Agricultural Processes Research Articles

The Multifaceted Role of Microbial Biosurfactants in Various Fields

This review paper aims to explore the multifaceted roles and applications of microbial surfactants, also known as biosurfactants. These amphiphilic compounds, synthesized by various microorganisms such as Acinetobacter sp., Bacillus sp., Candida antarctica, and Pseudomonas aeruginosa, exhibit both hydrophobic and hydrophilic properties. Through lowering surface tension and interfacial tension, biosurfactants facilitate fluid phase aggregation, imparting organisms with unique physiological and ecological advantages. The synthesis of biosurfactants in bacteria not only contributes to antimicrobial activity but also enhances substrate availability for cellular absorption, particularly in challenging environmental conditions. Categorized based on molecular weight and chemical composition, biosurfactants offer numerous benefits including biodegradability, reduced toxicity, and versatile applications in agriculture, detergent production, biopesticides, and microbial oil enhancement processes. This review synthesizes current knowledge on microbial surfactants, elucidating their diverse roles and promising applications across various industries.

The cultural construction of cellular agriculture food: through the lens of the whole-parts framework for meat

This essay presents a critical perspective on the nature of what is referred to as “cultured meat” in the public sphere by revisiting the concept of meat in cultural practice. We propose a perspective that focuses on and further investigates whether cultured meat is meat, examining the interaction between the emergence of cellular agriculture technologies and cultural practices. First, we review the production processes of cellular agriculture for meat, comparing this to the processes used to produce conventional meat. Second, we discuss how meat has been embedded in cultural practices, focusing on meat-related activities, and propose that a whole-parts relationship is critical for constructing the meaning of meat in cultural contexts. Third, several key issues raised in the social debate on cultured meat are addressed within the whole-parts framework. Cultured meat requires a complete bottom-up process in producing objects, indicating that the concept of cultured meat is not necessarily aligned with the concept of existing meat, as previously suggested from the perspective of producing conventional meat. In this perspective, we proceed to further investigation the meaning of meat in cultural constructions, wherein the relationship between the whole body and meat as a part of it is important. Thus, cultured meat can be considered a nonmeat under the whole-parts framework for meat. This insight can provisionally and speculatively update some of the ethical, legal, and social issues of cultured meat as well as products based on cellular agriculture technologies.

Effect of visualization of production process on consumers’ purchase intentions in farmer-assisted livestreaming

PurposeThis study investigates whether the visualization of agricultural products influences consumers’ purchase intentions in the context of farmer-assisted livestreaming in China. Moreover, it explores the moderating effect of packaging functionality and the mediating effect of consumer trust.Design/methodology/approachConsumers in China from multiple social media platforms participated in this survey, which yielded 333 valid responses for analysis.FindingsThe results revealed a positive relationship between the video presentation about the agricultural production process and consumers’ purchase intention, which is mediated by consumers’ trust. Meanwhile, packaging functionality moderates the relationship between agricultural product visualization and consumers’ purchase intentions as well as the indirect effect of consumers’ trust.Originality/valueThis study extends the application of the stimulus-organism-response (SOR) model to the field of farmer-assisted livestreaming. By building a more detailed model, this study adds to knowledge on the influencing mechanisms of consumers’ purchase intentions in farmer-assisted livestreaming.

Оценка эффективности взаимодействия участников зернопродовольственной цепочки

The article evaluates the effectiveness of the functioning of the grain-product subcomplex of the agroindustrial complex of the Tambov region. The results of the analysis showed that organizations of different industries and activities, including agriculture and grain processing industries, when choosing forms of interaction should take into account the union of conditions for equally beneficial cooperation for all participants. In general, in 2020-2022, there was a slight increase in the efficiency of the grain production chain of the Tambov region.

A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

A Computational Framework for Precise Aerial Agricultural Spray Delivery Processes

Improving Apple Fruit Quality Detection with AI and Machine Vision

The detection of apples using Raspberry Pi is an innovative approach that merges the realms of computer vision, machine learning, and agricultural automation. This abstract provides an extensive overview of the methodologies, implementations, challenges, and future directions pertaining to apple detection using Raspberry Pi, encapsulating the essence of the research conducted in this domain. The quest for automation in agriculture has spurred the development of novel technologies aimed at improving efficiency and reducing manual labor. Fruit detection, particularly the identification of apples, holds significant importance due to the fruit's widespread cultivation and economic value. Traditional methods of fruit detection often involve manual sorting, which is labor>intensive and time>consuming. Hence, there arises a need for automated systems capable of accurately identifying and sorting fruits, thereby streamlining agricultural processes. The implementation section details the practical realization of the apple detection system using Raspberry Pi. Hardware setup involves the integration of Raspberry Pi boards with camera modules and other peripherals necessary for image acquisition and processing. Software development entails the creation of Python>based modules for image preprocessing, feature extraction, and classification. OpenCV and scikit>learn libraries are utilized for implementing image processing and machine learning algorithms, respectively. The system is tested in different environments to evaluate its performance under various conditions, including controlled laboratory settings and outdoor agricultural scenarios.

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.

Efficient High-Resolution Deep Learning: A Survey

Cameras in modern devices such as smartphones, satellites and medical equipment are capable of capturing very high resolution images and videos. Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many other applications. Using high-resolution images and videos as direct inputs for deep learning models creates many challenges due to their high number of parameters, computation cost, inference latency and GPU memory consumption. Simple approaches such as resizing the images to a lower resolution are common in the literature, however, they typically significantly decrease accuracy. Several works in the literature propose better alternatives in order to deal with the challenges of high-resolution data and improve accuracy and speed while complying with hardware limitations and time restrictions. This survey describes such efficient high-resolution deep learning methods, summarizes real-world applications of high-resolution deep learning, and provides comprehensive information about available high-resolution datasets.

Analysis of the impact of agricultural credits on agricultural mechanization in Türkiye

Agricultural mechanization is one of the high-cost important inputs used in agricultural production. Thanks to agricultural mechanization, time and manpower are saved greatly in the agricultural production process. Agricultural loans, one of the most important agricultural financing instruments, are considered necessary for agricultural enterprises to invest in agricultural mechanization. This study, which adopts a quantitative research method approach, aimed to analyze the impact of agricultural loans on agricultural mechanization in Türkiye using time series analysis. Within the scope of the purpose, annual data covering the period 1981-2022 were gathered from data sources related to loans and agricultural mechanization. A log-log model was constructed with loans as the independent variable and agricultural mechanization as the dependent variable, and ARDL bounds testing method was applied in the estimation of the model. According to the findings obtained from the estimation of the model, there was a long-term, positive, and statistically significant co-integration relationship between loans and agricultural mechanization variables. A 1% increase in the loan balance was found to increase agricultural mechanization by approximately 0.035%. On the other hand, the short-term relationship between the variables was found to be negative and statistically significant. Short-term deviations from the long-term equilibrium caused by shocks were shown to vanish by approximately 86% after 1 period. In this context, it is clear that the adaptation process between the variables is very rapid.

Agricultural extension debatable issues

Extension, and extension workers in particular, drive the agricultural modernization process and the rest of those in associated professions and positions are ‘support staff.’ Whether you are a lecturer, a professor, a dean, a researcher, an extension director or a head of a non-government organization, if your mission is to develop smallholder agriculture at farmer level, you are supporting the field extension worker to achieve your/farmers’ goals. To this extent, it is no surprise that, when no perceptible improvement takes place at the farmer level, the blame lies squarely on the shoulders of extension. Despite its importance, agricultural extension is the most misunderstood of all agricultural disciplines, and the lack of understanding reveals itself in several ways. This paper discusses some of the misconceptions and debatable issues that affect the way extension is supported, the way it is structured, the way it is staffed and the way extension workers are trained and capacitated. The misconceptions also affect the kinds and levels of expectations people have of extension. The misconceptions include: unending definitions of extension, expanding extension concepts, blaming extension for perceived failures in agriculture, undermining the extension discipline, inadequacies in extension teaching and training, markets and the role of extension and structural changes in extension. The origins of some of these debatable issues can be traced to development partners who come with their conceptions of what is needed while others come from development theorists who have not done any extension work in the field. Unfortunately, there are no strong extension professional associations in many African countries that could: raise the profile of extension as a discipline, interrogate some of the agricultural development interventions before implementation and help clear some of the misconceptions. In the absence of extension platforms, extensionists operate as individuals, each struggling the best way they know how to make a difference at the farmer level. There is no way of harnessing the experiences the individuals are going through for purposes of learning, sharing and developing common positions. The purpose of this paper is to promote debate on, and scrutiny of these extension issues which are often presented as facts and absolute truths. Key words: Extension issues, debatable, agricultural discipline, misconceptions, professionals, smallholder farmers

Smart Irrigation and Pesticides System for Enhancing Harvesting in Agriculture Using IoT

Agriculture plays a vital role in the economy of many countries, serving as a primary source of food. However, traditional farming practices often face challenges such as inefficient water usage, over-reliance on pesticides, and inconsistent monitoring of crop conditions. In recent years, the Internet of Things (IoT) has emerged as a promising technology to address these challenges by providing real-time monitoring and control of agricultural processes. This leads in excessive water use, which has an impact on agricultural output. This integrated system means that farmers only need to perform little manual monitoring. The opening and closing of the reservoir and field gates, as well as the verification of the plant's age and the matching optimal water level necessary for that time of plant age, are all automated. Farmers may use their mobile phones to check the water level in the field since they are continually receiving updates on the field work. The most labour-intensive and time consuming activity in agriculture is water management. In our project, the water level in the agriculture harvest sting is measured, and if it is lower than the required level, the paddy field is automatically irrigated for the water source or tank using a water pump. It can comprehensively monitor air temperature and humidity, light intensity, ultraviolet light and other environmental elements. In specific use, the agricultural production environment monitoring system starts from the actual needs of the plantation zone and is assembled according to the environmental elements it needs to monitor.

COVID‐19 pandemic in Ethiopia: Socioeconomic impacts and government recovery measures

AbstractThe study assesses the implications of the COVID‐19 pandemic and the government responses for the economic performance and poverty incidence in Ethiopia for the fiscal years of 2019/20 and 2020/21. It accounts for the impacts of the pandemic on factor productivity, trade costs, export demand, tourism, remittances and foreign direct investment (FDI). An economy‐wide multi‐sectoral model determines impacts at macroeconomic, sectoral, and household levels. A poverty analysis module characterises the effects of the pandemic on food poverty headcount, gap, and severity. Results show that the COVID‐19 impacts could have been significant across all macroeconomic metrics had the government not intervened through fiscal and spending measures. However, much of the recovery through government intervention was driven by agricultural and food processing sectors while output in manufacturing, construction and services sectors continued to be negatively affected. Without government intervention, the food poverty headcount would have increased by about five percentage points. The government measures have mitigated that effect allowing food poverty to reach pre‐COVID‐19 values in 2020/21. Nevertheless, with all measures combined, poor urban households may have been left behind and would require more targeted support to compensate for a significant loss of income.

Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality

The quality and safety of agricultural products involve a variety of risk factors, a large amount of risk information data, and multiple circulation and disposal processes, making it difficult to accurately trace the source of risks. To achieve precise traceability of agricultural product quality and safety, the monitoring and supervision level of agricultural product traceability systems needs to be improved. In this paper, the agricultural product quality- and safety-related detection data are taken as the research object, the agricultural product supply chain and business process are sorted out, the agricultural product risk factors under the influence of heavy metals are analyzed, and the agricultural product quality and safety risk evaluation model is established. The agricultural product risk traceability system was designed based on the six-layer architecture of the equipment layer, data layer, service layer, application layer, display layer, and user layer. Combined with the agricultural product quality and safety risk evaluation model, the system data were stored and modified by using the MySQL relational database, and the agricultural product risk traceability system was developed, which realized the accurate traceability of the risk link of the quality and safety of agricultural products. The research results of this paper provide an effective information means for the quality and safety supervision of agricultural products, and provide a technical reference for the development of a traceability system of risk factors related to agricultural products, which has good practical significance.

An Effective Model for Selecting the Best Cloud Platform for Smart Farming in Smart Cities: A Case Study

Cloud computing has become an essential technology for various industries, including smart farming and smart city initiatives. Integrating smart farming into smart city initiatives can bring numerous benefits, such as increased food production, resource efficiency, environmental sustainability, and improved quality of life for urban residents. Smart cities and smart farming are two interconnected concepts that aim to improve the efficiency and sustainability of urban areas. Smart cities use technology and data to optimize infrastructure, services, and resources, while smart farming applies these same principles to agriculture. Smart farming with cloud computing involves the use of cloud computing technologies to optimize agricultural processes and improve crop yields. By using cloud computing technologies, farmers can collect, store, and analyze large amounts of data, access advanced analytics and machine learning tools, collaborate with agricultural experts, and benefit from scalability and flexibility. In this paper, we propose a novel MEREC-MAIRCA method to select the best cloud platform that helps decision-makers use smart farming in smart cities. By leveraging technology and data, smart farming can enable new business models, create new jobs, and foster social connections around food production and consumption.

High Added-Value by-Products from Biomass: A Case Study Unveiling Opportunities for Strengthening the Agroindustry Value Chain

The current era witnesses a remarkable advancement in biomass utilization, guided by the principles of green chemistry and biorefinery and the comprehensive exploitation of plant-based raw materials. Predominantly, large-scale production methods have been pursued, akin to approaches in the oil industry, enabling the incorporation of novel products into energy and petrochemical markets. However, the viability of such systems on a small and medium scale is hindered by logistical challenges and the constraints of economies of scale. For small agricultural producers and food processing companies, the complete utilization of biomass transcends environmental responsibility, evolving into a strategy for survival through the diversification of by-products with enhanced value. The state of Rio de Janeiro in Brazil presents a range of population dynamics, geographical features, climate conditions, and agricultural production patterns that closely resemble those found in various tropical countries and agricultural regions worldwide. This region, sustaining a green belt supporting 17 million people, provides an apt case study for investigating chemical compounds with potential value among agro-industrial residues, which can motivate the creation of a lucrative biotechnological industry. Examples include naringenin and hesperidin from oranges and lemons, epi-gallo-catechin gallate from bananas, caffeic acids from coffee, and the bromelain enzyme from pineapples. This study addresses the challenges associated with developing biotechnological alternatives within the agroindustry, considering economic, technological, logistical, and market-related aspects. The insights from examining the Brazilian state of Rio de Janeiro will contribute to the broader discourse on sustainable biomass utilization and the creation of value-added by-products.

ANALIZA I ZARZĄDZANIE ROZWOJEM ORGANICZNEGO ROLNICTWA W KRAJACH EUROPY WSCHODNIEJ

The study analyses trends in domestic markets and international trade in organic products in some Eastern European countries based on a multifactorial comparative analysis in the regional context and estimates of yield losses about traditional agricultural land processing technologies. Prospects for the development of organic farming by region and in general are determined based on unused reserves of agricultural land suitable for this. The study assessed the organic production development in different regions of Poland and Ukraine: the annual growth rate of transitional and organic lands should be at least +13.3% to meet the strategic goal of reaching 3% of the total agricultural land by 2030.

Intelligence Farming Using Machine Learning

Abstract: This research paper delves into the revolutionary role of machine learning in contemporary agriculture, a concept termed as "Intelligence Farming." The study encompasses the application of data in farming, including data related to weather conditions, soil quality, and crop health, and how machine learning contributes to efficient resource distribution and crop management. The paper underscores the significance of predictive yield forecasting, precision farming, and early detection of crop diseases, all facilitated by machine learning. Furthermore, it discusses the socio-economic and environmental outcomes of implementing this technology, such as enhanced productivity and sustainability. In conclusion, the paper strongly recommends the incorporation of machine learning in agricultural decision-making processes, underlining its critical role in the current era of data-driven decision-making.

Classification of Rice Grain Varieties Using Ensemble Learning and Image Analysis Techniques

This research explored the efficacy of machine learning techniques, specifically the Bagging meta-estimator, in the classification of rice grain images. Utilizing a dataset composed of 45,000 images of Arborio, Basmati, and Jasmine rice varieties, a 5-fold cross-validation was employed to evaluate the model's performance. The results were highly promising, with the model consistently achieving over 96% in accuracy, precision, recall, and F1-score across all folds, indicating its robustness and reliability. The study confirmed that ensemble learning techniques could significantly improve the classification accuracy over single classifier systems in agricultural applications. The findings offer a significant contribution to automated agricultural processes, suggesting that machine learning can greatly enhance the efficiency and precision of rice variety classification. These results pave the way for further research into the integration of such models into agricultural quality control and provide a foundation for the exploration of advanced image processing and deep learning techniques for improved performance. Future research directions include expanding the model to encompass a wider variety of crops and integrating additional data modalities to refine classification accuracy further. Practical applications should explore the incorporation of this technology into existing agricultural systems to maximize the benefits of automation in quality control.

Production of meat and processed products in the conditions of technological modernization of agro-industrial complex of the East Kazakhstan region

Goal - the current situation and promising directions for the development of meat industry in the East Kazakhstan region are considered, and limiting factors for its growth are identified. During the research process, in the context of a comprehensive analysis, calculationconstructive, statistical-economic, expert, and monographic methods were used. Results – an assessment of the main parameters of livestock complex and meat processing sector in the Eastern region over the past five years is presented. Based on the results of the study, it was revealed that there are positive changes in meat sector, but overall production is not efficient enough. The authors note the lack of raw materials and high cost of raw materials. Food imports occupy a significant place, and small-scale production of farms leads to decrease in the competitiveness of goods produced. Conclusions – the results of the study made it possible to substantiate the most optimal directions for increasing the volume of meat product subcomplex and expanding meat products market through effective government regulation, development of cooperative and integration relations between agricultural producers and processing industry enterprises, allowing the creation of cooperatives that contribute to increasing the size of production and processing of meat products. As economic measures, it is advisable to stimulate supply and demand for food products, preferential lending throughout the entire cycle of obtaining products and selling them on domestic and foreign markets. The authors focus on the need to find options for reducing costs of production processes, implementing innovative projects and forming cluster-type associations. The creation of new enterprises (meat processing plants, poultry farms) will create new jobs and increase the income of the population.

Multifractal Description of the Agricultural and Meteorological Drought Propagation Process

Multifractal Description of the Agricultural and Meteorological Drought Propagation Process