Quality Of Agricultural Products Research Articles

Analysis in Terms of Environmental Awareness of Farmers’ Decisions and Attitudes: Reducing Pesticide Use and Risks

Pesticide residues are a significant problem affecting the quality and safety of agricultural products in Turkey. This study aims to investigate farmers’ risk perception regarding pesticide residues, including the primary factors that influence their behavior from the farmers’ perspective. In addition, the main characteristics of pesticide residues encountered in the production of agricultural products against the current policy background is investigated to provide decision support to the Turkish government for improving the safe production of agricultural products. This paper uses a binary logistic model to analyze farmers’ perceptions of pesticide residues using a cross-sectional dataset of 323 vegetable growers in Bursa, Turkey. Farmer characteristics and pesticide application behavior were more effective in reducing residual risk perceptions than farm characteristics. The effects of membership in a cooperative, average amount of pesticide used, location where the pesticide was purchased, application timing, and adherence to last spraying and harvest timing on the perception of residues were the largest. Farmers receive helpful but limited information from pesticide distributors. However, the primary source of information is not the advisors, which is why these grievances exist. The study results show that policymakers should support cooperatives, expand pesticide training, and make public extension services more effective and that pesticide dealers should conduct more frequent inspections.

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.

Fresh Agricultural Products Cold Chain Logistics Development Status and Countermeasures Research

With the improvement of the living standard of consumers in China, people are more and more concerned about the demand and quality of fresh agricultural products, and the market scale of cold chain logistics of fresh agricultural products is growing explosively. However, China's cold chain logistics still exists such as insufficient cold chain logistics infrastructure, low level of information technology, high cost, low degree of standardization of fresh agricultural products production and other problems, resulting in China's fresh agricultural products cold chain logistics is facing serious challenges. This paper analyzes the development status quo of cold chain logistics and puts forward some solutions to provide some references for subsequent research.

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

Investigation of pesticide residues based on integrated multi-strategy: occurrence, fate and dietary risk assessment

To gain a comprehensive overview of the risk of pesticide intake exposure posed by vegetables from farms, a joint multi-strategy of pesticide monitoring was conducted on vegetable and edible mushroom samples from planting bases. The strategy integrated traditional methods and non-targeted screening by proposing modified rapid standard pre-treatment methods combined with GC, HPLC-MS and HPLC-HRMS for screening and quantification of known pesticides and unknowns. The results showed that 40 pesticides were detected at different levels in 308 samples by targeted monitoring and 27 pesticides were confirmed by non-targeted screening, of which 8 samples were detected at levels exceeding the defined maximum residue limits. The risk of acute and chronic dietary exposure for the detected pesticides in eleven groups of vegetables was acceptable. Comparative analysis of targeted and multi-strategies revealed that farm-based cultivation can improve the quality and safety of non-certified products, and pesticides with low ADIs confirmed via the multi-joint risk monitoring strategy will significantly increase the risk of cumulative chronic dietary exposure. These results will help to improve the quality and safety of agricultural products at the source and the monitoring and management of pesticides in planting bases.

Selective and efficient immobilization of cadmium in soil by layered double hydroxides intercalated with the mercaptosuccinic acid

Cadmium (Cd) contamination in cropland poses a significant threat to the quality of agricultural products, but even though in-situ remediation has been extensively applied, non-selective immobilization remains an issue. In order to develop a material that specifically immobilizes Cd in soil, a layered double hydroxide, intercalated with mercaptosuccinic acid (MSA-CFA), was synthesized through co-precipitation. In this case, the MSA-CFA's maximum adsorption capacity was increased from the 513.8 mg·g−1 for unintercalated hydrotalcite CFA to 692.6 mg·g−1. Besides, MSA-CFA efficiently removed 99.25 % of Cd from soil water-extract solution and immobilized up to 70.03 % of bio-available Cd. However, interestingly, its immobilization effects on beneficial metal elements Fe, Mn and Zn were milder, being equivalent to 2/7, 5/7 and 1/2 that of lime, respectively. Moreover, XRD and XPS techniques revealed isomorphous substitution with calcium and sulfhydryl complexation during the Cd adsorption by MSA-CFA. Compared with CFA, the increased adsorption capacity of MSA-CFA for Cd was due to intercalated MSA acting as a new adsorption site, while the enhanced selectivity was contributed by sulfhydryl's affinity for Cd. Altogether, MSA-CFA showed great promise as a competitive and highly efficient candidate amendment in Cd-contaminated soil remediation.

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.