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In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

Leafy vegetables are consumed in most daily xdiets worldwide. As living standards improve, food quality, safety requirements, and nutrition are becoming increasingly important to consumers when purchasing leafy vegetables. This study proposes an evaluation and traceability method that can be used to track the nutritional quality of leafy vegetables. Employing the principles of the Hazard Analysis and Critical Control Point (HACCP) system combined with fault tree analysis (FTA), a traceability model for the entire production and sale process of leafy vegetables is constructed. Four common leafy vegetables, spinach, rape, lettuce, and celery are examined in this research to establish a nutritional quality index system using fuzzy mathematics subordinate function method to evaluate nutritional quality. A nutritional quality and safety traceability system based on browser/server architecture and quick response (QR) code is then designed and developed for full traceability of leafy vegetable quality. This method can ensure food safety and hygiene through the control of key factors affecting food safety throughout the entire supply chain process.

Purpose The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods. Design/methodology/approach First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data. Findings The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R2 is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R2 are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties. Originality/value This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

There is a global shortage of fresh water to meet the clean production of field crops. Water-saving irrigation can increase the production of field crops, but under certain conditions it can be overly water-saving and unsustainable, particularly in complex terrains in China where there is already a shortage of water resources. In light of the insufficient water resources and partial inequality in China, several investigators in China and abroad have researched irrigation nozzles and pumps, but there is little information on the impact of the environment on crop irrigation. The purpose of this study was to provide a fast and effective crop water demand prediction model to estimate the water requirement during the growth period of crops, to achieve the purpose of applying water according to the demand of the crops, and to optimize the irrigation network to determine the optimal network structure in order to reduce irrigation energy consumption in the process. Specifically, this study analyzed the environmental factors, namely, the soil moisture, soil electrical conductivity, air temperature, and light intensity, affecting the physiological water demand of field crops. Relevant sensors were used in the field, and environmental information was collected. This study used the collected environmental data to build a water demand prediction model based on the back propagation (BP) neural network. It also carried out optimized layout and hydrodynamic analyses of the drip irrigation network and identified the best pipe network arrangement. On the basis of the BP neural network, the remarkable R amount of 0.98963 was obtained for the water demand prediction model. The mean square error (MSE) value was 0.00857724. We also found that the drip irrigation pipe network in the H-shaped arrangement was more suitable for field crop irrigation than the comb-shaped and fish bone-shaped arrangements. This study employed the common field crops in China as the research object, used the wireless sensing technology to obtain the field environment information quickly, and constructed a water demand prediction model with high prediction accuracy. Finally, this study realized the precise control of field crop irrigation, reduced the waste of agricultural water, and achieved sustainable field crop production.

The purpose of this article is to examine the impact of rural population aging on farmers’ cleaner production behavior through the intervening effects of three aspects (learning capacity, factor substitution and behavior imitation). The data used in this article were collected from a questionnaire survey of 916 farmers in the main grain producing provinces of the North China Plain. First, confirmatory factor analysis was carried out, and then data were analyzed through structural equation modeling using the bootstrap methodology in AMOS 24. The findings indicate that rural population aging inhibits farmers’ cleaner production behavior through learning capacity. In contrast, rural population aging promotes farmers’ cleaner production behavior through factor substitution and behavior imitation. From the perspective of both challenges and opportunities, this article investigates the impact mechanism and comprehensive effect of rural population aging on farmers’ cleaner production behavior, so as to explore new ways to promote cleaner agricultural production under the background of an accelerated aging the of rural labor force and provide reference for the formulation of relevant policies.

Against the background of the depletion of fossil fuel energy sources and environmental degradation, solar greenhouses represent an efficient and sustainable agricultural production method in China. The precise measurement and control of the position of the rolling shutter and rolling film is not only one of the core issues in the modification of the greenhouse environment in actual production but also the basis for improving the cleaner production efficiency of solar greenhouses. In this study, a mathematical algorithm for the control of a rolling shutter and rolling film was developed and is based on the simplification of the circular arc of the greenhouse surface. The “Liaoshen-IV” solar greenhouse was used as an example to illustrate the calculation steps of the algorithm. Any position on the light surface can be determined by calculating the angle at the bottom of the back wall of the greenhouse so that the position of the rolling shutter and the rolling film can be measured accurately by the angle sensor. Experiments using a scale model with one or two surface segments were conducted to demonstrate the advantages of the control algorithm with regard to the accuracy and stability compared with the existing control method of a time-delay relay. The relative errors of the control algorithm were stable and less than 1%. The use of the algorithm improved the control accuracy of the rolling shutter and rolling film and the average relative error was reduced by 6.37% in the first stage and by 3.40% in the second stage compared with the time-delay relay. The precise measurement and control of the position of the rolling shutter and rolling film in a solar greenhouse demonstrated in this study has great significance for practical applications. The proposed method may result in a reduction in energy consumption and greenhouse gas (GHG) emissions in greenhouse environments, thereby contributing to the improvement of resource efficiency and a cleaner production. The study also provides application examples and theoretical references for the automation of rolling shutters and rolling film in greenhouses worldwide.

The Chinese solar greenhouse (CSG) ensures farming activities to continue and allow the harvest timing to be manipulated and become an important part of Chinese agricultural facilities. Effective control of the solar greenhouse environment is critical for the well-being of crops and is important for the entire Eco-agricultural System. The rapid exchange of energy and materials in the CSG with the external environment are achieved through the natural ventilation of the upper vent and the lower vent. However, there is lack of relevant research that addresses the vent opening. In this study, the effect that the vent opening has on the CSG was studied through Computational Fluid Dynamics (CFD) simulation. The ventilation of a real solar greenhouse was simulated by establishing a 3D simulation model of the solar greenhouse with various vent openings. The main focus was the relationship between the vent opening and the air humidity changes in the CSG. This study presents a coupling between an energy balance model and a CFD model to study relationships between ventilation and humidity in a CSG. The simulation results demonstrated that natural ventilation increased along with the increasing of vent openings, which caused to a drop of air humidity and temperature in the solar greenhouse. This reduction was not a linear change in the traditional cognitive paradigm, but showed nonlinear changes through the simulation results. The requirements for the verification of simulation results were used to conduct a scale model to verify the results of the CFD experiments. The practical value and the significance of the research aims to provide basic reference for controlling the indoor air temperature and the air humidity by the natural ventilation of vents. The outcome could result in a reduction in energy consumption and GHG emissions in the greenhouse operations, which contributed to the improvement of resource efficiency and a cleaner production. These results have strong practical significance for the follow-up automation and intelligent control of CSG environments.

Purpose The selection of marketing channels by vegetable producers directly affects the income of producers and is important for the maintenance of a stable supply of vegetables and food control. The purpose of this paper is threefold: to investigate the cooperative selection of vegetable marketing channels; to identify the factors that influence the selection of marketing channels by professional vegetable cooperatives by comparing emerging and traditional circulation modes; and to solve the problems related to vegetable circulation channels in Beijing. Design/methodology/approach A total of 187 valid questionnaires were collected from seven main vegetable production districts in Beijing urban areas from September to December 2017, with a response rate of 89 percent. Binary logistic regression was used for analysis in this study. Findings Results revealed that the cooperatives mainly selected large wholesalers, wholesale markets, supermarkets and electronic commerce as their marketing channels for their vegetables. Estimation results showed that among the 18 influencing factors in the four categories, the educational level of the person in charge and some other factors significantly influence the selection of these four distribution channels by the cooperatives. Research limitations/implications Due to the lack of time and energy, this paper does not analyze the factors influencing a cooperative’s choice of different e-commerce platforms. If this problem can be solved, it will definitely promote the development of e-commerce in rural areas. Originality/value The results obtained in the present study and their implications could help policy makers establish a science-based and reasonable policy to encourage vegetable producers to participate in the new circulation modes of vegetables in Beijing and ensure their income in the vegetable supply chain. This study suggests methods to improve the vegetable sector in other cities facing similar issues.

The purpose aims to examine the key factors influencing Chinese consumer’s purchasing behaviour of eco‐friendly food in China giving its context as an emerging economy and its rapidly rising importance in the world eco‐friendly food market. This paper adopts and extends the Responsible Environmental Behaviour (REB) theory by empirically testing key psychosocial factors influencing the purchase intention of eco‐friendly food and the moderating effects of consumers’ demographic characteristics on the relationship between the key psychosocial factors and the purchase intention. A number of hypotheses are proposed. A questionnaire was designed and distributed via online survey in Beijing, China. A total of 239 valid responses were received. The empirical data were used to test the research hypotheses using the hierarchical multiple regression analysis. The research finds that the personality factors in the REB model (i.e., pro‐environmental attitudes, the internal locus of control and personal responsibly) have significant positive effects on the consumers’ eco‐friendly food purchase intention. Such effect is stable across consumers with different income levels. On the other hand, the knowledge–skill factors in the REB model do not have significant effect on the purchase intention of consumers. This study contributes to a better understanding of factors affecting eco‐friendly food consumption intention in China and the behavioural characteristics of consumers in developing countries. Moreover, the findings also shed light on the applicability of the REB theory in emerging economies and a specific industrial context.

Automatic visual inspection (AVI) is a popular tool for object detection and analysis in many fields. In fisheries, AVI systems designed for fish egg development analysis are used for fish egg assessments in commercial and experimental fish production systems and in ecological monitoring. The first step in AVI systems is to subdivide an image into meaningful regions. The accuracy of this segmentation directly influences the success of subsequent image analysis operations such as feature selection and classification. At present, a lot of image segmentation techniques are available, but there is no universal segmentation technique suitable for all kinds of images.This paper presents a cascading automatic segmentation method for microscopic digitised images of live common carp (Cyprinuscarpio) eggs in Petri dishes filled with water. The proposed method involves five main steps:1) image pre-processing, 2) image initial segmentation by Otsu’s method, 3) segmentation post-processing based on morphological operations, 4) image edge detection and morphological operations to remove incomplete eggs, and finally 5) a Watershed method was used to resolve the problem of separating adjoining fish eggs. Testing the method in Matlab software verified that fish eggs were recognized, segmented and counted with 100% accuracy compared to manual observation in all 96 test cases.

Analysis of plant disease images can increase disease detection accuracy. Successful extraction of lesions can provide a good precondition for research on the feature analysis (texture, color and shape) and detection of occurrence area and severity for cucumber powdery mildew. This paper proposes a new Otsu algorithm based on three-dimensional histogram reconstruction and dimension reduction to segment powdery mildew from cucumber disease images. In total, 166 RGB images of cucumber powdery mildew with 1440 × 1080 pixels were obtained from the greenhouse nos. 1 and 2 using a high-speed dome camera. First, a new correction formula is proposed to correct anomalous points to the correct position (around the line connecting the origin with diagonal end). Second, we reduced the algorithm dimension to save time and spatial complexity and achieved the desired results. Finally, the optimal threshold was obtained by a Gaussian fitting iteration. Convergence analysis demonstrated that the method should be able to obtain a new threshold after each iteration. Experimental results showed an average false negative error rate of 0.10% and average false positive error rate of 1.27%. The average running time was 5.082 s. Taken together, these represent satisfactory results for rapid automatic identification of cucumber powdery mildew.

Water scarcity has threatened the food security and been a critical concern in China. Promoting modern agricultural irrigation technologies has been identified as an important measure against water scarcity. The overall goal of this study was to analyze the adoption of water-saving irrigation technology by farmers and to identify the major influencing factors of this decision for metropolis suburbs. Based on a field survey of Beijing of China, the results showed that 53.1% of farmers adopted water-saving irrigation technologies to cope with water scarcity, most of which adopted engineering water-saving technologies. The number of adopted water-saving irrigation technologies followed a strong negative correlation with the share of adopters. Econometric analysis revealed that education, farm size, on-farm demonstration, cooperative, training, groundwater, access to information, water use associations, drought-prone area, neighboring farmers, and policy subsidies significantly improved the adaption to water scarcity. Age, production specialization, and cost posed a negative effect on famers’ adoption of water-saving irrigation technologies. These results and implications provide an understanding of farmers’ sustainable irrigation practices and offer an insight to influencing factors to frame improved strategies and policies that enable to cope with water scarcity of metropolis suburbs.

Toxic and hazardous sulfur dioxide (SO2) residue reduction is not only one of the core issues in the preservation process with SO2 solid antistaling agents in actual table grapes cold chain, but also one of the important ways to improve the cleaner production efficiency of the table grapes. The aim of this paper is to propose an improved preservation process for the table grapes in cold chain by combining the fumigation treatment with the slow-release SO2 solid antistaling agent. The process was performed to reduce the toxic and hazardous SO2 residue of the table grapes, improve the cleaner production efficiency of the cold chain, guarantee the quality and safety of the table grapes, and finally enhance the human health. The slow-release SO2 solid antistaling agent was developed with no pricking holes and relatively stable release rate of the SO2. According to the comprehensive analysis of the fumigation treatment condition determination, the slow-release SO2 solid antistaling agent performance, the table grapes quality performance, the SO2 residue performance and the evaluation on improved preservation process, the proposed improved preservation process with SO2 residue reduction could effectively reduce the bleaching injury, the decay rate, the threshing rate and the toxic and hazardous SO2 residue of the table grapes. There was no bleaching injury after the fumigation treatment at the optimal fumigation treatment conditions with 5000 ppm fumigation concentration and 20 min fumigation time. The maximization reduction range of the average decay rate and the average threshing rate of the table grapes was about 91.3% and 44.8%, while the maximization reduction range of the average SO2 residue in table grapes peels was about 44.7%. The proposed improved preservation process could be extended for other cold chain applications to reduce their SO2 residue and improve their cleaner production efficiency.

China is the largest producer and consumer of vegetables, its vegetable industry is playing an important role in the domestic agricultural sector and global vegetable export market. It is important to promote the long-term sustainable development of Chinese vegetable industry for food security and quality of people’s lives. To find out the intrinsic way to promote the development of Chinese vegetable industry, this paper analyzed the influencing factors of Chinese vegetable production by utilizing the LMDI method and demonstrated the spatial-temporal characteristics of vegetable production through application of the ArcGIS spatial autocorrelation analysis method. The results showed that the influencing factors of vegetable production were the cultivated land area, multiple cropping index, vegetable planting proportion and vegetable yield per hectare in China. The major driving factor had changed from vegetable planting proportion to vegetable yield per hectare. The influencing degrees of factors on vegetable production are different in different regions, regionalization is therefore a major feature of Chinese vegetable production. The government should take production technology, regionalization-driven effect, and marketing integration into consideration to promote the development of Chinese vegetable industry. Keywords: Vegetable production, Influencing factors, Contribution ratio, Logarithmic mean Divisia index (LMDI)

Safe food is healthy, nutritious and environmentally friendly by green, sustainable and clean production. The objectives of this study were to examine factors influencing the consumers' purchase intention and willingness to pay a premium price for safe vegetables. To achieve the objectives, this paper developed the model of consumers' purchase safe vegetables. Based on 840 valid questionnaires from Beijing of China, the model was confirmed by the logistic regression method. The results showed that food safety, strict production and processing, and healthy environment were the top three perceived advantages of safe vegetables for consumers. In addition, 93.0% and 87.1% of consumers were familiar with organic vegetables and green vegetables, respectively. Two-thirds of consumers identified safe vegetables via certification labels on vegetable packages. Furthermore, 67.6% of consumers were willing to buy safe vegetables and 65.8% would pay a premium price for safe vegetables. Meanwhile, consumers' purchase intentions for safe vegetables were positively affected by family food expenditure, children, familiarity, differential cognition, safety awareness, nutritional health, packaging, label trust, and online shopping experience but price level, safety status, and freshness had a negative effect. Consumers’ willingness to pay a premium price for safe vegetables was positively affected by family income, familiarity, differential cognition, safety awareness, nutritional health, packaging, label trust, and online shopping experience but family food expenditure, price level, price fluctuations, and safety status had a negative effect. The findings of this study have important implications to improve the consumption and guide cleaner production of safe vegetables. The findings obtained can also provide references for other similar studies in other areas.

Growth-related traits, such as aboveground biomass and leaf area, are critical indicators to characterize the growth of greenhouse lettuce. Currently, nondestructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. In this study, a method for monitoring the growth of greenhouse lettuce was proposed by using digital images and a convolutional neural network (CNN). Taking lettuce images as the input, a CNN model was trained to learn the relationship between images and the corresponding growth-related traits, i.e., leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA). To compare the results of the CNN model, widely adopted methods were also used. The results showed that the values estimated by CNN had good agreement with the actual measurements, with R2 values of 0.8938, 0.8910, and 0.9156 and normalized root mean square error (NRMSE) values of 26.00, 22.07, and 19.94%, outperforming the compared methods for all three growth-related traits. The obtained results showed that the CNN demonstrated superior estimation performance for the flat-type cultivars of Flandria and Tiberius compared with the curled-type cultivar of Locarno. Generalization tests were conducted by using images of Tiberius from another growing season. The results showed that the CNN was still capable of achieving accurate estimation of the growth-related traits, with R2 values of 0.9277, 0.9126, and 0.9251 and NRMSE values of 22.96, 37.29, and 27.60%. The results indicated that a CNN with digital images is a robust tool for the monitoring of the growth of greenhouse lettuce.

Village rules and formal environmental regulations are of great significance for standardizing farmers’ cleaner production behavior, promoting green transformation of agriculture and realizing sustainable development of agriculture. Based on the survey data of 946 farmers in five provinces of China, taking seed coating technology, soil testing and formulated fertilization technology, subsoiling tillage technology, green technology for pest and disease control and straw returning technology as examples, this article empirically analyzes the impact of village rules and formal environmental regulations on farmers’ cleaner production behavior by using the multivariate probit model. When formal environmental regulations are relatively lacking or weak, village rules can be used as a useful supplement to formal environmental regulations to promote farmers’ participation in cleaner production. Based on this, this article argues that the important reason for formal environmental regulations falling into relative system failure is that village rules have not been paid enough attention in promoting farmers’ cleaner production behavior. In the future, we should not only continue to strengthen the role of formal environmental regulations in farmers’ cleaner production, but also cultivate the informal institution represented by the village rules, and build the regulatory system of mutual support between informal institution and formal institution.

Freshwater aquaculture is the main form of aquaculture in inland areas, and pollution is a significant challenge. The aim of this work was to propose an effective risk assessment method for aquaculture to predict pollutant bioaccumulation in fish tissue based on water environment variables. This method was designed to assess the risk of pollutant levels in fish tissue that exceed China’s food safety standards. Firstly, field observation and investigation were conducted to analyze the migration mechanisms of the main pollutants between fish tissue and the water environment. Secondly, a classification and regression tree (CART) and generalized additive model (GAM) nexus method was used to analyze the environmental variables related to pollutants in the water environment and establish prediction models. Finally, models of the bio-reaction kinetics were established to predict the pollutant bioaccumulation in fish tissue. The experimental results indicate that the GAM models could explain 73.7%, 84.4%, and 84.5% of the deviations for the three predicted pollutants. For 90% of the sample data, the accuracy of the bioaccumulation prediction was 93.44% for volatile phenol bioaccumulation, and 90.16% for Cu and coliform bioaccumulation. The proposed risk assessment method could be extended for aquaculture applications that require monitoring of additional parameters to safeguard aquaculture quality and facilitate effective safety risk management.

Improving the extraction of cucumber leaf spot disease under complex backgrounds.Redefining the feature distance between pixel xj and clustering center vi.Calculating the two-dimensional neighborhood mean gray value as a sample point.Proposing a new weighting method for gray value and neighborhood gray value. Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as xj2-vi2. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.

China is the world's largest energy consumer and is also leading in the solar photovoltaic industry. The solar energy industry, with advantageous clean and highly efficient production, is integral in maintaining the sustainable energy supply in an environmentally sensitive manner. Further development of the Chinese photovoltaic industry requires a status assessment for the current industrial setting. An analysis on the 58 Chinese photovoltaic listed enterprises is conducted in this study to analyze the operating performance, industry agglomeration and spatial characteristics of Chinese photovoltaic industry. Comprehensive analysis and evaluation of the enterprises’ operating performance are based on financial data by utilizing the Data Envelopment Analysis (DEA) method; Systematic analysis examines spatial characteristics through the application of the spatial autocorrelation analysis method. Results confirm the Chinese photovoltaic industry preliminarily appeared industry agglomeration with enterprises mainly distributed in east and north China, and gathered in developed coastal provinces. Superior performing photovoltaic enterprises are specifically located in north China. Most of the enterprises are in the up-stream or mid-stream of the photovoltaic industrial chain. Overall operating performance of the photovoltaic industry in China suffers from weak profits due to low technical efficiency. The key to improve the future development of these enterprises is the improvement of operating performance by strengthening technical efficiencies. Concurrently, governmental guidance should address profitable photovoltaic industry investments and improvement of production rates.

Aquatic products are well received by customers from around the world. Quality and safety of aquatic products have attracted increasing attention, especially in emerging economies. Cased by actual aquatic products cold chain logistics, this article introduces the efforts for developing and evaluating the intelligent monitoring system for aquatic products (IMS-CS): an intelligent traceability system integrated wireless sensor network with statistical process control. Observations, interviews, literature review and experts’ questionnaires were used for system requirement and knowledge acquisition; actual aquatic products cold chain logistics scenario was applied to evaluate and validate the performance of IMS-CS. The results show that that the IMS-CS could trace and monitor the real time temperature fluctuation and the quality of aquatic products, which improve the transparency of the cold chain logistics and enables more effective control of the traceability, quality and safety. The valuable suggestion indicates that the process was under medium risk level and had potential to be improved. Practical Applications The proposed method could be exploited widely by future researchers or practitioners in cold chain monitoring and traceability tasks.

Wireless Sensor Network (WSN) is applied widely in food cold chain logistics. However, traditional monitoring systems require significant real-time sensor data transmission which will result in heavy data traffic and communication systems overloading, and thus reduce the data collection and transmission efficiency. This research aims to develop a temperature Monitoring System for Frozen and Chilled Aquatic Products (MS-FCAP) based on WSN integrated with Compressed Sending (CS) to improve the efficiency of MS-FCAP. Through understanding the temperature and related information requirements of frozen and chilled aquatic products cold chain logistics, this paper illustrates the design of the CS model which consists of sparse sampling and data reconstruction, and shelf-life prediction. The system was implemented and evaluated in cold chain logistics between Hainan and Beijing in China. The evaluation result suggests that MS-FCAP has a high accuracy in reconstructing temperature data under variable temperature condition as well as under constant temperature condition. The result shows that MS-FCAP is capable of recovering the sampled sensor data accurately and efficiently, reflecting the real-time temperature change in the refrigerated truck during cold chain logistics, and providing effective decision support traceability for quality and safety assurance of frozen and chilled aquatic products.

With continuous rise of table grapes consumption and increased public awareness of food safety, the quality control of grapes in storage after purchase is not sufficiently examined. Home storage constitutes the last and important stage in grape supply chain. Literature review shows that few researches on grape quality focus on the home storage stage compared with numerous researches reported on the quality control during postharvest and transportation process. This paper reports the performance evaluation of grape quality at home storage and consumers’ satisfaction using integrated sensory evaluations. The internal attributes, including Texture, Taste and Odor of the table grapes and the appearance indices, Color and Cleanliness are examined. Key results show that during home storage, all the internal attributes decrease rapidly as time goes on, and cleanliness and color appear to be deteriorating in a lower speed. A comprehensive quality index was created to measure the quality of table grape which has high correlation with the Overall acceptability perceived by consumers.

Peaches are a popular fruit appreciated by consumers due to their eating quality. Quality evaluation of peaches is important for their processing, inventory control, and marketing. Eleven quality indicators (shape index, volume, mass, density, firmness, color, impedance, phase angle, soluble solid concentration, titratable acidity, and sugar&ndash, acid ratio) of 200 peach fruits (Prunus persica (L.) Batsch &ldquo, Spring Belle&rdquo, ) were measured within 48 h. Quality indicator data were normalized, outliers were excluded, and correlation analysis showed that the correlation coefficients between dielectric properties and firmness were the highest. A back propagation (BP) neural network was used to predict the firmness of fresh peaches based on their dielectric properties, with an overall fitting ratio of 86.9%. The results of principal component analysis indicated that the cumulative variance of the first five principal components was 85%. Based on k-means clustering analysis, normalized data from eleven quality indicators in 190 peaches were classified into five clusters. The proportion of red surface area was shown to be a poor basis for picking fresh peaches for the consumer market, as it bore little relationship with the comprehensive quality scores calculated using the new grading model.

In the process of agricultural production, the yield of field crops is affected by the interaction of various factors. This relationship between soil nutrients, fertilization, and yield is very complicated. It has strongly non-linear and black box characteristics and is difficult to quantify with traditional analytic methods. This paper proposes a method for the precision fertilization of maize based on the wavelet-BP neural network. Firstly, the existing “3414″ experimental data was chosen and interpolated as the performing data for the modeling. Then, the general condition of low frequency and details of high-frequency of the yield were calculated via the wavelet decomposition and reconstruction method. After that, the wavelet-BP neural network was established. Three different BP neural sub-networks for each component of the yield after wavelet analysis were established, and the output of each sub-network was summed to obtain the predicted corn yield. The results show that the model that combines wavelet analysis with the BP neural network achieves better performance than the traditional BP neural network, Support Vector Machine (SVM), and Random Forest regarding accuracy and stability, which indicates the feasibility of this method. Finally, the best fertilizer amount that achieves the maximum yield or maximum profit is calculated based on nonlinear programming. The proposed method of the wavelet-BP neural network provides a new technique for precision fertilization research and enriches the existing fertilization system. The application of precision fertilization based on the wavelet-BP neural network has important practical significance that guides and increases maize production, while reducing production cost and agricultural pollution. At the same time, it also provides a way to solve similar problems in the entire field of cleaner agricultural production.

Low-carbon production is one of the dominating issues in the sustainable development of the food industry with high energy consumption, especially in the table grapes cold chain. The aim of this paper is to propose a profit maximization strategy of table grapes cold chain by integrating the carbon footprint to improve the low-carbon production and sustainability of the cold chain system. The carbon footprint was evaluated by life cycle assessment (LCA) in entire cold chain system of table grapes, and the economic order quantity (EOQ) model was used to develop the profit maximization model with minimal carbon footprint and to maintain the optimal balance between stock and cost. The profit optimization performance, the sensitivity performance and the influencing factors of the decay rate, the carbon emission price, and the distance and carbon emission coefficient in refrigerated transport were analyzed according to the profit maximization model and the inventory data in actual cold chain investigated. The sensitivity performance analysis illustrated that the selling price had the highest sensitivity, and the carbon emission coefficient in storage had the lowest sensitivity. The comprehensive analysis results indicated that there is an optimal combination point between the economy and environment in actual cold chain, which not only reduced the carbon emission, but also had minimal impact on the profit in cold chain. The enterprises should integrate the carbon footprint cost into the profit maximization once the carbon emission tax is levied. The proposed strategy of the profit maximization with carbon footprint constraint is also suitable for improving profit maximization of other low-carbon supply chain applications.