Processing Of Agricultural Products Research Articles

Naming and dividing: the production of residual labour in the region of Doñana

AbstractThrough a case study of the production of agricultural wage labour in southwestern Spain, in the region of Doñana, this articles analyses the process of constructing agricultural wage labour as a residual social relation. Through analysis of the deployment of labour in agro-industrial berry production, I trace the ways in which the notions of “temporariness” and “slave work” obscure the centrality of labour in the agricultural production process and conceal the reconfiguration of relations of exploitation.

Resource allocation design method based on WSN multi-dimension soil inspection

Abstract. With the industrialization of agriculture and the sharp rise of food prices, in order to promote the intensive development of natural resources and achieve economies of scale agriculture in production, people put forward the innovative strategy of "precision agriculture" to maintain the sustainable development of agriculture. To protect the environment and farmer communities and increase productivity. However, in the special context of precision agriculture, the problems related to energy consumption and electromagnetic wave propagation in complex environments have a significant impact on precision agriculture. In the case of no monitoring and harsh environment, wireless sensor networks (WSN) can complete the tasks of real-time monitoring and information collection. In the past, the understanding of WSN in agriculture was limited to unilateral detection, which led to the underutilization of the soil. This paper introduces an innovative solution for precision agriculture based on wireless sensor networks, which mainly focuses on the observation and impact assessment of soil water volume,PH value, temperature and pressure in agricultural production process. It aims to reduce the waste of natural resources and the use of fertilizer substances, while maintaining or improving the yield of farming, in order to better promote the development of WSN in the agricultural field.

Leaf disease detection and classification with a CNN-LSTM hybrid model

Abstract. Smart agriculture refers to the use of modern information technology, Internet of Things technology, and artificial intelligence to achieve accurate management, efficient operation and sustainable development of the entire process of agricultural production. Smart agriculture mainly includes the application of data collection and analysis, intelligent agricultural machinery, precise fertilization, disease and pest detection and agricultural products traceability. Leaf disease detection and classification are considered as challenging yet important tasks in smart agriculture. Deep leaning methods have been proven effective for these image-based recognition tasks. In this study, two advanced deep learning methods, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined together to achieve a further improvement. Numerical results demonstrate that the proposed method outperforms both CNN and LSTM variants.

Production losses in Russian agriculture: Impact on food security and environmental sustainability

This paper provides a quantitative assessment of the impact of losses on specific aspects of food security and environmental sustainability. The lack of full information on the benefits of food loss reduction destimulates developing appropriate effective measures at the public level and in the private sector. The study covers losses generated at the initial stages of the food supply chain, including agricultural production and primary processing. Methodological assessments are based on works of foreign authors, recommendations of the Food and Agriculture Organization of the United Nations (FAO), the Intergovernmental Panel on Climate Change (IPCC) and domestic methodological developments in this area. The results indicate that at the primary stages of the food supply chain, food losses generate a relatively small amount of greenhouse gas emissions, so their reduction in order to lower the carbon footprint is not as effective as at the stage of retail and household consumption. However, preventing losses in agriculture contributes to the creation of additional volumes of food, which can be directed to the consumption of vulnerable groups of population. At the same time, the consequences of loss reduction vary greatly depending on the type of product

An Analysis of Alternative Strategy Model for Agroindustry Business Development of Crystal Guava in Malang Raya

PT. Kreasi Tani Bumiaji is agroindustry company located in Banaran, Bumiaji Village, Bumiaji District, Batu City, East Java. This company focuses on processing agricultural products in the form of crystal guava and becomes a superior local product. This study aimed at analyzing alternative strategy of agroindustry business development of crystal guava as a product in PT. Kreasi Tani Bumiaji. Descriptive-quantitative method was used to collect the primary data from interviews and questionnaire items. Eight participants from the company were selected by using purposive sampling technique. The secondary data were obtained from academic articles. The data were analyzed by using SWOT analysis, Internal Factor Evaluation (IFE) Matrix, External Factor Evaluation (EFE) Matrix, Internal-External (IE) Matrix, and QSPM. The results of analysis showed that main internal factor is diversification of processed products with the score 0.277. Meanwhile, external factor is internship collaboration with the score 0.418. The position of company in IE matrix lies on the fifth cell which showed that stable growth with coordinate scores 2.930; 2.594. According to 15 alternative strategies identified using SWOT analysis, prioritized strategy based on QSPM analysis refers to business partnership with investors for the development of production facilities, business concept, and marketing with the highest score 7.23.

The impact of socialisation services in the whole process of agricultural production on food security—quasi-natural experimental evidence from China

Food security (FS) is an important guarantee for world peace and development and the basis for building a community of human destiny, which has a bearing on the sustainable development and future destiny of humankind. This study empirically analysis the relationship between socialisation services in the whole process of agricultural production (ASS) and guaranteeing FS, as well as the path of their role, using provincial panel data from 2010 to 2022 in China as an example, using the double-difference method. The regression results show that ASS can have the ability to contribute to the level of FS, and the conclusion still holds after a series of robustness tests. The results of the heterogeneity analysis show that the role of ASS in guaranteeing FS receives the influence of the level of agricultural development in each region, while it plays a greater role in the main food-producing areas. The mechanism analysis analyses the mechanism of the role of ASS in the whole process of agricultural production in guaranteeing FS from the three links of pre-production, mid-production and post-production respectively, and finds that ASS in the whole process of agricultural production is able to guarantee FS by reducing the cost of purchasing means of production in the pre-production stage, fostering a new type of agricultural management main body in the mid-production stage, and increasing the income of farmers’ household management in the post-production stage. Based on the above findings, this study proposes corresponding policy recommendations. This study can provide insights for ensuring world FS and contribute to maintaining social stability and development. The conclusions of the study are universally applicable, and all countries in the world can benefit from and draw lessons from it.

Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emerging trends and needs for non-destructive methods to evaluate fruit quality accurately and practically. This research aimed to develop a novel approach for predicting the physicochemical properties of papayas using a convolutional neural network (CNN) model that combines image analysis and weight assessment. This study involved capturing images of papayas at different ripening stages, measuring papaya weights, and determining various physicochemical properties such as texture, pH, total soluble solids, and seed weight. A total of 532 images were obtained from 132 papayas, and an additional 1064 images were generated through image augmentation. The dataset was divided into three sets with an 8:1:1 ratio for training, validation, and testing. The CNN model was trained using papaya images and weights as input values to predict and estimate the physicochemical property values. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R2) as metrics. The CNN model, integrated with image processing, could predict the diverse physicochemical properties of papayas with high accuracy. The MSE values estimated for the training and validation sets were 0.0284 and 0.1729, respectively. The R2 values for the test dataset ranged from 0.71 to 0.94. These findings demonstrate that CNN-based models could provide detailed and quantitative insights, facilitating improved understanding and management of papaya quality while enhancing predictive modeling accuracy in agriculture. PRACTICAL APPLICATION: This research introduces a new method for accurately predicting the quality of papayas using a computer model. Instead of relying on manual inspection, which can be slow and prone to errors, this model uses images of papayas and their weights to predict properties, including texture, pH, total soluble solids, and seed weight. This can help manage papaya quality better while also improving agricultural production and transportation processes.

Digitalization and Agricultural Green Total Factor Productivity: Evidence from China

Based on panel data of 31 provinces (autonomous regions and municipalities) in China from 2011 to 2022, this paper empirically examines the impact of digitalization on the inputs and outputs of the agricultural production process, and thereby derives the effects and mechanisms by which digitalization empowers the growth of agricultural green total factor productivity. The study finds that agricultural and rural areas’ digitalization significantly improves agricultural green total factor productivity, and this promotion mainly comes from the improvement of technical efficiency. Further analysis shows that digitalization mainly reduces land input and labor input, increases expected output, and reduces undesired output during the agricultural production process to achieve an improvement in agricultural green total factor productivity, indicating that digitalization has altered the allocation of agricultural factors. Heterogeneity analysis finds that the effect of digitalization on the growth of agricultural green total factor productivity is more pronounced in the eastern regions, southern regions, and areas with higher levels of agricultural digitalization, indicating that the development of digitalization exacerbates the gap in agricultural green total factor productivity among regions.

PELATIHAN PENGEMBANGAN AGRO EDU WISATA MENUJU SMART VILLAGE YANG BERKELANJUTAN PADA KELOMPOK MILENIAL

Agro edu-tourism is an alternative to community economic development, especially in rural areas, which can increase the income and welfare of local residents through the tourism and agriculture sectors. Sindulang Village is one of the villages that has great potential in the agricultural and tourism sectors. With this great potential, the Sangga Buana University Community Service team together with partners from local millennial groups aim to make the agricultural land area in Sindulang Village an Agro Edu Tourism object towards a sustainable Smart Village. One of the important factors to achieve the realization of the objectives of this activity is by improving the quality of human resources of partners. Several problems were found that became obstacles to the realization of agro edu tourism development in terms of knowledge about the agricultural production process, digital marketing-based marketing, and Tourism Awareness Groups. The method of implementation in this activity is by conducting training, demonstrations, and discussions with service partner participants. The results of the implementation of the training increased understanding and knowledge of the material raised from the obstacles experienced. So that after the training is carried out, agro edu tourism planning towards a smart village which is the goal of this activity can be continued by looking at various other factors, guidance on utilizing digital marketing, keeping financial records, photography, managing the development of tourism packages, and handling tourism risk management.

Social and Economic Problems of Agricultural Enterprises: The Case of Bayburt

Agricultural enterprises are enterprises that make significant contributions to the economic, social and cultural development of countries by engaging in agricultural production, agricultural product processing and agricultural marketing activities. It can be said that agricultural enterprises in Türkiye face many problems because they are mostly SMEs. The aim of this study is to examine the social and economic problems of agricultural enterprises based on the Bayburt example. In the study, it was deemed appropriate to use face-to-face interview technique within the scope of qualitative research method. In the interview, a questionnaire containing 32 questions about the social and economic problems of agricultural enterprises was used in addition to questions about participants and agricultural enterprises such as position, sector experience, enterprise size, enterprise title. Data were collected from 27 agricultural enterprises engaged in production, processing and marketing activities in Bayburt province. Content analysis technique was used to analyze the data. The responses of the participants were processed in depth within the scope of the themes determined in accordance with the content analysis technique, and the social and economic problems of agricultural enterprises were identified and solution suggestions were developed. As a result of the analysis, it was determined that agricultural enterprises have various problems related to management, production, marketing, finance and risk management, accounting, human resources, R&D and infrastructure, and suggestions were made within the scope of each theme. In conclusion, it is thought that this study will shed light on all agricultural enterprises operating in the sector and relevant stakeholders by addressing the existing problems, identifying the obstacles and developing recommendations.

Artificial Neural Network Model for Predicting Carrot Root Yield Loss in Relation to Mechanical Heading

Modelling and predicting agricultural production processes have high cognitive and practical values. Plant growth, the threat of pathogens and pests, and the structure of agricultural machinery treatments are mostly non-linear, measurable processes that can be described mathematically. In this paper, a multiple regression analysis was carried out in the first step to check the non-linearity of the data and yielded a coefficient of determination of R2 = 0.9741 and the coefficient of determination corrected for degrees of freedom was R2adj = 0.9739. An artificial neural network model, called CH-NET, is then presented to predict the yield loss of carrot roots by leaving root mass in the field during harvest at the mechanical heading stage. The proposed network model has an architecture consisting of an input layer, three hidden layers with 12 neurons each, and an output layer with one neuron. Twelve input criteria were defined for the analysis and testing of the network, eight of which related to carrot root parameters and four to the heading machine. The training, testing, and validation database of the CH-NET network consisted of the results of field trials and tests of the operation of the patented (P.242097) root heading machine. The proposed CH-NET neural network model achieved global error (GE) values of 0.0931 t·ha−1 for predicting carrot root yield losses for all twelve criteria adopted. However, when the number of criteria is reduced to eight, the error increased to 0.0991 t·ha−1. That is, the prediction was realised with an accuracy of 90.69%. The developed CH-NET model allows the prediction of economic losses associated with root mass left in the field or contamination of the raw material with undercut leaves. The simulations carried out showed that minimum root losses (0.263 t·ha−1) occur at an average root head projection height of 38 mm and a heading height of 20 mm from the ridge surface.

Ecological unequal exchange: Evidence from imbalanced cropland soil erosion and agricultural value-added embodied in global agricultural trade

The cropland soil erosion (CSE), a major driver of land degradation and water pollution, is directly caused by agricultural production processes driven by food demand in a globalized food market. However, it is essential to determine what role global trade and agricultural value chains play in regional CSE, especially when imbalanced erosion is embodied in global agricultural trade. In this study, the Revised Universal Soil and Water Loss Equation and the Environment Expanded Multi Region Input-Output Model were used to quantify the global distribution of CSE and the value added in global agricultural value chains to identify trade imbalances. We introduce the Local Environmental Coefficient, the Regional Environmental Coefficient, and the Agricultural Trade Environment Inequality (AEI) index, which were used to analyze the overall and bilateral inequalities in agricultural trade among the world’s regions. The results showed that 34.65 % of the global CSE was embodied in international trade, whereas the ratio of the value added was 30.80 %. The global trade in agricultural products is generally unbalanced, as indicated by the AEI index of developed regions such as Norway and Japan, which is more than 30 times higher than that of regions such as China and Brazil, and more than 20 times higher than the global average. In terms of bilateral trade, China, the United States, and Brazil face more prominent imbalances in their international trade. Major agricultural producers generally bear more environmental costs with less value-added benefits in trade. This study quantifies, for the first time, the implied imbalances of CSE transfer in agricultural trade. Global ecological governance thus requires accountability from all regions, especially in an era of increasing globalization of agricultural trade. Timely adoption of ecological compensation and technology transfer for both large agricultural producers and less-developed regions are key to achieving sustainable global agricultural production.

Effective removal of nitrate by palygorskite-supported nanoscale zero-valent iron from aqueous solution

Nitrate nitrogen (NO3--N) has become an important pollutant in industrial and agricultural production processes in China. In this study, Palygorskite-supported nanoscale zero-valent iron (nZVI/PG) adsorbent was prepared using the liquid-phase reduction method for the removal of NO3--N from water. Benefiting from the good dispersion uniformity of nZVI on the Palygorskite carrier, the prepared nZVI/PG exhibited a significantly increased specific surface area compared to nZVI, with a noticeable reduction in nZVI aggregation. The Fe/PG mass ratio of 2:1 was found to be the optimal loading ratio for the removal of NO3--N by nZVI/PG, with a removal capacity of 24.36 mg/g and a removal efficiency of 81.23 %, with a nitrogen gas conversion rate of 40.92 %. The removal process of NO3--N by nZVI/PG followed a pseudo-second-order kinetic model, with the removal rate inversely proportional to the initial concentration of NO3--N and directly proportional to the dosage. nZVI/PG exhibited high removal efficiency for NO3--N within the pH range of 2 to 9, with the highest selectivity for NO3--N conversion to N2 observed at pH 5. The main mechanism for the removal of NO3--N by nZVI/PG was found to be adsorption and reduction, with the main products being NH4+-N, NO2--N, and N2.

A hydroponic vegetable business management approach in holistic good agricultural practices in Thailand

Good agricultural practices (GAP) are guidelines designed to ensure the production of high-quality agricultural products that meet specified standards while optimizing resource use. This study aimed to examine the challenges and success factors in implementing a holistic system of GAP in hydroponic vegetable business management and to enhance the efficiency of Thailand’s policies on safe agricultural standards. In this study, a qualitative research methodology was used, using in-depth interviews, semi-structured interviews, and observations with 36 intermediaries and consumers in Thailand. Data analysis was conducted using descriptive statistics. The study revealed that key challenges to successful GAP implementation include unsupportive government policies and a lack of in-depth knowledge among farmers on how to effectively apply GAP principles. For successful implementation, it is crucial that farmers who lead these efforts thoroughly understand the methods and prioritize safe and proper farming practices. This knowledge can then be leveraged to prepare and educate farmers, producers, and entrepreneurs to produce high-quality agricultural products. GAP for crops include policies, benchmarks, and audit processes that adhere to international standards, providing a framework for controlling and promoting product quality throughout the agricultural production process, including post-certification maintenance. The study suggests that cooperation across all relevant sectors, from upstream to downstream, is essential for the successful implementation of GAP. Such collaboration would significantly enhance the quality and safety of Thailand’s agricultural standards.

METHOD FIRST APPROXIMATION STABILITY ANALYSIS OF ELECTRICAL CONTROL SYSTEMS

This article is devoted to the research and analysis of automated control systems and control of electrical equipment of technological processes of agricultural production. The first approximation method is used for evaluation the stability of the operation of electric drive control systems. Methods for assessing the stability zone of electric drive control systems, determining critical gain coefficients, and optimizing the parameters of electrical circuits included systems, in order to increase the efficiency and reliability of production chains are proposed. To solve the problem of controlling the electric drives of automated systems for harvesting and sorting agricultural crops, the method was tested, a critical gain value of 3.2 was obtained, which allows us to talk about optimizing such systems in terms of speed and load.

Enabling Precision Agriculture through a Web-Based Fertilization Management System for Nawungan Selopamioro Fruit Orchards

Precision Agriculture (PA) is an integrated farming system based on information and technology for managing agriculture to identify, analyze, and manage spatial and temporal diversity information in specific locations to obtain optimum and sustainable benefits while minimizing unwanted environmental impacts. Fertilization is one of the crucial phases in agricultural production process considering technical cultivation aspects, costs, and environmental impacts. The current fertilization process at Kebun Buah Nawungan Selopamioro (KBNS) is still conventional, so there is no standard rule in determining the fertilization dose. Therefore, a PA approach is needed to provide suitable fertilizer doses for agricultural production needs. This objective of this study was to develop of a web-based fertilizer management system, integrating with orchard management to enhance accessibility and decision-making. The system calculates fertilizer requirements by analyzing soil nutrient availability (N, P, K), cultivation area, crop type and age, and available fertilizer types. The development followed the waterfall methodology, encompassing stages from requirement analysis to system maintenance. The outcome is a web application that manages land assets, administrative activities, and fertilizer needs tailored to specific land blocks, crop characteristics, and nutrient inventories. Subsequent validation against field conditions ensures the accuracy of its recommendations. Although comprehensive testing confirmed a 100% success rate in functionality, the system currently operates within a limited scope of variables. Future enhancements are planned to incorporate broader agronomic factors, such as soil pH and texture, to augment the system's precision. Despite its limitations, this system represents a significant technological advance in precision agriculture, promising to improve fertilizer application efficiency and support sustainable farming practices.

Prediction of Potato Volume by Neural Network Regression Model

Potatoes are essential for meeting the world's dietary requirements due to their high concentration of carbohydrates, vitamins, and minerals. Potato production is plentiful worldwide, but postharvest processing losses have reduced potato quality. Potatoes can be graded manually, which is time-consuming and labor-intensive. In this study, a neural network regression model was developed to predict the volume of potatoes using 2D images, which can be used as a parameter for grading potatoes automatically. The image processing method was used to extract features like the major axis, minor axis, and roundness of 400 potato samples, and each potato's volume was thoroughly measured using the water displacement method, and a dataset was generated. These attributes were used to create the neural network regression model and predict the volume. The results revealed that the mean squared error (MSE) was 83.93, the root mean squared error (RMSE) was 9.16, and R2 was approximately 0.882. The potatoes were categorized into three groups: small, medium, and large, based on their size. The utilized model demonstrated an overall accuracy rate of 91.4%. Specifically, it achieved 90% and 85% accuracy for small-sized and medium-sized potatoes, respectively, whereas the model displayed a decrease in accuracy from 60% to 70% for large-sized potatoes. To summarize, the suggested neural network regression model provided an efficient approach to volume prediction using 2D images to improve the grading process of agricultural products.

Interference of mechanical damage on the physiological performance of corn seeds

Corn (Zea mays L.) is cultivated and consumed globally due to its nutritional qualities and short production cycle. However, during harvesting, processing, transportation, and storage, seeds are susceptible to various types of damage that can compromise their productive capacity, affecting both germination and plant vigor. Therefore, this study aimed to evaluate the physiological quality of corn seeds subjected to different types of mechanical damage. The experiment was conducted at the Laboratory of Storage and Processing of Agricultural Products at the Federal University of Campina Grande, Campina Grande, Paraíba, Brazil. Corn seeds were subjected to four types of mechanical damage: hammering, cutting, abrasion, and perforation, alongside a control sample that did not undergo any mechanical damage. Each treatment (mechanical damage) consisted of 200 seeds, which were evaluated through germination tests, including first count, germination rate, germination speed index (GSI), seedling dry weight, and root length. The experiment was conducted in Completely Randomized Design. Data were subjected to Analysis of Variance, and means were compared using the Scott-Knott test (P ≤ 0.05) when necessary. Mechanical damage reduces the germination and vigor of corn seeds. Cutting-type damage affects corn seeds more severely, reducing germination by an average of 80% and impairing vigor.

Detection of physical hazards from fruit processed products using hyperspectral imaging and prediction based on PLS-DA and logistic regression machine learning models

The current study aims to investigate a prediction model from reflection values and the spectral angle mapper (SAM) obtained from hyperspectral imaging (HSI) technology for the detection of three types of foreign materials – a branch, a knife, and rubber – that are associated with the various fruit-processed products. The study found that the maximum and minimum reflection values in the 900 to 1700 nm range for juices (apple, grape, tomato) and jams (strawberry, peach, tomato) differed depending on the presence or absence of physical hazards. The presence of physical hazards was also confirmed by the color difference in the SAM image. The partial least squares discriminant analysis model (PLS-DA) and logistic regression implemented on the Jupyter Notebook platform through the Anaconda prompt provided accuracy, F1 score, specificity, and sensitivity based on the confusion matrix. The maximum value was 100.0 %, while the minimum value was 97.6 % of the result of the PLS-DA modeling in the testing set. Logistic regression modeling also had a similar result: the maximum value was 100.0 %, while the minimum value was 97.8 % in the testing set. The sensitivity value in the testing set, which is a meaningful result for detecting physical hazards, was a maximum of 100.0 % for both PLS-DA and logistic regression. Results from the current study suggest that the reflection value and SAM data obtained through hyperspectral imaging could build a big data platform for early determination of physical hazards during agricultural product processing.

Integration of Remote Sensing and Machine Learning for Precision Agriculture: A Comprehensive Perspective on Applications

Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.