Quality Of Agricultural Products Research Articles

Exploration of the Application of Agricultural Intelligent Robots in the Qinghai-Tibet Plateau

In order to improve agricultural efficiency, reduce labor intensity, and improve the quality and yield of agricultural products on the Tibetan Plateau, this paper takes the application of traditional agricultural robots as an example and proposes the application of agricultural robots to agriculture on the Qinghai-Tibet Plateau based on the special breeding environment of the Qinghai-Tibet Plateau combined with the current situation of traditional agriculture. Through the application practice of robots in traditional agriculture, the possibility of the application of agricultural robots in the Qinghai-Tibet Plateau is pointed out, the working methods and advantages of robots in the Qinghai-Tibet Plateau are expounded, and the application of intelligent agricultural robots in the agriculture of the Qinghai-Tibet Plateau is discussed and analyzed. The results show that with the progress of science and technology, intelligent robots have gradually played an important role in various fields, and intelligent robots have broad application prospects in plateau agriculture. The application of agricultural robots in the Qinghai-Tibet Plateau should be aimed at the Qinghai-Tibet Plateau to make some adaptive research and development, intelligent upgrading and demonstration project promotion in order to promote the Qinghai-Tibet Plateau agricultural automation.

Analysis of Distribution and Marketing Efficiency of Pineapples from a Sharia Economic Perspective

The agricultural sector plays an important role in long-term economic development and rural economic recovery in Indonesia. The development of agricultural-based businesses and sustainable agricultural systems is needed to improve the quality of human resources and agricultural products. Horticultural commodities, such as fruit, have high economic potential and can be a source of income for farmers. However, there are several obstacles in the agricultural sector, such as competition with other horticultural products, low quality and quantity of local supplies, and a lack of transparent market information. In addition, distribution in Islam has principles that can be applied in the context of the distribution of wealth and economic resources in Islam, such as justice, equality, public benefits, prohibition of exploitation, and social solidarity. This research aims to analyze pineapple marketing distribution and pineapple marketing efficiency from a sharia economic perspective. The research method used is field research using primary and secondary data. The research results show that there are two marketing distribution channels that are used, namely marketing distribution channel I and marketing distribution channel II. Marketing distribution channel I involves farmers selling directly to the city without intermediaries, while marketing distribution channel II involves collectors buying from farmers and selling to the city. Keywords: Distribution, Efficiency, Marketing

Study of the Impact of New Rural Cooperatives on the Implementation of the Rural Revitalisation Strategy

This study aims to explore the impact of new rural cooperatives on the implementation of China's rural revitalisation strategy, focusing on the roles and challenges of new cooperatives in promoting agricultural modernisation, facilitating the optimisation of socio-economic structures, and achieving an ecological civilisation. Through literature review, case study analysis and field research, this study comprehensively assesses the importance and impact of new rural cooperatives in rural revitalisation. The study finds that new rural cooperatives have significantly improved agricultural productivity and product quality and contributed to agricultural modernisation by pooling farmers' efforts and introducing modern agricultural technologies and management models. These cooperatives have also contributed to the optimisation of rural socio-economic structures by providing employment opportunities, skills training and increasing farmers' incomes. In addition, co-operatives have demonstrated a positive role in promoting rural ecological environment protection and sustainable development, such as promoting green agriculture, conserving resources and protecting the ecological environment. At the same time, this study also points out the challenges faced by new rural co-operatives in the process of development, including management and operational efficiency, market adaptability, balance between environmental protection and sustainable development, fund raising and resource allocation. In response to these challenges, the study proposes a series of countermeasures, such as strengthening the internal management of cooperatives, broadening market channels, promoting environmentally friendly agricultural practices, enhancing the effective use of funds and resources, and strengthening policy support and guidance. Overall, new rural cooperatives play an important role in promoting rural revitalisation strategies, and their successful experiences and challenges are of great reference value in guiding the implementation of rural revitalisation strategies. Through an in-depth study of new rural cooperatives, this paper provides practical insights and suggestions for policymakers, cooperative managers and rural development researchers.

Analysis of the heavy metals and Escherichia coli contamination of liquid organic fertilizers in the local market, Sri Lanka

Commercially available liquid organic fertilizers have gained popularity in agriculture due to their convenience and perceived environmental benefits. However, these fertilizers are susceptible to various contaminations, including heavy metals such as lead (Pb) and cadmium (Cd), as well as bacterial contaminants like Escherichia coli (E. coli). Monitoring these contaminants helps to identify potential health risks to consumers, prevent soil and water pollution, and maintain the quality of agricultural products. Therefore, this study involved a comprehensive analysis of Pb and Cd heavy metals and E. coli bacterial contaminants in two widely used liquid organic fertilizers in the local market. The samples were digested using an open-vessel acid digestion followed by the flame atomic absorption spectroscopic (FAAS) analysis. The limit of detection (LOD), limit of quantification (LOQ), matrix effect, and spike recovery were determined. The Pb concentration of one fertilizer was 3.45 (± 0.07) mg/kg with 92% recovery whereas the other was 2.87 (± 0.08) mg/kg with 91% recovery. In contrast, the Cd concentration was 0.09 (± 0.01) mg/kg and 0.11 (± 0.01) mg/kg with percentage recoveries of 93% and 90% for two fertilizers respectively. The Pb content exceeded the maximum permissible limit of 1.0 mg/kg set by the Sri Lanka Standard (SLS 1702:2121), while Cd levels remained within the limit of 0.5 mg/kg. Polymerase chain reaction (PCR) coupled with gel electrophoresis confirmed the presence of the pathogenic O157:H7 E. coli strain in both fertilizers. Thus, the results of this study confirmed the lead (Pb) and Escherichia coli contamination in liquid organic fertilizers in the local market of Sri Lanka.

The Enabling Effect of Digital Economy on High-Quality Agricultural Development-Evidence from China

The Enabling Effect of Digital Economy on High-Quality Agricultural Development-Evidence from China

Analysis of the heavy metals and Escherichia coli contamination of liquid organic fertilizers in the local market, Sri Lanka

Commercially available liquid organic fertilizers have gained popularity in agriculture due to their convenience and perceived environmental benefits. However, these fertilizers are susceptible to various contaminations, including heavy metals such as lead (Pb) and cadmium (Cd), as well as bacterial contaminants like Escherichia coli (E. coli). Monitoring these contaminants helps to identify potential health risks to consumers, prevent soil and water pollution, and maintain the quality of agricultural products. Therefore, this study involved a comprehensive analysis of Pb and Cd heavy metals and E. coli bacterial contaminants in two widely used liquid organic fertilizers in the local market. The samples were digested using an open-vessel acid digestion followed by the flame atomic absorption spectroscopic (FAAS) analysis. The limit of detection (LOD), limit of quantification (LOQ), matrix effect, and spike recovery were determined. The Pb concentration of one fertilizer was 3.45 (± 0.07) mg/kg with 92% recovery whereas the other was 2.87 (± 0.08) mg/kg with 91% recovery. In contrast, the Cd concentration was 0.09 (± 0.01) mg/kg and 0.11 (± 0.01) mg/kg with percentage recoveries of 93% and 90% for two fertilizers respectively. The Pb content exceeded the maximum permissible limit of 1.0 mg/kg set by the Sri Lanka Standard (SLS 1702:2121), while Cd levels remained within the limit of 0.5 mg/kg. Polymerase chain reaction (PCR) coupled with gel electrophoresis confirmed the presence of the pathogenic O157:H7 E. coli strain in both fertilizers. Thus, the results of this study confirmed the lead (Pb) and Escherichia coli contamination in liquid organic fertilizers in the local market of Sri Lanka.

OPTIMASI OPTIMASI PROSES PEMBERSIHAN BERAS: STRATEGI TERAPAN UNTUK PENINGKATAN KUALITAS HASIL PERTANIAN

This community service activity (PKM) aims to introduce and train the community to process their rice harvests using appropriate technology, in the form of a grain peeling machine. The main target of this PKM activity is the people of the Jaya Mandiri Farmers Group in Batu Layang Village RT.04/RW.15, North Pontianak District, who still have limited means for their agricultural activities. The methods applied in this PKM activity are science and technology diffusion, socialization, and active community participation. So that with this PKM activity, the community has gained knowledge and skills in processing their post-harvest rice products, and it is hoped that this can help overcome their productivity problems due to limited agricultural facilities. Cleaning rice not only aims to remove empty grains, dirt and other foreign objects, but also increases the selling value per unit weight, increases the efficiency of drying and processing post-harvest products and extends shelf life. The working principle of the automatic rice cleaning tool is that when the rice is in the main reservoir, it then goes down through a small gap so that when it comes down it is cleaned with a fan so that the dirt stuck to the rice is blown away and the clean rice enters the reservoir section. The construction of this rice cleaning machine is divided into a hoper as a place to input rice, a crankshaft system, while the drive system consists of an electric motor, pulley, belt, pegs, bearings. With this device, the process of cleaning rice can be carried out simply and easily, and the process time, namely cleaning time, is shorter. With a shorter processing time, the production rate per unit time becomes greater. Improving the quality of agricultural products is very important in supporting food security and farmer welfare. One of the key aspects in ensuring the quality of agricultural products is the post-harvest processing process, especially the rice cleaning process. In this context, this research aims to optimize the rice refining process through the implementation of effective applied strategies. This study was conducted through a comprehensive series of laboratory experiments and field tests. Refining methods involving a combination of traditional and innovative technologies were evaluated to find the most efficient strategy for improving rice quality. The research results show that the use of a combination of certain technologies can increase the efficiency of rice cleaning, as well as reduce yield losses and increase the selling value of the product. This research makes a significant contribution to the development of agricultural practices that are sustainable and oriented towards quality results. These findings have direct implications for farmers in increasing the competitiveness of their products in local and global markets, as well as in supporting government efforts to achieve national food security goals.

Efficient residual network using hyperspectral images for corn variety identification.

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.

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.

Effect of roasting on the total polyphenol content and antioxidant activity of sesame (Sesamum indicum L.) seeds grown in Ethiopia

Consumers prioritize safe and high-quality foods, underscoring the importance of ensuring the quality of agricultural products to meet their expectations. Sesame (Sesamum indicum L.) is the most widely grown food crops, cultivated in different cultivars and types throughout the world. It is the second most valuable crop next to coffee in Ethiopia's foreign exchange earnings. The present study aimed to determine the total phenolic content and antioxidant activity of raw and roasted sesame seeds from seven major sesame-producing areas in Ethiopia. The total phenolic and flavonoid contents of raw seeds ranged from 67 to 154 mg GAE/100 g DW and 19–43 mg CE/100 g DW, with DPPH scavenging activity between 0.55 and 0.85 mg AAE/g. Conversely, roasted seeds had average phenolic and flavonoid levels, as well as antioxidant activity, averaging 56–97 mg GAE/100 g DW, 14–21 mg CE/100 g DW, and 0.63–1.04 AAE/g, respectively. Significant differences (p < 0.05) were observed between the raw and roasted seeds in these parameters. The roasting process led to a decline in polyphenol and flavonoid contents, likely due to heat-induced degradation and/or alteration of these compounds. However, the roasted seeds exhibited enhanced antioxidant properties, probably ascribed to the formation of new substances having antioxidant activities. The study suggests further investigation into optimal roasting conditions to minimize polyphenol loss.

Performance of vision transformer and swin transformer models for lemon quality classification in fruit juice factories

AbstractAssessing the quality of agricultural products holds vital significance in enhancing production efficiency and market viability. The adoption of artificial intelligence (AI) has notably surged for this purpose, employing deep learning and machine learning techniques to process and classify agricultural product images, adhering to defined standards. This study focuses on the lemon dataset, encompassing ‘good’ and ‘bad’ quality classes, initiate by augmenting data through rescaling, random zoom, flip, and rotation methods. Subsequently, employing eight diverse deep learning approaches and two transformer methods for classification, the study culminated in the ViT method achieving an unprecedented 99.84% accuracy, 99.95% recall, and 99.66% precision, marking the highest accuracy documented. These findings strongly advocate for the efficacy of the ViT method in successfully classifying lemon quality, spotlighting its potential impact on agricultural quality assessment.

AI Driven Applications for Precision Weed Management in Agricultural Crops

Agriculture plays a most important role in our Indian economy and therefore lowering the cost of production and improving the quality of agricultural products is highly demanded. A weed is a plant which grows in wrong place at the wrong time and doing mostly harm than crops. Weed competes with the crops for water, light, nutrients and space, and therefore it prevents crop yields. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology to prevent these weeds. Machine learning technologies, are becoming crucial in agriculture to increase productivity, where advanced automation and control have been required. Based on large training datasets and pre-trained models, (Deep Learning) DL-based Convolutional Neural Networks (CNN) methods have proven to be more accurate than previous traditional techniques. Recently, Deep Learning (DL) has gained much attention due to its advantages in object detection, classification, and feature extraction. The system implementation of image processing technique for weed detection, a trained image is taken as a sample in order to demonstrate the difference between weed and the crop. Yolo frame work is used for annotate boundary boxes to the image with datasets. The effectiveness of the (You Only Live Once) YOLO-WEED system for real-time Unmanned Aerial Vehicle (UAV) weed detection, given its high speed and high accuracy in detection. After certain steps, we get desired output, where the weeds are separated from the crop that has been taken in the sample image. Key Words: DeepLearning,CNN, Unmanned Aerial Vehicle (UAV), Precision Weed Detection.

US-China trade friction and agricultural nitrogen loss in China

Agricultural production, trade and environmental quality are interdependent. The US and China have been the world largest trade partners. However, trade tensions and interest in protectionist policies intensified in recent years. We use a price endogenous partial equilibrium model to systematically examine the impact of increased Chinese tariff on agricultural imports from the US on nitrogen (N) loss in China. The results show that a 25% tariff on US soybean imports increases the acreage of major crops in China by 1.37%. Although aggregate crop acreage increases, the impact on cumulative nitrogen fertilizer uses and the corresponding N loss from cropland is not significant as less N-intensive soybean acreage replaces some of the more N-intensive crops like wheat. However, our estimates show significant heterogeneity in N loss across Chinese provinces, ranging from 0.05% to 2.97% increase. These results identify provinces where remediation measures may be needed to effectively manage the potential increase in nitrogen loss associated with greater soybean production.

Research of blockchain-embedded agricultural quality credit regulation influencing factors

PurposeThe credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.Design/methodology/approachUsing the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.FindingsThe results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.Originality/valueCredit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

Development of a Monoclonal Antibody-Based Indirect Competitive Enzyme-Linked Immunosorbent Assay for the Rapid Detection of Gallic Acid.

Gallic acid (GA) is closely related to the quality of herbal medicines and other agricultural products. In order to facilitate the rapid detection of GA, we developed a monoclonal antibody-based ic-ELISA method. Antigens with and without connecting arms were prepared. It was found that the introduction of connecting arms (linear carbon chain) was beneficial for immune response. By utilizing hybridoma technology, a specific mAb (anti-GA-M702) was screened and identified, which exhibited a 1:40,500 antibody titer and IgG2b antibody subtype. The ic-ELISA assay was established based on anti-GA-M702. The optimal working concentrations of the encapsulated antigen and antibody were 0.5 µg/mL and 0.67 µg/mL, respectively. The ic-ELISA method showed a linear detection range of 297.17-2426.61 ng/mL for GA with a sensitivity of 849.18 ng/mL. It displayed a good applicability for the determination of GA in Galla chinensis. In conclusion, the ic-ELISA method provides an efficient approach to the rapid detection of GA in products.

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.

Behavioral decision-making of government, agricultural product producers, and consumers on agricultural product quality and safety regulation in a digital environment.

The quality and safety of agricultural products are related to people's lives and health, economic development, and social stability, and have always been a hot issue of concern to the government and society. The rapid development of digital traceability technology in the digital environment has brought new opportunities for the supervision of agricultural product quality and safety, but the frequent occurrence of agricultural product safety incidents in recent years has exposed many problems such as the lack of governmental supervision, unstandardized production process of enterprises, and weak consumer awareness. To improve the cooperation efficiency of stakeholders and ensure the quality and safety of agricultural products, this paper proposes a dynamic model based on evolutionary game theory. The model incorporates the government, agricultural product producers, and farmers, and evaluates the stability and effectiveness of the system under different circumstances. The results of the study show that there are multiple evolutionary stabilization strategies in the tripartite evolutionary game model of agricultural product quality and safety supervision, and there are corresponding evolutionary stabilization conditions. There are several factors affecting the stability of the system, the most important of which are government regulation, severe penalties for agricultural product producers, and incentives. When these factors reach a certain threshold, the stakeholder cooperation mechanism can establish an evolutionarily stable strategy. This study contributes to the understanding of the operational mechanism of stakeholder cooperation in agricultural product quality and safety regulation in the digital environment and provides decision support and policy recommendations for stakeholders to promote the sustainable development and optimization of agricultural product quality and safety regulation.

An Overview of Agricultural Supply Chain and Customization

The promotion of national policies and the progress of information technology have made customized agriculture possible. Compared with contract farming, customized agriculture can not only guarantee the income problem of farmers, but also better guarantee the quality of agricultural products and close to the market demand. This paper systematically reviews the current research status of agricultural supply chain and customization, summarises the shortcomings of the existing research, and points out the direction for the subsequent research.

Crater-Spectrum Feature Fusion Method for Panax notoginseng Cadmium Detection Using Laser-Induced Breakdown Spectroscopy.

Panax notoginseng (P. notoginseng) is a valuable herbal medicine, as well as a dietary food supplement known for its satisfactory clinical efficacy in alleviating blood stasis, reducing swelling, and relieving pain. However, the ability of P. notoginseng to absorb and accumulate cadmium (Cd) poses a significant environmental pollution risk and potential health hazards to humans. In this study, we employed laser-induced breakdown spectroscopy (LIBS) for the rapid detection of Cd. It is important to note that signal uncertainty can impact the quantification performance of LIBS. Hence, we proposed the crater-spectrum feature fusion method, which comprises ablation crater morphology compensation and characteristic peak ratio correction (CPRC), to explore the feasibility of signal uncertainty reduction. The crater morphology compensation method, namely, adding variables using multiple linear regression (MLR) analysis, decreased the root-mean-square error of the prediction set (RMSEP) from 7.0233 μg/g to 5.4043 μg/g. The prediction results were achieved after CPRC pretreatment using the calibration curve model with an RMSEP of 3.4980 μg/g, a limit of detection of 1.92 μg/g, and a limit of quantification of 6.41 μg/g. The crater-spectrum feature fusion method reached the lowest RMSEP of 2.8556 μg/g, based on a least-squares support vector machine (LSSVM) model. The preliminary results suggest the effectiveness of the crater-spectrum feature fusion method for detecting Cd. Furthermore, this method has the potential to be extended to detect other toxic metals in addition to Cd, which significantly contributes to ensuring the quality and safety of agricultural production.

Smart Farming: Bridging Farmers and Consumers through Machine Learning-Enabled E-commerce Platforms

Abstract: This research paper proposes the development of an e-commerce platform tailored specifically for farmers, integrating machine learning algorithms for fruit detection and classification, alongside blockchain technology for enhanced authentication. The platform aims to streamline the agricultural supply chain, facilitating direct transactions between farmers and consumers while ensuring product quality and authenticity. The machine learning models will enable automated fruit recognition and categorization, allowing farmers to efficiently showcase their produce online. Additionally, blockchain technology will provide a secure and transparent framework for verifying the origin and quality of agricultural products, fostering trust among buyers. The synergistic combination of machine learning and blockchain holds promise for revolutionizing the agricultural sector, promoting fair trade practices, and empowering farmers in the digital marketplace.