Agricultural Equipment Research Articles

Performance test and economic analysis of paddy direct seed planter modified pulled by tractor (amator) in tidal swamp, South Sumatra

Abstract Lack of labor is another obstacle to the characteristics of the tidal swamp land itself so that the use of agricultural equipment and machinery such as Amator can be used as solution. This paper aims to analyze the use of the amator in terms of technical and economic aspects of the equipment so that it is known whether it is efficient and profitable for farmers. Data collection was carried out through observation of equipment performance tests in the field and key informant interviews. This research was carried out in Sri Katon Village, Air Salek District, Banyuasin Regency. The data observed are technical performance including work capacity and work efficiency, as well as economic analysis in the form of basic costs and break-even point of using equipment. The results of the performance test show that the amator has a high efficiency (87.52%) with an effective working capacity value of 0.6244 ha hr−1 (1.6012 hr ha−1). The basic cost incurred for the use of the amator is IDR 193.145 ha−1 with a BEP of 16,8 ha yr−1. The use of amator with seeds only 35 kg ha−1 can save planting costs of IDR 200,000 ha−1 compared to the farmer practice.

EquipFarm: Farmer's Apartment Internet Site

Abstract: A farmer-focused rental software called Equip Farm was created to make agricultural machinery and equipment more accessible. By linking farmers and equipment owners, the platform facilitates the effective use of resources and raises farming operations' output. Farmers may browse a variety of machinery options, compare rental costs, and conveniently reserve the equipment they require for particular chores like planting, harvesting, or plowing with Equip Farm. The Equip Farm abstract emphasizes the organization's commitment to helping smallholder farmers overcome the obstacles of equipment affordability and accessibility, which will ultimately lead to the development of sustainable agricultural methods.

Structural Optimization of 4-DOF Agricultural Robot Arm

The shortage of human labor is increasing; thus, more agricultural machinery and equipment are expected to enter the agricultural sector. One of the agricultural machinery widely studied nowadays involves robot arms. Therefore, developing robot arms is a hot issue in this field. The ideal structure of the robot arm with optimal length is currently gaining popularity and being used in many sectors, such as manufacturing and agriculture. This is closely related to the dynamic structure of agricultural areas. Therefore, this study uses the forward kinematic modeling method to design an optimal robot arm to achieve a specific coordinate in a dynamic environment. The robot in this study arm mimics the boom and arm installed on a tractor. The forward kinematic problem in this study is defined using the Denavit-Hartenberg (DH) convention method. The DH convention is commonly used to solve kinematic analysis problems of a robot arm. Simulation of kinematic modeling is performed using MATLAB software. This study studies various optimization algorithms to compare the performance of algorithms that can achieve the optimal length with minimum errors. The comparison between artificial bee colony (ABC) and particle swarm optimization (PSO) is studied. At the end of the study, the best algorithm was selected for the robot arm design with a four-degree-of-freedom (4-DOF). The best algorithm, i.e., the PSO algorithm, is evaluated by calculating mean square error (MSE of 0.00108527), root mean square error (RMSE of 0.01678), mean absolute error (MAE of 0.004286081), and end-effector position error (error of 0.080557045), where the best algorithm has the lowest value of error.

Application of the FMECA method on the electro-hydraulic system for drilling machine type NKH45

This article focuses on the implementation of a preventive maintenance strategy, responding to a major challenge: the search for optimization in minimizing downtime of the radial drilling machine (NKH45). Crucially positioned at the heart of the mechanical manufacturing process, this machine holds significant influence on overall production performance and availability. In consideration of the strategic importance of this equipment within the framework of the mechanical manufacturing activity, an advanced methodology, in particular FMECA (Failure Mode Effects and Criticality Analysis), is implemented. FMECA represents one of the most recommended approaches in this context, based on reliability, allowing the meticulous identification of the main causes of functional failures of equipment. FMECA is a risk prevention method. It is used to improve the reliability of a product, a process or even a means of production. It makes it possible to assess the criticality of potential failures in a system. According to the recommendations of the maintenance department of the company specializing in the construction of agricultural equipment, this study will focus on a subsystem of the electro-hydraulic part of the NKH45 radial drilling machine, we use the FMEA Analysis method (Failure Mode Effects and Criticality Analysis). Thanks to this approach, it is possible to identify the main reasons for equipment breakdowns that affect production and to classify these breakdowns in order to develop an optimal maintenance strategy aimed at: reducing the number of breakdowns, preventing breakdowns, improving the preventive maintenance, reduce downtime, improve corrective maintenance, therefore the ultimate objective of this approach is to design an optimal maintenance action plan in order to significantly minimize downtime, which slows down production flow.

Farmer typology and drivers of agricultural mechanization use in Haiti

Agricultural mechanization is recognized as an important technology to increase agricultural productivity, face labor shortages, and reduce post-harvest loss. However, variations among farms’ characteristics and agricultural production systems suggest adopting a targeted strategy in mechanization programs for farmers. This research aimed to answer the following questions in the particular case of Haiti: are there different types of smallholder farmers in terms of mechanization use and socio-economic characteristics? What types of mechanization are used by farmers, and what drives their use among different types of farmers? What are the different types of farms in terms of mechanization use? We used typology construction methodology (principal component analysis (PCA) and hierarchical cluster analysis (HCA)) for a sample of 637 farmers and have identified four different clusters of farmers according to the characteristics of the farms they managed: “Little rain-fed farms” (cluster 1), “Little lowlands farms” (cluster 2), “Medium-sized farms in irrigated plains” (cluster 3), and “Large fragmented mountain farms”. Farms in cluster 3 were those who used more agricultural mechanization, and the results of multinomial logistic regression (MNLR) model revealed that the significant drivers of this use were location, access to credit and low food security status. Mechanization use of farms in clusters 1 and 4 was distinctively driven by saving behavior and off-farm income, respectively. In the pooled sample, the drivers of mechanization were: regions or location, age of the farmers, irrigation, livestock, access to credit, off-farm income and food security status. This study contributes to the literature by testing new drivers of agricultural mechanization such as food security status, and off-farm income. The findings can be used to design appropriate mechanization strategies to increase productivity and face labor price/scarcity challenges. They suggest that mechanization policies should focus on agricultural equipment that are adapted to the specificities of the production systems of each farm type, and strengthen access to credit. Otherwise, mechanization will be predominantly used only in irrigated lowlands.

Development of modern diagnostic methods automotive equipment

The ways of achieving the results of various diagnostic methods at diff erent stages of technology development are considered — from the level of the fi rst tractors and combines to modern unmanned complexes. To achieve this goal, the following tasks were solved: the design features and methods for determining the technical condition were considered; a practical analysis of diagnostic methods at diff erent levels was presented using the example of agricultural and truck equipment. It is proved that, despite the availability of all modern technologies, techniques and tools for determining the technical condition, specialists need to resort to various methods, including organoleptic examination, which implies the use of only accumulated experience.

Exploring the Socioeconomic Dynamics and Technological Adaptation among Farmers: Implications for Sustainable Agricultural Development and Rural Livelihoods in Jaipur, India

The research was conducted in Jaipur district of Rajasthan. There are 23 AAO departments under the jurisdiction of Deputy Commissioner of Agriculture, Jothwara (Jaipur). Among these, 4 AAO departments viz. Hingonia, Jobner, Boraj and Bichun were deliberately chosen for this study because the AAO circle is next to SKNAU, Jobner is within a radius of 20 km, researchers from the school respectively many people are making technological improvements in nearby villages and farmers are also active. A total of 120 participants were selected from these four AAO circles. Judging from the entire health history of farmers benefiting from Nongtang, the majority of farmers benefiting from Nongtang are middle-aged (35 to 51 years old) (58.33%). Primary school graduates (20.00%), farmers are union members (37.50%), their share of average annual income (77.50%) is Rs. 1,26,781 to Rs. 4,72,303 and farmers share small land. category (38.33%). Moreover, most of the beneficiary farmers (44.17%) have moderate farming experience ranging from 15 to 31 years, (23.19%) farmers use PM Kisan application, (70.00%) farmers use information technology, (68%) 34) consider agriculture as the main business. job. The relationship between income and technological change shows that there is a positive relationship between education level, social participation, annual income, insured land, agriculture, agricultural practice and knowledge. The beneficiary's social profile indicates education, community involvement, annual income, land ownership, farm practice use, and data-effective and relevant job creation and annual income. The correlation between the profile of the beneficiaries and their social variables shows a positive relationship between the level of education, social participation, and annual income. There is a significant relationship between the use of agricultural practices and the change in personal property and the education level of family members. Agricultural knowledge and information are important and positively related to changes in household members' education levels and ownership of agricultural equipment.

Effect of Soil Texture on Agricultural Machine Performance in Sylhet, Bangladesh.

Soil texture is one of the primary soil physical properties that largely affects agricultural machine performance. Before implementing an agricultural machine at the farm level, it is important to test the machine to gain an understanding of its technical functionality and economic viability to get the best output from it. The research was carried out to assess how soil texture influences the performance of machinery, aiming to determine the effectiveness of agricultural equipment on a particular type of land. Soil samples were collected from nine different fields, and their textures were determined using hydrometer analysis. To correlate soil textural classes with machine performance, machinery data—effective field capacity, forward speed, and field efficiency—were calculated by operating a combine harvester on the study areas during the harvesting season of Boro rice. Two types of soil were identified: loamy sand and sandy loam. The findings of the study showed that machine efficiency has a positive correlation with sand proportion and a negative correlation with clay and silt fractions. It was observed that soil with loamy sand-type textural classes demonstrated better field efficiency, higher effective field capacity, and higher forward speed compared to sandy loam soil. Small and irregularly shaped fields also caused variation in performance. The study identified significant variation in speed between these two types of soils but no significant effect on effective field capacity or field efficiency at the 5% level of significance. The outcomes of this research will help farmers make informed decisions when selecting appropriate equipment for their agricultural operations.

Recent trends in reduction of hand arm vibration syndrome for agricultural handheld equipment: A review based on challenges and future directions

A major occupational health risk in the agricultural industry is Hand Arm Vibration Syndrome (HAVS), which is brought on by exposure to vibration from handled machinery. This review thoroughly covers the historical background and prevalence of HAVS in agriculture, highlighting the harm it does to the health and productivity of workers. The goal is to solve the research deficit in this area by concentrating on vibration isolation and dampening approaches. The paper comprehensively examines measurement methods, such as accelerometer-based approaches, strain gauge and force plate methods, wireless sensor networks, for evaluating vibrations related to HAVS. These methods are compared in order to clarify their advantages and disadvantages. Furthermore, engineering controls, isolation mounts, dampers, and smart technologies are included in the categories of shock and vibration reduction solutions based on their efficiency and application. Also covered are human factors that affect vibration reduction. The difficulties in putting reduction strategies into practice are underlined. These difficulties include economic considerations, technological limitations, and user approval. In order to draw useful conclusions, case studies demonstrating effective vibration reduction methods in agricultural contexts are provided. Future directions for reducing HAVS in agriculture include interdisciplinary research opportunities, developing technologies for vibration reduction, and a global approach. This review concludes by highlighting the importance of tackling HAVS, providing insights into real-world applications, and suggesting future research initiatives.

Agriculture eCommerce Website with ML Chatbot

The project aims to develop an innovative Agriculture eCommerce platform integrated with a Machine Learning (ML) chatbot to revolutionize the agricultural industry's online marketplace. The proposed system addresses the growing need for efficient access to agricultural products and services while enhancing user engagement through intelligent conversational interfaces. The Agriculture eCommerce site serves as a digital marketplace where farmers, agricultural professionals, and enthusiasts can browse, purchase, and interact with a wide range of agricultural products, equipment, and services. Leveraging ML algorithms, the platform's chatbot offers intuitive conversational interactions, allowing users to inquire about products, receive recommendations, and troubleshoot queries effectively. Through this project, B.Tech students will gain valuable experience in developing a comprehensive eCommerce solution integrated with cutting-edge ML technology. Additionally, the project holds the potential to make a significant impact on the agricultural sector by fostering digital transformation and facilitating access to agricultural resources. Keywords: - Agricultural industry, eCommerce, Machine Learning, Digital experience, User-friendly platform, Chatbot, Farming community.

ПРОТИВОКОРРОЗИОННАЯ ЗАЩИТА МЕДИ МАСЛЯНЫМИ КОМПОЗИЦИЯМИ С КОМБИНИРОВАННОЙ ДОБАВКОЙ М-531

The use of anti-corrosion protection is necessary to maintain the performance of agricultural machinery and equipment. The effectiveness of oil compositions with the addition of M-531 in relation to copper was studied. The compositions were obtained on the basis of industrial and motor M10G2k (both fresh and used) oils. Their protective ability was studied using gravimetric and electrochemical methods (polarization and impedance) studies. According to the data obtained from corrosion gravimetric tests in 0.5 M NaCl, it was noted that compositions based on M10G2k oil turned out to be the most effective when 5 wt. % of the additive, the protective effectiveness (Z) was 63%, a further increase in concentration led to a slight increase to Z = 65% at 10 wt. % M-531. The data from electrochemical measurements qualitatively confirm the data from gravimetric tests. Differences in the magnitude of protective effectiveness are associated primarily with different times of exposure to an aggressive environment: the duration of the experiment in the first case was 15 minutes, in the second - 14 days. According to the results of polarization measurements, compositions based on MMO were the most effective, already at a content of 5 wt. % additive value Z = 98%, while compositions containing 5 wt. % M-531 in I20-A and M10G2k had Z = 90%. The results of impedance studies showed that the addition of the M-531 inhibitor to MMO, M10G2K and I20-A oils affects the copper impedance hodographs. The inhibitor concentration had a significant effect on the change in Nyquist diagrams. At 3 wt. % M-531, a moderate change in copper plating impedance was observed, indicating that the inhibitor has some protective ability against corrosion, but its effectiveness may not be sufficient to fully protect copper. When the inhibitor concentration increases to 5 - 7 wt. % noticeable increase in the change in impedance indicated that a higher concentration of the M-531 inhibitor helps to improve the protective properties of the copper coating.

JUSTIFICATION OF NOZZLE PARAMETERS OF JET-TYPE WASHING MACHINES FOR CLEANING AGRICULTURAL MACHINES AND UNITS

Agricultural machinery and equipment are exposed to significant contamination by various types of pollutants, some of which can be easily removed, while others require the use of special cleaning agents and machines. To remove these contaminants, a pressurized water jet is used, the biggest disadvantage of such devices is the significant water consumption, so the issue of intensifying the cleaning process of agricultural machinery using a limited amount of water is an important scientific task. Cleaning of agricultural machinery and units from contaminants is a complex technological operation, therefore, there is a need to develop and justify the parameters and operating modes of a high-tech washing device that would ensure high-quality removal of all types of contaminants with high efficiency, minimal water consumption and minimal labor costs. The analysis of cleaning technologies showed that water jet cleaning is the most promising for removing dirt from the surface of agricultural machines and units, the technology allows to increase the level of mechanical impact by using additional energy, which can be the energy of a rotating jet. The study examines the efficiency of cleaning washing plants from various contaminants. The main focus is on studying the impact of nozzle design on the quality and productivity of the washing process. Nozzles determine the shape of water jets and their velocity, determining the cleaning efficiency. The dagger and fan nozzles, their advantages and disadvantages are considered. Nozzle parameters such as orifice diameter, spray angle, and number of fan jets are investigated. It is shown that properly selected nozzle parameters play an important role in achieving the optimal cleaning effect and water saving. Factors such as flow coefficient, drag coefficient, and velocity coefficient that affect the performance and energy of the jet are outlined.

Finite element explicit dynamics simulation of an impact cutting mechanism analysis of Populus tomentosa branches

The branch impact failure mechanism has gradually received attention from scholars, along with the application of impact cutting methods in plantation forest pruning. In this paper, the impact cutting failure mechanism of Populus tomentosa branches was mainly studied. The impact cutting process of branches was simulated by using Finite Element Method (FEM)-based explicit dynamics. The stress change and deformation characteristics in the branch failure process were studied. A theoretical model of branch impact cutting mechanism with branch diameter, cutting clearance, and branch angle as the main factors was proposed. The model describes the branch cutting damage process and the changing characteristics of cutting force. A branch failure state equation was proposed to describe the branch impact cutting failure patterns. The forest experiment was conducted to validate the branch impact cutting mechanism and the branch failure state equation. This work fills a vacancy of relevant theory and provides a theoretical basis for the future development of forestry and agricultural equipment using impact cutting.

Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.

An effective object detector via diffused graphic large selective kernel with one-to-few labelling strategy for small-scaled crop diseases detection

Amid the swift evolution of advanced technologies such as autonomous robotics, edge computing, and the Internet of Things, Intelligent Agriculture Management Systems (IAMS) have become increasingly pivotal in contemporary smart agriculture. One vital task, crop disease monitoring, significantly contributes to ensuring agricultural production quality and food supply security. However, IAMS encounters a particular challenge: due to the limited computational capacity of agricultural equipment and the scarcity of available training datasets, general detection models struggle to achieve anticipated performance in agricultural environments, especially when dealing with small-scaled targets. In this paper, we propose a diffusion learning detector named CropDetdiff, designed for disease detection in agricultural environments where training samples are limited. We have developed a backbone network based on dynamic convolutional kernels, which also embeds a graph attention network in its final stage, enhancing feature extraction capabilities for small targets. Additionally, we employ a fuzzy label assignment strategy to balance positive and negative samples and devise a new loss function, HIoU, to supervise the detection of small targets. A series of experiments on two crop disease datasets validate that CropDetDiff achieves an optimal balance between detection performance and operational efficiency. Specifically, the proposed model has achieved a mean Average Precision (mAP) of 73.2% and 40.1% on the two datasets, improving by at least 3.7% and 2.9% over other detectors, respectively, and there is a significant improvement in small targets particularly, thus achieving optimal performance. Additionally, the proposed model exhibits commendable performance in terms of parameter quantity and execution velocity. These results substantiate the efficacy and stability of CropDetDiff, underscoring its suitability for real-world applications within IAMS.

Farmer Application

The aim of the program is to provide a platform for farmers to rent modern agricultural machinery and equipment as well as rent storage space for crops. The app will also connect to government servers to track data on crops, fertilizer use and government funds. Records of machinery and equipment will be kept along with details of the crop stored. In India, agriculture is the main source of income for the majority of the population. However, many farmers are not aware of advances in agricultural technology and cannot understand the market value of their products. That's why they often sell products at very low prices. In addition, they also try to follow the government's agricultural plans and reports. To solve these problems, our initiative will create a platform where farmers can get the latest information on vegetables and fruits in every market in India. This will help them sell their products at reasonable prices and improve their financial situation. Additionally, farmers will also receive notifications regarding government agricultural programs and in-app notifications to benefit from these programs. Additionally, our application will help farmers prepare the required products by estimating the amount of vegetables and fruits according to the market. We will also include weather data to support farmers' planning for the next 2-3 days. Overall, our programs focus on providing farmers with new knowledge and technology that will improve their livelihoods and increase their productivity

Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education

Considering that the fusion of education and technology has delivered encouraging outcomes, things are becoming more challenging for higher education as students seek experiences that bridge the gap between theory and their future professional roles. Giving priority to the above issue, this study presents methods and results from activities assisting engineering students to utilize recent machine-learning techniques for tackling the challenge of water resource preservation. Cost-effective, innovative hardware and software components were incorporated for monitoring the proper operation of the corresponding agricultural equipment (such as electric pumps or water taps), and suitable educational activities were developed involving students of agricultural engineering. According to the evaluation part of the study being presented, the implementation of a machine-learning system with sufficient performance is feasible, while the outcomes derived from its educational application are significant, as they acquaint engineering students with emerging technologies entering the scene and improve their capacity for innovation and cooperation. The study demonstrates how emerging technologies, such as IoT, ML, and the newest edge-AI techniques can be utilized in the agricultural industry for the development of sustainable agricultural practices. This aims to preserve natural resources such as water, increase productivity, and create new jobs for technologically efficient personnel.

Developing Inclusive Models of Value Chain Financing

Abstract This paper presents a framework for designing external financial intervention mechanisms in the form of partnership agreements between financial institutions and different economic participants involved in the farm machinery supply chain. The scope of these partnership agreements is to facilitate small farmers’ access to loans when purchasing agricultural equipment. The research methodology and research results are drawn from desk and field research conducted on the emerging agricultural market. Several inclusive conceptual models of financing mechanisms based on business relationships between financial institutions, small farmers and either the suppliers of agricultural equipment or buyers of agricultural outputs are presented.

Seeds of the Settler Colony: How Peasant and Kazakh Knowledge, Environment, and Bureaucracy Shaped Steppe Agronomy in the Late Russian Empire.

At the turn of the twentieth century, Russian imperial officials hoped to transform the Kazakh Steppe from a zone of pastoral nomadism into a zone of sedentary grain farms. They planned to accomplish this transformation by importing peasants from European Russia and settling them in the steppe along with advanced scientific agricultural practices, equipment, and infrastructure. It was a project that linked steppe settlement and the Russian Empire to a global story of settler colonialism, science, and technology in the first decades of the twentieth century. An examination of this project through the lens of the expansion of grain farming reveals that the changes it wrought were not solely due to European science and technology but were contingent, dependent on local knowledge, the vagaries of climate, and adaptation to the realities of the steppe environment.

Sensitivity of Xylella fastidiosa subsp. pauca Salento-1 to light at 410 nm

All over the world, from America to the Mediterranean Sea, the plant pathogen Xylella fastidiosa represents one of the most difficult challenges with many implications at ecological, agricultural, and economic levels. X. fastidiosa is a rod-shaped Gram-negative bacterium belonging to the family of Xanthomonadaceae. It grows at very low rates and infects a wide range of plants thanks to different vectors. Insects, through their stylets, suck a sap rich in nutrients and inject bacteria into xylem vessels. Since, until now, no antimicrobial treatment has been successfully applied to kill X. fastidiosa and/or prevent its diffusion, in this study, antimicrobial blue light (aBL) was explored as a potential anti-Xylella tool. Xylella fastidiosa subsp. pauca Salento-1, chosen as a model strain, showed a certain degree of sensitivity to light at 410 nm. The killing effect was light dose dependent and bacterial concentration dependent. These preliminary results support the potential of blue light in decontamination of agricultural equipment and/or plant surface; however, further investigations are needed for in vivo applications.