Reduce Labor Costs Research Articles

Identify and segment microalgae in complex backgrounds with improved YOLO

Microalgae are widely distributed in the ocean, and some species are prone to causing harmful algal blooms that threaten the marine ecological environment. At present, microscopy is the most common method for microalgae analysis, and the combination of computer vision and microscopy is the mainstream trend in algae morphology classification. However, most methods only focus on the species and quantities of algae, without obtaining contour information that can further analyze their survival status and biomass. Therefore, this article proposes a convolutional neural network (AlgaeSeg-YOLO), which can recognize the species, quantities, and contours of algae. Firstly, a dataset of microalgae microscopic images in microfluidic chips was constructed, which includes a total of 2799 annotated images of 6 harmful microalgae, including 3916 instances. Secondly, the AlgaeSeg-YOLO was constructed with stronger feature fusion and pixel-level spatial information modeling capabilities based on YOLOv8n-seg. Finally, compared to other common methods, the experimental results show that the mAP (mean Average Precision) of AlgaeSeg-YOLO reaches 95.61 %, which is 1.64 %, 1.76 %, 5.28 %, 3.34 %, and 5.39 % higher than YOLOv8n-seg, YOLOv5n-seg, Mask R-CNN, Cascade Mask R-CNN, and SOLOv2, respectively, achieving real-time and accurate segmentation of microalgae in complex backgrounds. Meanwhile, the parameters and computation remain relatively low. This work helps to achieve fully automated analysis of microalgae, reduce labor costs, and provide technical support for long-term monitoring of the ecological environment and further research.

Development of universal working bodies for cultivators in inter-row processing of cotton

Abstract The development of multifunctional universal working bodies that can perform various functions with quick and uncomplicated adjustment, low energy and material consumption, and ensure high-quality processing of row spacing throughout the entire growing season of plants is in high demand. The project documents were developed for mastering the production of a cultivator with a universal working body and were introduced into the design process at BMKB-Agromash JSC. As a result, a universal working body was developed and installed on the beam of the KXU-4B cultivator, featuring quick and easy adjustment and low metal consumption. The use of a cotton cultivator equipped with universal working bodies in inter-row processing of cotton is expected to reduce labor costs by 16.0%, exploitation costs by 15.56%, and metal consumption of the aggregate by 1.23 times, as well as fuel consumption per hectare of cultivated field by 11.26%. To achieve this, a universal working body was designed that performs all technological operations typically performed by multiple working bodies mounted on existing cultivators without compromising quality. The universal working body is equipped with a wide right and left blade, allowing for the cultivation of the middle of the cotton row, slopes, and weed cutting. The result is a constructive scheme of a universal working body, where working parts are concentrated in one column, enabling the performance of technological operations during different vegetative seasons.

The Influence of Automated Harvest Robots in Japan on the Interest of TRPL IPB University Students in Improving the Welfare of Agriculture

This study investigates the impact of Automated Harvesting Robots on agriculture in Japan, examines the potential influence of TRPL IPB students' interest in these technologies on agricultural innovation in Indonesia, and explores the reactions of these students to the functionality of the robots. Automated Harvesting Robots have significantly transformed Japanese agriculture by improving efficiency, reducing labor costs, and addressing labor shortages. The interest of TRPL IPB students in these advanced tools indicates a promising avenue for fostering agricultural innovation in Indonesia, leveraging technological advancements to enhance local practices. Furthermore, the responses of these students to the functionality of Automated Harvesting Robots provide valuable insights into their perceptions and readiness to adopt such technologies. This study highlights the critical role of technological exchange and student engagement in advancing agricultural practices and innovation across regions.

Optimal Planting Time for Summer Tomatoes (Lycopersicon esculentum Mill.) Cropping in Korea: Growth, Yield, and Photosynthetic Efficiency in a Semi-Closed Greenhouse.

In Korea, greenhouses are traditionally used for crop cultivation in the winter. However, due to diverse consumer demands, climate change, and advancements in agricultural technology, more farms are aiming for year-round production. Nonetheless, summer cropping poses challenges such as high temperatures, humidity from the monsoon season, and low light conditions, which make it difficult to grow crops. Therefore, this study aimed to determine the best planting time for summer tomato cultivation in a Korean semi-closed greenhouse that can be both air-conditioned and heated. The experiment was conducted in the Advanced Digital Greenhouse, built by the National Institute of Agricultural Sciences. The tomato seedlings were planted in April, May, and June 2022. Growth parameters such as stem diameter, flowering position, stem growth rate, and leaf shape index were measured, and harvesting was carried out once or twice weekly per treatment from 65 days to 265 days after planting. The light use efficiency and yield per unit area at each planting time was measured. Tomatoes planted in April showed a maximum of 42.9% higher light use efficiency for fruit production and a maximum of 33.3% higher yield. Furthermore, the growth form of the crops was closest to the reproductive growth type. Therefore, among April, May, and June, April is considered the most suitable planting time for summer cultivation, which is expected to contribute to reducing labor costs due to decreased workload and increasing farm income through increased yields. Future research should explore optimizing greenhouse microclimates and developing crop varieties tailored for summer cultivation to further enhance productivity and sustainability in year-round agricultural practices.

IoT Applications in Agriculture: Enhancing Crop Yield in Vietnam

Purpose: To aim of the study was to analyze the IoT applications in agriculture: enhancing crop yield in Vietnam Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: IoT applications in agriculture have significantly enhanced crop yield in Vietnam. Through sensors and IoT devices, farmers monitor soil conditions like moisture, temperature, and humidity in real-time. This data-driven approach optimizes irrigation and fertilization, minimizing waste and maximizing resource efficiency. Studies show that smart farming techniques using IoT lead to higher crop yields by ensuring optimal growth conditions and timely pest and disease management. Remote monitoring also improves farm management, reducing labor costs and promoting sustainable practices through precise resource management. Unique Contribution to Theory, Practice and Policy: Technology acceptance model (TAM) & diffusion of innovations theory& resource-based view (RBV) may be used to anchor future studies on IoT applications in agriculture: enhancing crop yield in Vietnam. Encourage the adaptation and customization of IoT solutions to suit local farming practices and environmental conditions across different regions of Vietnam. Implement policies that incentivize the adoption of IoT technologies in agriculture, particularly among smallholder farmers.

Robotics Integration in Manufacturing: Case Study of South Korea

Purpose: Aim of the study was to analyze the robotics integration in manufacturing: case study of South Korea. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Robotics integration in South Korean manufacturing has revolutionized industry, boosting efficiency, productivity, and global competitiveness. Key sectors like automotive and electronics have seen significant benefits, including reduced labor costs, enhanced product quality, and faster production cycles. This advancement has also spurred innovation in robotics technology, making South Korea a leader in this field globally. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, technology acceptance model (TAM) & network externalities theory may be used to anchor future studies on robotics integration in manufacturing: case study of South Korea. Develop practical guidelines for integrating robotics into diverse manufacturing sectors. South Korea's experience underscores the importance of sector-specific customization and scalability in robot deployment. Establish regulatory frameworks that balance innovation with safety and ethical considerations in robotic manufacturing. South Korea's proactive regulatory policies support industry standards for robot safety and data privacy, fostering a conducive environment for technological innovation.

YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection

Deploying deep convolutional neural networks on agricultural devices with limited resources is challenging due to their large number of parameters. Existing lightweight networks can alleviate this problem but suffer from low performance. To this end, we propose a novel lightweight network named YOLOv8-RCAA (YOLOv8-RepVGG-CBAM-Anchorfree-ATSS), aiming to locate and detect tea leaf diseases with high accuracy and performance. Specifically, we employ RepVGG to replace CSPDarkNet63 to enhance feature extraction capability and inference efficiency. Then, we introduce CBAM attention to FPN and PAN in the neck layer to enhance the model perception of channel and spatial features. Additionally, an anchor-based detection head is replaced by an anchor-free head to further accelerate inference. Finally, we adopt the ATSS algorithm to adapt the allocating strategy of positive and negative samples during training to further enhance performance. Extensive experiments show that our model achieves precision, recall, F1 score, and mAP of 98.23%, 85.34%, 91.33%, and 98.14%, outperforming the traditional models by 4.22~6.61%, 2.89~4.65%, 3.48~5.52%, and 4.64~8.04%, respectively. Moreover, this model has a near-real-time inference speed, which provides technical support for deploying on agriculture devices. This study can reduce labor costs associated with the detection and prevention of tea leaf diseases. Additionally, it is expected to promote the integration of rapid disease detection into agricultural machinery in the future, thereby advancing the implementation of AI in agriculture.

Feeding the future: 3D food printing as a strategic response to labor scarcity

PurposeThis paper aims to address the critical labor shortage in the food industry by exploring the potential of 3D food printing technology as a strategic solution. The study investigates how 3D food printing can enhance productivity, reduce labor costs, and offer innovative applications in various sectors of the food industry.Design/methodology/approachThe research employs a comprehensive review of existing literature and case studies to analyze the current state of labor scarcity in the food industry and the technological advancements in 3D food printing. The paper also assesses the technical, operational, regulatory, and ethical challenges of 3D food printing and provides strategic recommendations for stakeholders.Findings3D food printing technology presents a viable solution by automating labor-intensive tasks, enhancing labor efficiency, and enabling customized food production. The technology’s potential benefits include improved productivity, reduced operational costs, and the ability to meet personalized nutritional needs. However, the adoption of 3D food printing faces challenges such as high initial costs, maintenance requirements, and scalability issues. Additionally, regulatory and consumer acceptance hurdles need to be addressed.Practical implicationsPolicymakers are encouraged to support the development and adoption of 3D food printing through funding and clear regulatory frameworks. Business leaders should consider strategic investments in 3D printing technology and training programs to harness its benefits. Technology developers must focus on advancing the capabilities and user-friendliness of 3D food printers. Addressing these aspects can help the food industry overcome labor scarcity and achieve long-term sustainability and efficiency.Originality/valueThis paper provides a comprehensive analysis of 3D food printing technology as a strategic response to labor scarcity in the food industry. It contributes to the existing body of knowledge by highlighting the potential of 3D food printing to revolutionize food production and offering practical recommendations for its adoption and integration.

Industrial Applications of Artificial Intelligence Technologies and Their Socio-economic Impacts

This paper outlines the development of Artificial Intelligence (AI) technology and its contribution to the Industrial Revolution. From the First Industrial Revolution to the Fourth Industrial Revolution, technological innovations have continuously promoted productivity and social progress. In particular, the fourth industrial revolution, represented by AI technology, has penetrated into various industries and fields with its wide range of applications, significantly improving production efficiency and quality. In the manufacturing industry, AI technology realizes automated production and intelligent quality inspection, reducing labor costs and improving product quality. In the field of agriculture, AI technology has effectively improved the yield and quality of crops through drone spraying and intelligent irrigation. In addition, AI technology also plays an important role in social governance, improving the efficiency and level of social management.

Digital transformation as a factor in reducing labor shortages in Russian agriculture

This article examines the dynamics of the number of employees of agricultural organizations, the dynamics of energy availability, electrical equipment, the level of mechanization and technical equipment as key conditions for the introduction of digital solutions and the reduction of labor shortages in the industry. In addition, an analysis of the dynamics of labor costs in the sectoral and product context was carried out, which allows us to identify the most labor-intensive and requiring the introduction of digital solutions in the agricultural sub-sector. The key digital technologies that contribute to reducing the shortage of labor resources have been studied. A factorial regression model is proposed that allows us to study the impact of digital transformation on reducing the labor costs of producing a unit of production. The results of the study allowed us to propose an approach to assessing the impact of the digital transformation of agriculture on labor costs and reducing the shortage of labor resources of an industry or an individual enterprise (group of enterprises), consisting in analyzing the dynamics of the need for workers, the conditions of technical equipment of production, labor costs and approximation based on a regression model of the impact of digital transformation technologies on labor cost reduction.

OMC-YOLO: A Lightweight Grading Detection Method for Oyster Mushrooms

In this paper, a lightweight model—OMC-YOLO, improved based on YOLOv8n—is proposed for the automated detection and grading of oyster mushrooms. Aiming at the problems of low efficiency, high costs, and the difficult quality assurance of manual operations in traditional oyster mushroom cultivation, OMC-YOLO was improved based on the YOLOv8n model. Specifically, the model introduces deeply separable convolution (DWConv) into the backbone network, integrates the large separated convolution kernel attention mechanism (LSKA) and Slim-Neck structure into the Neck part, and adopts the DIoU loss function for optimization. The experimental results show that on the oyster mushroom dataset, the OMC-YOLO model had a higher detection effect compared to mainstream target detection models such as Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5n, YOLOv6, YOLOv7-tiny, YOLOv8n, YOLOv9-gelan, YOLOv10n, etc., and that the mAP50 value reached 94.95%, which is an improvement of 2.62%. The number of parameters and the computational amount were also reduced by 26%. The model provides technical support for the automatic detection of oyster mushroom grades, which helps in realizing quality control and reducing labor costs and has positive significance for the construction of smart agriculture.

Design and Simulation of Intra-Row Obstacle Avoidance Shovel-Type Weeding Machine in Orchard

This paper presents the design of an intra-row obstacle avoidance shovel-type weeding machine. Theoretical analysis of intra-row weeding components guided the determination of the structures and parameters for key parts, including the signal acquisition mechanism, automatic obstacle avoidance mechanism, and weeding shovel. Furthermore, a hydraulic system was designed to support these functions. The design aims to optimize intra-row weeding operations, reduce labor costs, enhance weed control effectiveness, and prevent collisions between weeding equipment and grapevines. Through the construction of a mathematical model, the analysis determined the necessary minimum return speed of the hydraulic cylinder for the intra-row weeding shovel to avoid grapevines. We also established a reasonable range for the extension speed of the hydraulic cylinder to minimize areas missed during weeding. Further analysis showed that using the minimum return speed of the hydraulic cylinder effectively reduced missed weeding areas. A virtual prototype model of the weeding machine was created in ADAMS. Using the coverage rate of weeding operation as the evaluation index, single-factor simulation tests determined that the extension speed of the piston rod in the obstacle avoidance hydraulic cylinder and the forward speed of the weeding machine are the main influencing factors. The preset threshold of the control system, which triggered the automatic obstacle avoidance mechanism when the obstacle avoidance rod reached a specific angle (the “Angle Threshold”), was identified as a secondary influencing factor. Other factors were considered irrelevant. Hydraulic cylinder extension speed, weeding machine forward speed, and angle threshold were chosen as the influencing factors. Following the principles of a Box–Behnken experimental design, a quadratic regression combination experiment was designed using a three-factor, three-level response surface analysis method. The evaluation criterion focused on the coverage rate of weeding operation. A regression model was developed to determine the coverage rate of the weeding operation, identifying the optimal parameters as follows: obstacle avoidance hydraulic cylinder extension speed of 120 mm/s, forward speed of the weeding machine at 0.6 m/s, and an angle threshold of 18°. The optimized coverage rate of the weeding operation achieved 86.1%. This study serves as a reference for further optimization of intra-row weeding machines in vineyards and for other crops.

A Comprehensive Review on Effects of Material Composition, Mix Design, and Mixing Regimes on Rheology of 3D-Printed Geopolymer Concrete

Recent years have witnessed a significant growth in the research and development of additive manufacturing methods involving concrete and cementitious materials, with technologies like three-dimensional (3D) printing becoming more widely used in the construction industry. Construction has the possibility to be revolutionized, not only in the context of cost savings but also in the context of increased sustainability and functionality. 3D printing of concrete is a cutting-edge technology that has the potential to speed up construction, reduce labor costs, give architects more creative freedom, improve precision, obviate requirements for formwork, and result in less construction wastes. In addition, 3D printing can be a long-term solution for both economy and environment. Even though 3D printing in concrete has made tremendous strides recently, developing an effective 3D-printable material that decreases material usage and enhances performance is critical for carbon dioxide reduction. Robust geopolymer formulations for 3D printing concrete technology in current construction applications have emerged as the subject of much research among scientists to find novel ways to circumvent this constraint. This study intends to highlight the current state of the art in developing 3D-Printed Geopolymer Concrete (3DPGC) with a comprehensive review related to the material composition, mix design, and mixing regimes on rheology of 3DPGC. The rheology of 3DPGC in terms of printability and buildability is discussed. The mixing regimes employed for the preparation of one-part and two-part 3DPGC are tabulated and commented on. Lastly, the research gaps are identified and summarized, and several research directions are also provided for future investigations to expedite the ubiquitous use of 3DPGC in versatile construction applications.

AUTOMATED COMPLEX FOR MEASURING THE WINDING TEMPERATURE OF TRACTION ELECTRIC MACHINES DURING THEIR HEATING TEST

Purpose. The development and implementation of automated complexes for testing traction electric machines of rolling stock at the current stage is an extremely important task. Automated test complexes provide an opportunity to significantly increase the quality and efficiency of tests on traction electric machines. Thanks to the use of advanced technologies, these complexes ensure accurate data collection and analysis during tests. One of the main advantages of automated complexes is the reduction of cost and labor costs for conducting tests. Instead of manual intervention by operators, automation systems can perform many tasks automatically, saving time and reducing labor costs. Methodology. The methodological basis of the research is analytical research methods based on the compilation of a calculation system of differential equations based on the accepted test scheme, as well as the development of an algorithm for conducting tests on a designed stand. Findings. The proposed test complex allows to conduct tests of collector traction electric machines, to determine the excess temperature of the windings of electric machines without the need to stop them. The functional scheme of the measuring complex allows you to automatically record the parameters of the tested electric machines, and if necessary, it is possible to output the test results to a computer with the subsequent formation of the test protocol in electronic form. The accuracy of the determination of the measured parameters is ensured by the accuracy of the measuring devices and the algorithm of the measuring complex. Originality. The proposed automated measuring complex is a big step in the direction of improving the efficiency and reliability of heating tests of traction electric machines. One of its key advantages is the ability to perform tests without stopping the machine, which significantly reduces the time and costs of conducting tests. Automatic registration of measured parameters allows to record several controlled indicators at the same time, which provides a more complete and objective assessment of the state of the electric machine during testing. This complex not only facilitates the testing process and reduces the cost of time and resources, but also increases the reliability of the obtained results, which is important for manufacturers and operators of transport systems. In general, the automated measuring complex opens up new opportunities for increasing the efficiency and reliability of testing electric traction machines, contributing to the further development of transport technologies. Practical value. The implementation of the obtained results by enterprises for the repair of traction electric machines of rolling stock will allow to reduce the material costs of carrying out repairs while simultaneously improving the quality of the conducted tests.

Application of digital entertainment experience based on intelligent interactive system in personalized fitness training system

The application of digital media in fitness environments can improve the entertainment of the fitness process, allowing users to watch entertainment videos while engaging in fitness activities. At the same time, we can add relevant game elements to the fitness process, such as playing tennis through gesture movements, thereby increasing user interest in fitness participation. Therefore, this article has developed a personalized fitness training system using optical sensors and intelligent algorithms, and conducted testing experiments on blood oxygen saturation under two sports states: running and cycling. This article analyzes the intelligent optimization algorithm for optical signals. This algorithm has strong adaptability and intelligence level, and can directly learn and adjust the model based on the characteristics of the data. The automation performance of the intelligent algorithm can effectively reduce labor costs and improve efficiency. Finally, this article provides a basic analysis and testing of the personalized fitness training system. Through the obtained data, personalized guidance is provided to users, helping people make fitness more scientific and intelligent, and improving fitness efficiency.

Innovative structure to mount demand feeders to concrete raceways at a production fish hatchery

AbstractObjectiveAs a type of automatic feeder, demand feeders reduce labor costs and can improve fish‐rearing efficiencies. However, mounting demand feeders can be problematic because of the variety of nonstandardized fish‐rearing units. This article describes an innovative, simple, sturdy, and durable demand feeder mount for rectangular, concrete raceways.MethodsThe feeder mount was made from aluminum and sits flat on a raceway wall or walkway. A horizontal base plate, secured to the wall by vertical plates, holds a vertical riser of aluminum tubing. Attached to this tubing is a rod that secures the demand feeder.ResultThis feeder mount design has proven durable over several years of actual use. Over this time, it has needed no maintenance other than occasional cleaning with water to remove spilled fish food. The feeder can be easily and quickly removed from the mount by simply pulling a pin and sliding the feeder off of the tubing. The mount is constructed to dramatically reduce tripping hazards and occupational safety and health risks.ConclusionThis simple, inexpensive (~US$50 to construct), and no‐maintenance mount is a viable solution to the typically difficult problem of mounting demand feeders to concrete raceways used for trout production.

Digital-Twin-Assisted Skill Learning for 3C Assembly Tasks.

The utilization of robots in computer, communication, and consumer electronics (3C) assembly has the potential to significantly reduce labor costs and enhance assembly efficiency. However, many typical scenarios in 3C assembly, such as the assembly of flexible printed circuits (FPCs), involve complex manipulations with long-horizon steps and high-precision requirements that cannot be effectively accomplished through manual programming or conventional skill-learning methods. To address this challenge, this article proposes a learning-based framework for the acquisition of complex 3C assembly skills assisted by a multimodal digital-twin environment. First, we construct a fully equivalent digital-twin environment based on the real-world counterpart, equipped with visual, tactile force, and proprioception information, and then collect multimodal demonstration data using virtual reality (VR) devices. Next, we construct a skill knowledge base through multimodal skill parsing of demonstration data, resulting in primitive policy sequences for achieving 3C assembly tasks. Finally, we train primitive policies via a combination of curriculum learning, residual reinforcement learning, and domain randomization methods and transfer the learned skill from the digital-twin environment to the real-world environment. The experiments are conducted to verify the effectiveness of our proposed method.

Modelling the economy-wide effects of unilateral CO2 pricing under different revenue recycling schemes in Austria – Searching for a triple dividend

In this paper we identify policy implications for implementing carbon pricing in Austria, taking into account model structure uncertainty. Methodologically, we compare the results of two macroeconomic models, DYNK (a New Keynesian model) and WEGDYN-AT (a neoclassical general equilibrium model), to evaluate the effects of carbon pricing under different revenue recycling options. Specifically, we examine the model results with respect to their findings regarding a potential triple dividend, that we define as a simultaneous materialization of i) a reduction in CO2 emissions, ii) positive effects on GDP and employment, and iii) positive and distributionally progressive effects in household consumption possibilities. Accounting for model structure uncertainty improves the robustness of our results; at least within the macroeconomic frameworks applied here. Our results confirm the tradeoff between equity and efficiency identified in previous analyses. In the New Keynesian model, but not in the general equilibrium model, we find evidence for a triple dividend for a combination of the recycling options of non-wage labor cost reductions and lump-sum per-capita transfers (“climate bonus payments”).

Determination of the impact of innovative technologies in the system of strategic management of corporate social responsibility

The object of research is the process of using innovative technologies in the system of strategic management of social responsibility of enterprises. The main problem solved during the research was the need for a more effective integration of the principles of social responsibility in the business strategy, which will help enterprises to adapt to the global challenges and demands of modern society. The study of new technological approaches in this area allowed to identify the most effective tools for increasing transparency, responsibility and sustainable development of companies. As a result of the conducted research, the content of the concept «innovative technologies in the system of strategic management of social responsibility of enterprises» was clarified, which is proposed to be understood as a modern approach to the integration of technological innovations into strategic management, aimed at improving the social indicators of the enterprise. These technologies are found to include a wide range of tools such as Big Data, Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and others that help businesses effectively implement and monitor social programs and initiatives. It is substantiated that the use of innovative digital technologies in the system of strategic management of social responsibility of enterprises opens wide opportunities and generates significant advantages. Namely, increasing transparency and openness; optimization of resources and costs; increasing internal efficiency; strengthening of reputation and brand; automation of enterprise processes; innovativeness and competitive advantages; stimulating innovation. A model for determining the relationship between the level of profitability of enterprises by type: large, medium, and small enterprises and by the number of employees and their labor costs is proposed, which allows to establish the required number of employees for each type of enterprise. This enables the enterprise to function effectively, reducing labor costs, but not reducing the positive impact on supporting the social sector of the population, creating jobs and remaining a socially responsible enterprise. The possibility of using the latest digital technologies in the social responsibility management of enterprises is summarized.

Design and Performance Evaluation of a Cashew Apple (Anacardium occidentale L.) Slicing Machine

One challenge in cashew apple (CA) production is the absence of processing equipment, leading to manual operations. Cashew apple processors often resort to manual slicing, resulting in uneven thickness and increased manpower requirements. This study aimed to address these issues by developing a slicing machine that ensures uniform thickness and reduces the need for manual labor during the slicing process. The machine underwent evaluation at three different slicing speeds (28, 42, and 60 strokes per minute [spm]). The most efficient speed was found to be 60 spm, with a slicing capacity of 249.26 kg/h and a slicing efficiency of 86.66%. The corresponding rates for damaged sliced CAs and juice extraction during slicing were 4.93 and 22.33%, respectively. In comparison, manual slicing demonstrated a capacity of 32.21 kg/h and a slicing efficiency of 29.99%. The machine’s capacity was almost eight times higher than that of manual slicing. The optimal slicing speed for CAs was determined to be 60 spm, which significantly reduced losses in terms of thickness, extraction, and physical damage of CA during the slicing process. Using the machine not only minimized the time required for slicing but also reduced labor costs, making it a more efficient and economically viable option for CA processing.