed the high quality of the generated data and the outstanding expressive ability of the deep residual network. In order to measure the performance of the model in this paper, we used 7 types of generative adversarial networks to generate fault data. The quality of the generated data samples is still analyzed and evaluated through the three aspects mentioned above. The results show that only the conditional Wasserstein generative adversarial network model generates data of high quality, which can be used as extended data for fault data and has good results in actual applications.

Research Status and Development Trend of Obstacle Crossing Mechanism of HV Transmission Line Inspection Robot

With the rapid development of the power grid industry, the traditional manual inspection method is gradually being replaced by intelligent robot technology, which has been widely concerned around the world. The research of inspection robots aims to improve the efficiency and safety of transmission line inspection, while reducing labor costs and quickly identifying potential risks. The obstacle crossing mechanism of inspection robot is the core part of normal inspection of robot. This paper analyzes the obstacle crossing mechanism of inspection robot developed at home and abroad, summarizes the types and characteristics of the obstacle crossing mechanism, and finally puts forward the development trend of the obstacle crossing mechanism of inspection robot

Several Modes of Digitalisation of Value Chains and Their Effects on Firm Behaviour and Performance: Case Studies of Apparel and Video Game Industries

ABSTRACT This paper analyses the digitalisation of the global value chain (GVC) in the apparel and video game industries and its effects on firm behaviour and performance. It elaborates on several modes of digitalisation, namely, thin digitalisation, platform digitalisation and full-range digitalisation. Digitalisation contributes to the expansion of the volume of sales and profits while reducing labour costs. However, its impacts on profitability remain uncertain, with the benefits and costs offsetting each other. GVC digitalisation benefits startups by lowering entry costs and facilitating upgrading, whereas it may be detrimental to incumbent companies since it can enable platform providers to transform into competitors.

Optimal Artificial Neural Network-based Fabric Defect Detection and Classification

Automated Fabric Defect (FD) detection plays a crucial role in industrial automation within fabric production. Traditionally, the identification of FDs heavily relies on manual assessment, facilitating prompt repairs of minor defects. However, the efficiency of manual recognition diminishes significantly as labor working hours increase. Consequently, there is a pressing need to introduce an automated analysis method for FD recognition to reduce labor costs, minimize errors, and improve fabric quality. Many researchers have devised defect detection systems utilizing Machine Learning (ML) approaches, enabling swift, accurate, and efficient identification of defects. This study presents the Optimal Artificial Neural Network-based Fabric Defect Detection and Classification (OANN-FDDC) technique. The OANN-FDDC technique exploits handcrafted features with a parameter-tuning strategy for effectively detecting the FD process. To obtain this, the OANN-FDDC technique employs CLAHE and Bilateral Filtering (BF) model-based contrast augmentation and noise removal. Besides, the OANN-FDDC technique extracts shape, texture, and color features. For FD detection, the ANN method is utilized. To improve the detection results of the ANN method, the Root Mean Square Propagation (RMSProp) optimization technique is used for the parameter selection process. The simulation outputs of the OANN-FDDC technique were examined on an open fabric image database. The experimental results of the OANN-FDDC technique implied a better outcome than the 96.97% of other recent approaches.

Comparison Study Between Weight Training and Fartlek To Improve The Running Speed Of Trainee Teachers In The 100-Meter Event

The purpose of this study is to compare the effectiveness of weight training and Fartlek in improving the running speed of trainee teachers in the 100-meter event. The study aims to determine which training method is more effective in enhancing running speed. The study participants were randomly assigned to either weight training or fartlek groups and underwent their respective training programs for four weeks. The running speed of both groups was evaluated and compared using a standardized assessment tool. The results of this study will contribute to the existing knowledge on the benefits and limitations of weight training and Fartlek in improving running speed. The t-test showed that there is no significant difference between the two means among both training groups and its proven that both weight training and Fartlek training is suitable is improving the running speed of trainee teachers in 100-meter event. This study is expected to provide valuable information for coaches, trainers, and physical educators in developing effective training programs for athletes and physical education students. and reduce labor costs and water wastage and increase productivity and efficiency.

Automatic Counting Machine with IR-Sensor Based Counting Machine in Sorting Areas Using Conveyor Belt

Absolutely, the transition from manual counting to automated counting with the help of technology has been a significant advancement across various industries. This shift has brought about increased accuracy, efficiency, and convenience in monitoring and managing processes. The use of sensors, cameras, and other smart technologies has revolutionized how businesses handle tasks that require counting or tracking. In the context of factories and stores, automated counting systems offer several advantages. They eliminate the errors associated with manual counting, reduce labor costs, and provide real-time data that can be crucial for decision-making. These systems are not only faster but also more reliable, allowing businesses to streamline their operations and improve overall productivity. Moreover, the integration of these automated counting systems with digital displays or screens makes the information easily accessible to relevant personnel. This real-time visibility enhances monitoring and control, enabling quick responses to changes or issues in the production or sales processes. The use of such technology reflects the ongoing trend of leveraging automation to enhance various aspects of work, making processes more efficient and accurate. It's indeed a testament to how innovation has transformed everyday tasks and workflows, bringing about positive changes in different industries. Key Word: Arduino Uno, I2C module, IR sensor, LCD, Buzzer, Motor, Conveyor Belt.

Analysis of practical experience in the development and implementation of standard operating procedures in the activities of nursing medical staff on the base of the FSBHI MRC «Sergievsk mineral waters» FMBA of Russia

The use of innovative technologies in the daily practice of a nurse makes her work more professional, ensures safety and convenience when performing basic professional tasks, reduces labor costs, and allows for the implementation of the diagnostic and treatment process quickly and efficiently. Target. To analyze the experience of developing and implementing standard operating procedures for nurses for 2022–2023. Results. The successful development and further implementation of SOPs for various types of activities of nursing staff was completed; solved problems that arose during the development and implementation of SOPs by creating corrective actions with mutual analysis, taking into account the feedback of the team of specialists; the proposed technologies and standard documents can be used as a model by most medical organizations. Conclusion. In the Federal State Budgetary Healthcare Institution MRC «Sergievsk Mineral Waters» FMBA of Russia, as of January 2024, the following have been developed: 121 SOPs, 30 SOPs have been revised. In 2024, 4 SOPs were introduced in medical organizations and continuous work is underway to further develop them.

Fibre reinforced concrete under in-plane shear stresses

Steel fibres have a positive influence on the shear behaviour of concrete elements, such as beams and walls, the more so as the developments in fibre reinforced concrete (FRC) technology allow reaching increasingly higher residual post-cracking strengths. The partial, or total, replacement of shear reinforcement by steel fibres allows increasing the construction speed and reducing labour costs, potentially offering economic benefits in the context of industrialized fabrication. However, the combined behaviour of steel fibres with reinforcement in shear critical elements is still not fully understood hindering the development of consistent design procedures.In this study, a detailed mechanical model based on the Cracked Membrane Model using fixed and interlocked cracks (F-CMM) is presented, which is capable of describing the behaviour up to failure of concrete panels reinforced with both steel fibres and rebars (R-FRC) subjected to in-plane shear and axial forces. The model explicitly considers all the individual mechanical phenomena governing shear behaviour of R-FRC and can serve as the basis for the development of methodologies for shear safety verifications.The model is validated against the existing experimental results from R-FRC panels subjected to in-plane shear loading. A calculation example is used to discuss in detail the model outcomes and the impact of the spatial variability in the fibre distribution. A parametric study is developed to understand the effects of fibres on the shear strength and failure modes, and its interactions with both the reinforcements and crack shear stress transfer mechanics.

Drone-mounted remote-controlled arm for monitoring and precision spraying coconut rhinoceros beetle infestations

This paper presents a low-cost (locally-assembled) drone system with a sprayer arm and a remote control for monitoring and precisely applying pesticides to coconut trees infested by the Rhinoceros Beetle. A modified RGB IP camera continuously monitors potential coconut beetle infestation points, capturing real-time spray images, and displaying the results through a smartphone interface. The system provides real-time monitoring, allowing immediate remote control of the spray operation from the ground upon detecting beetle infestations. The drone-mounted sprayer arm is controlled in response to the ground-based signals, to accurately extend or contract while effectively dispensing spray chemicals onto the target areas at the upper canopy of coconut trees. In our tests using Nam-Hom coconut trees, which averaged a height of 10.4 m, the system operated at a speed of 1.25 km/h, achieving a spraying flow rate of 0.333 l/h and a working capacity of 0.352 ha/h. The controlled spraying setup emits a solid jet, ensuring precision and effective atomization of chemicals within a distance range of 1.0 to 4.0 m from the target. The spray quality attributes (droplet size, pattern, and coverage) were not significantly affected by the wind speed. The system could potentially reduce labor costs, chemical usage, and health risks associated with pesticide application.

Genetic study for seed germination and shattering in Euphorbia lagascae in response to different seed treatments

Euphorbia lagascae Spreng is a promising emerging oilseed crop, with its seed oil accounting for approximately 50% of the seed weight. Euphorbia oil contains a significant amount of vernolic acid, comprising two-thirds of its composition, which boasts various industrial applications, including acting as a stabilizer-plasticizer and natural dye. However, this species was known to have a high degree of seed-shattering and a low germination rate, which act as two important barriers to large-scale production and exploitation. Therefore, the objective of this study is to determine the genetic control of seed germination and seed-shattering traits in order to develop a reliable pipeline that would be applicable for industries and breeders to select superior E. lagascae lines and design a robust breeding scheme in a short time at reduced labor costs. For this objective, five different wild-type genotypes of E. lagascae that demonstrated high germination potential were crossed with an ethyl methanesulfonate (EMS) mutant genotype that produces non-shattering capsules. The F2 populations from two successful crosses (A and B) were separated into three different treated groups for seed germination evaluation and to study the segregation of 200 individuals per F2 population. The three treatments were: light, gibberellic acid (GA3), and control treatment. Consequently, plants treated with approximately 250 μmol/m2/s of light showed significant improvement in germination up to 75% in cross A and 82.4 % in cross B compared with the control plants and the group treated with 0.05% GA3. According to the chi-square test results, the inheritance pattern of seed germination in response to light treatment follows a 3:1 segregation ratio between germinated and non-germinated seeds, indicating a dominant gene action in the F2 generation. The same conclusion was followed for the shattering trait in the group treated with light, which was also simply inherited as a 3:1 ratio for shattering vs. non-shattering capsules. Our results emphasize the importance and significance of light treatment in producing uniform populations through acceptable germination and shattering resistance of the mutant genotypes of E. lagascae. This is the first report of light treatment that significantly improved seed germination of E. lagascae, which may enhance efforts in the development of this new industrial crop as a feedstock for vernolic acid production.

Impact of Drip Irrigation in Sugarcane Cultivation in Mandya District of Karnataka, India

This study investigates the impact of drip irrigation on sugarcane cultivation in Mandya district, Karnataka, considering the challenges posed by traditional flood irrigation methods and the increasing demand for water resources. The research compares the adoption of drip and flood irrigation, analyzing socio-economic characteristics, cropping patterns, and the comparative economics of sugarcane cultivation under both methods. Externality concept and Partial budgeting technique was emphasized to estimate the additional benefit derived from drip irrigation over flood irrigation. The study reveals that drip irrigation leads to higher water use efficiency, improved crop quality, and environmental sustainability. The adoption of drip irrigation in sugarcane cultivation results in significant economic benefits, with higher net returns and increased yields then farmers following flood irrigation. The findings highlight the positive externality associated with drip irrigation, contributing to water conservation, reduced labor costs, and increased agricultural sustainability with an additional benefit of around Rs. 41,000 derived due to drip adoption over flood irrigation in Sugarcane cultivation. As Karnataka faces evolving challenges in agriculture, the widespread adoption of drip irrigation emerges as a pivotal factor in ensuring the prosperity of sugarcane cultivation and addressing environmental concerns.

An Apple Detection and Localization Method for Automated Harvesting under Adverse Light Conditions

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.