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An intelligent pH-sensitive film was developed by incorporating cyanidin-3-glucoside (C3G) into bacterial cellulose (BC), and its application as a freshness indicator for tilapia fillets was investigated. The physical properties of the film were characterized using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The results demonstrated the mechanical properties of the film were significantly changed due to higher crystallinity induced by C3G. XRD and FTIR analysis showed the increased crystallinity and transmittance intensity of the BC-C3G film. Moreover, this film exhibited distinctive color changes from red to green when exposed to buffers with a pH of 3 to 10. In accordance with changes in total volatile basic nitrogen (TVB-N) and total viable count (TVC) of tilapia fillets, the indicator demonstrated visualized color changes as rose-red (fresh), purple (still suitable), and lavender (spoiled) during storage at both 25 °C and 4 °C. The results suggest that this film has great potential to be used as an intelligent indicator to monitor the freshness of fish.

Leaf wetness duration (LWD) provides the necessary conditions for pathogen infection. Among them, dew condensation on the crop canopy due to high humidity in a greenhouse is a major cause of LWD formation. However, it would be costly to monitor the condensation of all the leaves in a greenhouse. A computational fluid dynamics model was studied for the spatial and temporal distribution of the indoor microclimate and leaf condensation in a single-slope Chinese solar greenhouse at night. Models were embedded to simplify the input parameters and enhance the practicality. Without compromising the performance of the model, the model inputs were reduced to five: outdoor solar radiation intensity, outdoor air temperature, outdoor relative humidity, outdoor average wind speed per hour, and soil temperature. The distributions of roof condensation and leaf condensation were simulated. Condensation always appeared first on the roof rather than on the leaves. The leaf condensation results were manually observed for comparison with the simulated results. Leaf condensation always occurred first in the area near the semi-transparent roof, both in the observations and the simulation. The LWD was simulated by considering the duration of the simulated leaf condensation at each point. The evaluation was conducted on 216 pairs of samples. The True Negative Rate (TNR), True Positive Rate (TPR), and Accuracy (ACC) were 1, 0.66, and 0.89, respectively. This paper can serve as a reference for an early warning model of disease based on the temporal and spatial distribution of leaf condensation.

Background Cold chain logistics (CCL) is not only vital for maintaining the quality and safety of fresh agro-products and reducing losses but also provides important support to help increase farmer income and thereby promote the revitalization of rural industry in China. In recent years, numerous studies have focused on improving the efficiency and sustainability of CCL, and the results have important implications for promoting innovation, applying technologies, improving facilities and equipment, and optimizing management in the CCL industry. Scope and approach This review discusses active research areas, gaps in the existing state of research, and future research challenges for CCL. Furthermore, we summarize the current status of China's CCL industry and technology and compare the state of CCL development in China with that in more developed countries in terms of infrastructure, data handling, and national policies. Key findings and conclusions The future trends of CCL involve low carbon strategies and intelligent innovation, which are the key to meeting environmental concerns and the evolving needs of the market. Advances in next-generation information technology (including IoT, blockchain, AI, etc.) have significantly accelerated the modernization of CCL. Meanwhile, attaining these dual objectives of a low-carbon footprint and intelligent innovation requires cooperation between national regulators, industry, consumers, and interdisciplinary experts. A key finding of this review is that national policy and financial intervention in China are expected to be the main forces behind renovating infrastructure and upgrading standardization, which is required to narrow the CCL development gap between China and other more developed nations.

In aquaculture, the accurate prediction of feed intake for group fish is considered to be crucial to any feeding system. Previous studies mainly used mathematical statistics to establish the mapping relationship between feed intake and influencing factors. The result was easily influenced by subjective experience. To solve the above issues, this paper proposed a feed intake prediction model for group fish using the back-propagation neural network (BPNN) and mind evolutionary algorithm (MEA). Firstly, four factors, including water temperature, dissolved oxygen, the average fish weight and the number of fish were selected as the input of the BPNN model. Secondly, the initial weight and threshold of the BPNN were optimized by the MEA to improve the matching precision. Finally, the prediction model was achieved after training. Experimental results showed that the correlation coefficient between the predicted and measured values reached 0.96. And the root mean squared error, mean square error, mean absolute error, mean absolute percent error of the model was 6.89, 47.53, 6.17 and 0.04, respectively. In addition, the proposed method also had the better nonlinear fitting ability than BPNN and GA-BP. By using an intelligent optimization algorithm, the mapping relationship between fish intake and environmental factors was automatically established, thus avoiding the subjectivity of traditional methods. Therefore, it can lay a theoretical foundation for the development of intelligent feeding equipment and meet the needs of the smart fishery.

This study presents a novel method for predicting the shelf life of pork in real-time based on front-face fluorescence excitation-emission matrices (EEMs). The total viable count (TVC) of bacteria was used as the indicator of microbial spoilage in the pork samples. Modified Gompertz and square root equations were used to establish models for the trends in microbial growth and for predicting the shelf life, the R

We investigated three-dimensional (3-D) fluorescence spectroscopy for its potential to evaluate beef quality deteriorative changes and freshness. The fluorescence characteristics of heme, conjugated Schiff base and amino acids, could be indicators of internal biochemical reactions associated with beef deterioration, including color changes, lipid oxidation, and protein degradation, as well as a measure of freshness decline. To classify beef quality in terms of color (sensory index) and pH (chemical index), cluster analysis method (CA) was used. Three classes were identified: "fresh", "acceptable", "spoiled". We then developed a qualitative model to classify stored beef into these three classes using 3-D front-face excitation-emission matrices (EEMs) of fat tissue, combined with a parallel factor analysis (PARAFAC) algorithm. The resulting model had calibration and validation accuracies of 95.56% and 93.33%, respectively. These results demonstrate the potential of fluorescence spectroscopy to accurately and non-destructively monitor beef quality decline.

In aquaculture, information on fish appetite is of great significance for guiding feeding and production practices. However, most fish appetite assessment methods are inefficient and subjective. To solve these problems, in this study, an automatic method for grading fish feeding intensity based on a convolutional neural network (CNN) and machine vision is proposed to evaluate fish appetite. The specific implementation process was as follows. First, images were collected during the feeding process, and a dataset was constructed and extended using rotation, scale, and translation (RST) augmentation techniques and noise-invariant data expansion. Then, a CNN was trained on the training dataset, and the fish appetite levels were graded using the trained CNN model. Finally, the performance of the method was evaluated and compared with other quantitative and qualitative feeding intensity assessment methods. The results show that the grading accuracy reached 90%; thus, the model can be used to detect and evaluate fish appetite to guide production practices.

Traceability systems have changed the way food safety is managed and data is stored. Blockchain tracking services now provide customers with an infrastructure that allows them to easily access data online. However, there are limitations to these new capabilities, such as a lack of transparency and the existence of privacy and security challenges. Additionally, as the need for more agile, private, and traceability secure data solutions continues to grow exponentially, rethinking the current structure of blockchain agricultural traceability is mission-critical for a country. By leveraging and building upon blockchain's unique attributes, including tamper-evident, security hash crypto-data, and distributed ledger, we have proposed a prototype that allows traceability data to be reliably stored via blockchain while simultaneously being secured, with completeness auditing to enhance credibility. The result, the trusted auditing chain (TA chain), is a flexible solution that assures data security and solves challenges such as scalability and privacy-preserving. The TA chain works through Schnorr-style non-interactive Zero-knowledge proof to support security automatical choose privacy augmented. In addition, The TA chain can audit more than 1000 transactions within 1ms, and its error stabilizes below the 250 μs, which proves a security and fair traceability system to assure that data is distributed and reliably, and provably audited.

In recent years, duck production has been changed from conventional free range and open water outdoors to confinement in birdhouses. And the concept of animal welfare in high stocking density begins to be accepted in China. The search of the relationship between growth performance and flock activity of Pekin ducks with different stocking densities using camera surveillance has great potential as an aid to improving flock management. The aim of this study was to determine the impact of stocking density on growth performance and activity of Pekin duck flocks. Furthermore, the correlation between growth performance and the activity of White Pekin ducks was investigated. Eventually, it will generate an automatic method for monitoring growth performance and stocking density evaluation of Pekin ducks. All ducks (24 days of age, n = 1200) were randomly allotted into 5 stocking density groups of 5 ducks/m2, 6 ducks/m2, 7 ducks/m2, 8 ducks/m2, 9 ducks/m2, with 6 replicates for each group. One group was selected for monitoring the activity of ducks using video recording system. The optical flow measures were extracted to describe the duck flock activity statistically using night video data. On the 24 and 42 days of age, sample collection was conducted for initial and final body weight (BW) measurements respectively. The results showed that the stocking density had significant effects on final BW and weight gain (P 0.05). The stocking density also had significant effects on mean and variance of optical flow produced by duck flock activity throughout the experimental duration. In the last, a significant relation was found between the mean of optical flow and final BW (r2 = +0.87). These results show that the proposed method has potential in automatic growth monitoring and stocking density management of Pekin ducks. It can cut out the bio-security risk and animal stress of having people actually visiting duck houses.

Near infrared vision was used to quantify feeding behavior of fish.Fish feeding decision was realized using the neuro-fuzzy model.The method performance was evaluated by accuracy, fish growth and water quality.The proposed method can save feed costs and reduce water pollution. In aquaculture, the feeding efficiency of fish is of great significance for improving production and reducing costs. In recent years, automatic adjustments of the feeding amount based on the needs of the fish have become a developing trend. The purpose of this study was to achieve automatic feeding decision making based on the appetite of fish. In this study, a feeding control method based on near infrared computer vision and neuro-fuzzy model was proposed. The specific objectives of this study were as follows: (1) to develop an algorithm to extract an index that can describe and quantify the feeding behavior of fish in near infrared images, (2) to design an algorithm to realize feeding decision (continue or stop) during the feeding process, and (3) to evaluate the performance of the method. The specific implementation process of this study was as follows: (1) the quantitative index of feeding behavior (flocking level and snatching strength) was extracted by Delaunay Triangulation and image texture; (2) the adaptive network-based fuzzy inference system (ANFIS) was established based on fuzzy control rules and used to achieve automatically on-demand feeding; and (3) the performance of the method was evaluated by the specific growth rate, weight gain rate, feed conversion rate and water quality parameters. The results indicated that the feeding decision accuracy of the ANFIS model was 98%. In addition, compared with the feeding table, although this method did not present significant differences in promoting fish growth, the feed conversion rate (FCR) can be reduced by 10.77% and water pollution can also be reduced. This system provides an important contribution to realizing the real-time control of fish feeding processes and feeding decision on demand, and it lays a theoretical foundation for developing fine feeding equipment and guiding practice.

An automatic procedure to identification Holstein dairy cows using tailhead images is proposed.Zernike moments are extracted and used as a shape descriptor of object features.Two groups of feature and different state-of-the-art classifiers are compared.The proposed method aims to precision livestock farming, especially, the individual identification in BCS evaluation system. The implementation of dairy cow identification will be of great significance in precision animal management based on computer vision. In this study, a computer vision technique to identify the individual dairy cows automatically was proposed and evaluated. The tailhead image, which was used as a Region of Interest (ROI), was captured in a dairy farm. Zernike moments were used as descriptors of shape characteristics for the white pattern on the ROI. Two groups of Zernike moments were extracted from the preprocessed image and classified using four alternative classifiers, namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), artificial neural network (ANN) and support vector machines (SVM). The QDA classifier had the highest value, 99.7%, while the SVM classifier had the highest precision, 99.6%. Comprehensively, the QDA and SVM classifiers presented the best performance, with equal F1 score of 0.995. These results show that the low-order Zernike moment feature, along with the QDA and SVM algorithms is an effective approach for individual dairy cow identification and has significant applications in precision animal management.

The current packaging designs and the efficiency of forced-air cooling (FAC) of fresh produce can be considerably improved by comprehensively comparing and evaluating the existing packaging designs. This study presents a market survey that studies samples of typical apple cartons used in China. Furthermore, by combining experiment and computational fluid dynamics (CFD) modelling, a novel integral approach is proposed to evaluate cooling rate and uniformity, energy efficiency, and fruit quality (including safety) as a result of FAC for different ventilated-packaging designs. The process uses CFD to simulate the three-dimensional spatio-temporal distributions of airflow and product temperatures during precooling. In addition, experiments on chilling injury and mass loss are also reported. The results show that the optimum fresh-fruit packaging design depends on the product size and the location of the product and tray inside the packaging. For all existing package designs, the optimal air-inflow velocity is found to lie in the range 0.4–1 m/s (or 3–5 L s−1 kg−1), any further increase in airflow rate simply wastes energy because it leads to a relatively low increase in cooling rate and uniformity. The level of chilling injury and mass loss per box show a different trend with increasing air-inflow velocity. The accuracy of the CFD simulations was confirmed by a good agreement with experiments. The maximum root-mean-square error and mean absolute percentage error for produce temperature are 0.727 °C and 18.69%, respectively. This research unveils the advantages and disadvantages of the various existing packaging designs and provides a reliable theoretical and experimental basis for achieving an integral evaluation of the performance of FAC.

Logistics and storage is the main processing for agro-food supply chain. Because of disconnection information between the two processing, it is difficult to trace continuously. An intelligent conveyer belt provides an effective method to associate storage and logistics by QR code scanning and information recording. Improving the QR code readability in movement state is the core of implementing continuous chain traceability with this belt. In this paper, a intelligent conveyer belt including automatic conveyer unit, barcode scanning unit, fault remove unit and control display unit was designed. Four factors affected QR readability were selected and the value range was confirmed, which was reading distance, code size, coded characters and belt moving speed. Based on the belt, an Central Composite Inscribed (CCI) experiment of four factors with five levels was designed using Response Surface Methodology (RSM) to obtain the optimal reading parameters. The result shows that the main factors of reading distance, belt moving speed and the interaction between reading distance and code size have the significant effect on QR code readability. Under the optimization condition of 141.45 mm reading distance, 34.58 mm code size, 100 bytes coded characters and 2.98 m/min belt moving speed, the average value of QR code readability was 95%. With the optimization parameters, the intelligent conveyer belt was used in an apple marketing enterprise. The result shows that the continuous traceability between storage and logistic can be implemented with the extended breadth, deepened depth and improved precision.

Delaunay Triangulation was applied to the extraction of behavioral characteristics.Support Vector Machine was used to classify the reflective frame.Serious reflection frames were removed and new data were fitted.The linear correlation coefficient between FIFFB and human expert can reach 0.945. In aquaculture, fish feeding behavior under culture conditions holds important information for the aquaculturist. In this study, near-infrared imaging was used to observe feeding processes of fish as a novel method for quantifying variations in fish feeding behavior. First, images of the fish feeding activity were collected using a near-infrared industrial camera installed at the top of the tank. A binary image of the fish was obtained following a series of steps such as image enhancement, background subtraction, and target extraction. Moreover, to eliminate the effects of splash and reflection on the result, a reflective frame classification and removal method based on the Support Vector Machine and Gray-Level Gradient Co-occurrence Matrix was proposed. Second, the centroid of the fish was calculated by the order moment, and then, the centroids were used as a vertex in Delaunay Triangulation. Finally, the flocking index of fish feeding behavior (FIFFB) was calculated to quantify the feeding behavior of a fish shoal according to the results of the Delaunay Triangulation, and the FIFFB values of the removed reflective frames were fitted by the Least Squares Polynomial Fitting method. The results show that variations in fish feeding behaviors can be accurately quantified and analyzed using the FIFFB values, for which the linear correlation coefficient versus expert manual scoring reached 0.945. This method provides an effective method to quantify fish behavior, which can be used to guide practice.

Recent developments in the legal establishment and the market have motivated more agro-food companies to implement traceability systems (TS). TS play an important role not only for planning system implementation before development, but also for analyzing system performance after using the system. A novel agro-food TS model is presented here, based on comprehensive and quantifiable granularity concepts. A 2-layer index system was established; the first layer was mainly factors such as precision, breadth, and depth, and the second layer included seven indicator sub-factors: external trace units, internal flow units, IU conversion, information collection content, information update frequency, forward tracking distance, and backward tracing distance. An indicator’s overall score was scaled with five contributing scores that graded the assignment method. Indicator weight was confirmed with the AHP method. The weight values of the seven indicators were 0.1985, 0.1141, 0.0872, 0.1870, 0.1248, 0.1442, and 0.1442, respectively. A weighted sum model was adopted to calculate the evaluation value. A high evaluation value indicated high granularity. The granularity model was applied in two enterprises, here identified as WPF and WFPE, which were located at different stages in wheat-flour supply chain. The survey results showed that WFPE should invest more in tracing equivalent granularity than WPF should because it involves multi-stage processing, a complicated supply chain structure, it is a large enterprise, and operates in a strict regulatory environment. Furthermore, WFPE was motivated to implement a high granularity level because of benefits in supply chain management, market and customer response, and recall and risk management. In the future, an updated granularity evaluation model that could combine enterprise characteristics and uncover hidden costs and benefits will be studied further.

Vibrational spectroscopy is an effective non-destructive technique, and it has been successfully applied in characteristics identification for agro-food samples. However, owing to the high dimensionality of spectral dataset, it is difficult to distinguish samples of different characteristics from observing the raw spectral. In this study, t-Distributed Stochastic Neighbor Embedding (t-SNE), an state-of-art method, was applied for visulization on the five vibrational spectroscopy data sets. The performances of t-SNE and the other reference methods (PCA and Isomap) were illustrated both from the differentiation ability in the 2-dimensional space and the accuracy of sequential classification model. For the former, t-SNE showed more satisfied visual discrimination results in 2-dimensional space and obtained better scores of clustering metrics, Silhouette Coefficient (0.59 average score compared to 0.24 achieved by PCA and 0.59 by Isomap) and Davies-Bouldin Index (1.51 average score compared to 2.58 achieved by PCA and 1.52 by Isomap). For the latter, two supervised classification models, k-nearest neighbor (KNN) and support vector machine (SVM), were constructed based on the new representations in 2-dimensional space, in both cases, the representations given by t-SNE outperformed the other methods in terms of accuracy (for KNN, 96% average accuracy compared to the 85% achieved by PCA and 92% by Isomap; for SVM, 96% average accuracy compared to the 86% achieved by PCA and 92% by Isomap). The results showed great potential of t-SNE for recognizing minute spectral differences between classes, and proved that t-SNE is an effective dimensionality reduction and visualization method, especially when complex and highly overlapping vibrational spectra are used for analysis.

In aquaculture, the real-time detection and monitoring of feed pellet consumption is an important basis for formulating scientific feeding strategies that can effectively reduce feed waste and water pollution, which is a win-win scenario in terms of economic and ecological benefits. However, low-quality underwater images and extremely small targets present great challenges to feed pellet detection. To overcome these challenges, this paper proposes an uneaten feed pellet detection model using an improved You Only Look Once (YOLO)-V4 network for aquaculture. The specific implementation methods are as follows: (1) The feature map responsible for large-scale information in the original YOLO-V4 network is replaced by a finer-grained YOLO feature map by modifying the connection mode of the feature pyramid network (FPN) + path aggregation network (PANet). (2) The residual connection mode in CSPDarknets is modified via a DenseNet, which further improves the feature reuse and the network performance. (3) Finally, a de-redundancy operation is carried out to reduce the complexity of the YOLO-V4 network while ensuring the detection accuracy. Experimental results in a real fish farm showed that the detection accuracy is better than that of the original YOLO-V4 network, and the average precision is improved from 65.40% to 92.61% (when the intersection over union is 0.5), for an increase of 27.21%. Additionally, the amount of computation is reduced by approximately 30%. Therefore, the improved YOLO-V4 network can effectively detect underwater feed pellets and is applicable in actual aquaculture environments.

Most real-world scenarios face the problems of small-sample learning and fine-grained recognition. For many rare insect classes, collecting a large number of training samples is infeasible or even impossible. In contrast, humans are able to recognize a new object class with little supervision. This motivates us to address the problems of small-sample recognition and fine-grained recognition for insects by combining recognition and localization; this can provide an effective remedy for data scarcity and the two techniques can bootstrap from each other. In this paper, we propose a saliency-detection model to localize the key regions that have the largest discriminative features for fine-grained insect classification. The learner learns to predict foreground and background masks for such localization, having been trained on a training set annotated with bounding boxes. Additionally, to further generate discriminative features, a center neighbor loss function is used to construct a robust feature-space distribution. The proposed model is trained end-to-end on our small-sample learning dataset, which comprises 220 insect categories from a real-world complex environment. Compared with the method using prototypical networks, the proposed method achieves a superior performance, with a mean recognition rate (top-5 accuracy) of 57.65%, and can effectively recognize insects under small-sample and complex-scene conditions.

Agricultural pest catches on sticky traps can be used for the early detection and identification of hotspots, as well as for estimating relative abundances of adult pests, occurring in greenhouses. This study aimed to construct a detection model for whitefly and thrips from sticky trap images acquired in greenhouse conditions. An end-to-end model, based on the Faster regional-convolutional neural network (R-CNN), termed ‘TPest-RCNN’, was developed to improve the tiny pest detection accuracy. This architecture was trained using a transfer learning strategy on the Common Objects in Context dataset before training on the tiny pest training set to create the TPest-RCNN model. The new model achieved mean F1 score and average precision of 0.944 and 0.952, respectively, on a validation set. The TPest-RCNN model outperformed the Faster R-CNN architecture and other approaches using handcrafted features (color, shape and/or texture) in detecting multiple species from yellow sticky trap images. The test results also showed the model was robust to detect tiny pests on images of different pest densities and light reflections. Using a linear regression between the manual counts and an automatic detection results using the proposed method on images of 41 days, the determination coefficients reached 99.6% and 97.4% for whitefly and thrips, respectively. These results demonstrated that the proposed method could facilitate rapid gathering of information pertaining to numbers of the abundance of tiny pests in greenhouse agriculture and provide a technical reference for pest monitoring and population estimation.

A precise transpiration prediction model thus becomes an important tool for greenhouse automatic irrigation management. Moreover, leaf is an organ of transpiration, and leaf area index is a basic variable to estimate this water lost, but it is still a weak spot in the crop growth estimation. In this paper, two different leaf area index models are established and compared with the evolution of the real crop determined with an electronic planimeter: (1) Considering the temperature and photosynthetically active radiation (PAR) as the main impact factors over crop growth, a TEP-LAI model based on product of thermal effectiveness and PAR is built to estimate the leaf area index dynamics; and (2) TOM-LAI model based on a tomato growth model is also used to estimate the leaf area index as an explicit function of the number of leaves and vines. Finally, the results of both simulation models (TEP-LAI and TOM-LAI) are compared with the measured values. Moreover, a crop transpiration model is established using the empirical data sampled in a multi-span greenhouse in Almeria (Spain). In this greenhouse, a microlysimeter (two different weight scales) was used to obtain the transpiration and the drainage values. Thus, the data collected is used to obtain a model of the estimated water lost by transpiration, that it is based on Back Propagation-Neural Network was optimized using genetic algorithm and Nonlinear Auto-regressive model with Exogenous Inputs model. Once described the different models, the estimated values of leaf area index are compared satisfactorily with the measured ones. TEP-LAI is the model chosen to be introduced as input of the final transpiration model. As expected, the transpiration estimation with inside conditions generates better results, but the outside climate based model shows that it could be used as an irrigation predictor with data from cheaper outside meteorological stations.

Counterfeiting products and abusing labels lead to less credibility for traceability system in China recently. Authentication and supervision agencies driven by government departments play an important role for ensuring the quality safety in the case of lacking the willingness and credit of enterprises. A complete authentication and supervision flow framework was constructed based on an identification code (IdC) for authenticated origin base, which linked two actors of the agencies and the enterprises, and three subsystems of On-line Authentication Subsystem (OAS), Safety Production Management Client (SMC) and Mobile Supervision Application (MSA). IdC consisted of longitude and latitude of origin base as position code, production code and authentication type code. With a relative position partition method on 6 zones every 27° for China map and a coordination transformation algorithm, an absolute longitude and latitude value was converted into a relative position value and a zone mark value. IdC and packaging date code formed initial traceability code (TC). 8 digits packaging date code was reconstructed into 3 digits relative time value and 1 digit period mark according to a relative time period partition method with a period of 999d as time intervals and four periods form a cycle. Validation code was generated integrating the zone mark value, period mark value and authentication type code. Therefore, transformed 20 digits TC with the characters of shorter code length and stronger encryption was formed with IdC, relative time value and validation code. Three subsystems for different actors which provide the main function such as origin base registration, agency authentication, QR code generation, data uploading and product verification, were developed. The system has been used in Tianjin city from 2012. 213 enterprises were audited through OAS and used SMC. Through investigating 8 supervision agency staffs, 30 origin base owners, and 50 customers, it is shown that the positive effects are approved by most of the investigators and two negative effects for enhancing the costs and doubting the authentication reliability are laid by 17 enterprises and 12 customers. Furthermore, 4 typical cases for counterfeiting and abusing the labels were exampled and can be solved to a certain extent with the system. However, except for the technology itself, a management measures fitting the supervision flow and system need to draft in order to improve the system application well in the future.

Optimizing the design of fresh produce packaging is vital for ensuring that future food cold chains are more energy efficient and for improving produce quality by avoiding chilling injuries due to nonuniform cooling. Computational fluid dynamics models are thus increasingly used to study the airflow patterns and heat transfer inside ventilated packaging during precooling. This review discusses detailed and comprehensive mathematical modeling procedures for simulating the airflow, heat transfer, and mass transfer that occurs during forced-air precooling of fresh produce. These models serve to optimize packaging design and cooling efficiency. We summarize the most commonly used parameters for performance, which allows us to directly compare the cooling performance of various packaging designs.

Humidity control, which is affected by the performance of humidifying device and structure of the container, is very important for delaying water loss of fresh products. Humidifying rate and humidity distribution uniformity in fresh-keeping container were investigated to evaluate the characteristic of humidity control by Computational Fluid Dynamics (CFD) models. A pressure gauge was adopted to measure the ventilation resistance of products, by which the inertial resistance and viscous resistance values were obtained. The results of humidity performance were evaluated by entropy method. The results showed that the number of ultrasonic atomizers and sensor location had a significant effect on the humidifying rate. The effects of spray height, deflector angle and products quantity on humidifying rate are not significant, but they showed significant difference in the humidity distribution uniformity on products. The results of this study provide a better understanding of humidity control, which will help for the environment control in a fresh-keeping container.

In stomatology, the promotion of alveolar bone regeneration while preventing the reduction of ridge absorption remains a challenge. In this work, we designed and prepared bio-mimetic polysaccharide hydrogels that are multi-functional in terms of being injectable, promote self-healing, degradable, porous structure, et al. After introducing nano-hydroxyapatite particles, the composite scaffold of hydrogel/hydroxyapatite (GH) stent was obtained. When GH material was injected into the mandibular incisors of rats following tooth extraction, the new bone area was enhanced more than 50%, while the alveolar ridge was promoted in excess of 60% after 4 weeks. What's more, the wound soft tissue was healed within 1 week. Overall, our results indicate that this optimized GH stent has the potential to both maintain dimensional alveolar ridge, as well as to promote soft tissue healing. Moreover, using the hydroxyapatite-containing hydrogel platform has the potential to promote bone and soft tissue regeneration.

We investigated the potential of front-face synchronous fluorescence spectroscopy (△λ = 75 nm) to non-destructively evaluate beef freshness and quality decline during chilled storage. The total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS) and total viable count (TVC) values were used as standard freshness indicators. The fluorescent substances, including amino acids, collagen and conjugated Schiff bases, were highly correlated with the chemical and microbial deterioration of the beef. Quantitative models for simultaneously predicting the three freshness indicators were built combined with partial least squares (PLS) algorithm and showed good reliability. For TVB-N and TBARS values, Rc2 and Rp2 were both above 0.900, and for TVC values Rc2 and Rp2 were 0.912 and 0.871, respectively. The qualitative model established by partial least squares discriminant analysis (PLS-DA) algorithm could accurately classify beef samples as fresh, acceptable or spoiled. The accuracy of the calibration and validation sets were 92.54% and 86.96%, respectively.

Optimization of fresh fruit packaging designs is required to reduce energy loss by minimizing the precooling time and to enhance fruit quality by providing more uniform cooling without inducing chilling injuries. In this work, a computational fluid dynamics (CFD) model is developed to study the airflow patterns and heat transfer inside an existing container and a newly developed container. The CFD model employs an unsteady-state approach based on a two-equation eddy-viscosity turbulence model (SST-κ–ω model). The cooling performance of the existing container and the new container are evaluated experimentally and numerically with the CFD model. The CFD results reveal a complex and uneven distribution of the airflow inside the existing vented package. Such airflow leads to a non-uniform temperature distribution over the produce, with a maximum temperature difference of ∼8 °C between two layers of stacked produce. For the new boxes, the half-cooling time and coefficient of temperature variation are about twofold less than those for the existing boxes, and the maximum temperature difference is ∼2.5 °C between two layers of stacked produce. Thus, the new package design clearly shows significant improvements in cooling performance. The numerical model is verified by comparing the simulation results to those of experiments, and the predicted results are consistent with the measured results. The maximum temperature deviation is less than 1.5 °C, and the maximum root-mean-square error and average relative error for produce temperature are 1.452 °C and 13.6%, respectively. This research provides a reliable theoretical and experimental basis for improving airflow and produce-temperature uniformity and for minimizing energy consumption during the forced-convection cooling of produce.

A bidirectional acquisition system on smart phones.Farm information collection flow on tree identification with QR code.Sensors search rule on tree position and multi-point environment value model. An orchard precision management system plays an important role in improvement at the management level and the enhancement of decision abilities. A single orchard tree or an orchard tree microcommunity is the basic management unit, and bidirectional information on the environment and plants is the important content for precision management. A type of RFID label was applied with a UHF chip in the core and a QR code in the surface for single tree identification. A bidirectional acquisition system for orchard production, which included farming information collection for the forward direction and environmental information acquisition for the backward direction, was designed with smart phones. In the farming information collection part, information collection flow that included QR code image acquisition, image preprocessing, barcode decoding and farming information collection was established. An improved local threshold method was adopted to improve the QR code identification rate in the smart phone platform. In the environment information acquisition part, a sensor search rule on the single tree position and a multi-point environment value model were designed. The orchard information bidirectional acquisition system was developed on an Android platform with the Java language, which has the function of QR decoding, farm record information collection, environment information acquisition, data uploading and statistical analysis. The system was tested in an apple orchard. A total of 144 trees were chosen to decode the QR codes in the tree label. The success rate was approximately 96.52%. The identification time of 85% of the trees was less than 4s for the 20 chosen trees. In taking the temperature, for example, the difference between the computed temperature value and the measured temperature value around each tree was small. The system could decrease the cost of the professional equipment, such as portable RFID readers and writers, which was a low-cost and high-efficiency solution for orchard production information collection.

A rapid, non-destructive and robust method for measuring peroxide values (PVs) and acid values (AVs) of common edible oils (soybean, rapeseed, sunflower and peanut) simultaneously under various thermal oxidation was explored by FT-IR and Raman spectroscopy data fusion strategy. Uninformative variable elimination (UVE) and successive projections algorithm (SPA) methods were used for feature variables extraction, quantitative models for prediction of chemical quality indices were established using partial least squares regression (PLSR) algorithm. The bands associated with vibration of C=O and C=C stretching were highly correlated with PVs and AVs, data fusion of the two spectra showed the best modeling results when variables identified by SPA were used. For modeling of PVs, the resulting Rc2 and Rp2 were 0.964 and 0.939, RMSEC and RMSEP were 0.060 and 0.080. For modeling of AVs, the resulting Rc2 and Rp2 were 0.955 and 0.919, RMSEC and RMSEP were 0.025 and 0.027.

There are not or weak anti-counterfeit functions in the current traceability system. As a result, the counterfeiters could imitate this system easily. This phenomenon had a large impact on the traceability system construction and on consumer trust in the traceability information. The aim of our research was to construct an anti-counterfeit code for aquatic product identification, for traceability and supervision of aquatic enterprises in the domestic market. The aquatic products batch code (APBC) was in the form of a segmented combination encoding an enterprise identification code, a product code and a check code, which implements a combination of traceability and supervision. An encryption algorithm based on the Advanced Encryption Standard (AES) was designed for decimal anti-counterfeit code based on the unique identification of the aquatic trace units. Simulation tests indicated that a diffusion rate of greater than 90% was achieved when the encryption was run four or more times, thereby leading to the implementation of an anti-counterfeiting technique for aquatic traceability, known as “one time, one code”. The anti-counterfeit code combined with GS1 was used in a product label, and the method has a high level of security and is used for supervision and tracing of aquatic products in China.

Treatment during the primary infection phase is essential for controlling cucumber downy mildew in solar greenhouses. An early warning model applicable to this phase would represent a foundation for early warning systems for managing the disease and reducing pesticide usage. Based on the input parameters that were both readily available and appropriately limited in number, EWMPICDW (early warning model for primary infection of cucumber downy mildew in solar greenhouses) was developed based on monitoring data, early warning theory and plant disease epidemiology. The elaboration of this model included clarification of the meaning of warning, monitoring the warning indicators, forecasting the warning situation, tracing the warning sources and controlling the warning situation. The definition of warning included disease occurrence (yes or no) and probability. Because the leaf wetness duration (LWD) played an important role in disease warning systems for crops in solar greenhouses and was difficult to monitor, the leaf wetness sensor and RH threshold model were investigated and combined to form a practical estimation solution for LWD. Within the warning situation forecasting model system, the infection condition and incubation early warning submodels received the most attention. The infection condition early warning submodel was developed by using a threshold method based on the combination of LWD and mean temperature in LWD. The temperature was chosen as the warning indicator for incubation, and the incubation early warning submodel was defined using nonlinear regression methods. The warning sources traceability algorithm was developed in relation to expert knowledge and in terms of a mode of disaster mitigation that involved cutting the disaster chain from the headstream. The method for controlling the warning situation was based on good agricultural practices (GAP). The early warning model was implemented as a system and was evaluated using data for 4 years at two sites in Beijing, China. The warnings can be provided more than 2 d before symptoms appear. Using EWMPICDW, a positive early warning is associated with a change in the chance of disease occurrence from 0.68 to 0.96. Accordingly, the probability of disease occurrence calculated for the early warning model was 96%. These results demonstrate that the data-driven model will support the development of early warning systems for primary infection by cucumber downy mildew in solar greenhouses.

For the small-scale and scattered fresh cucumber production in China, the result that production record-keeping and its transfer are inefficient have prevented the wide application of traceability systems in China. With the mobility and computability, Personal Digital Assistant (PDA) provides a new way for agricultural information collection to solve the above problems. Thus a PDA-based Record-keeping and Decision-support System (PRDS) for traceability in cucumber production was developed on Windows Mobile platform invoking a Geographic Information System (GIS) control. For improving the decision making feasibility of PRDS, the fertilization recommendation model and pesticide usage early warning model were developed by using the Technical Specification of Balanced Fertilization by Soil Testing and the Guideline for Safety Application of Pesticides in China. The architecture of PRDS was provided. With Unified Modeling Language (UML), a requirement model including two types of users and 17 use cases was described, and a static class model was also designed, which consisted of table class, table operation class, algorithm class and interface class. Based on these models, the functions of system setup, map management, data management, production record-keeping and decision-support and query, etc., were implemented by adopting Hosting MapInfo MapX Mobile Controls on the .NET Compact Framework 2.0, and the data synchronization was realized by Remote Data Access (RDA). Two agricultural production enterprises were chosen as case study to evaluate the system by questionnaires. The results show that the efficiency of production record-keeping and decision-support is improved by the simple and friendly system.