Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-09-05 Cooperative journals: 《农业图书情报学报》
Abstract： Purpose/Significance Different collaborators play different roles and assume corresponding responsibilities in scientific research collaboration. Distinguishing the different roles in research collaborators is important for the evaluation of research talent and human resources allocation. Previous studies have defined the roles of collaborators from multiple perspectives, both qualitative and quantitative, but lack a simple and efficient way to identify core collaborators. In this paper, we use the number of collaborations to identify core collaborators in scientists' collaborative relationships based on the H-index measure, which is a very easy to calculate and intuitively understandable method. Method/Process Using the OpenAlex database as a data source, an empirical analysis of approximately 5.05 million journal papers in the field of computing in China and G7 countries over 20 years (2000-2021) was conducted. First, the core collaborators of highly productive scientists were studied and their collaboration characteristics were analyzed from the perspective of size and share. Second, based on the H-index fitting formula proposed by previous authors, a formula for estimating the number of core collaborators based on the number of publications and the average number of collaborators per article was proposed. Finally, the formula was used to compare the differences between the theoretical and actual values of the number of core collaborators across countries. Results/Conclusions The study found that in terms of size and proportion of core collaborators, China had the highest average total number of collaborators among highly productive scientists, followed by the USA, Germany and the UK, while Italy had the lowest. The number of core collaborators was generally 3-7 across countries, with China and Italy having a higher rate of cooperation and the UK, France and Canada having a lower rate of cooperation. In terms of the number of core collaborators as a percentage, no country has more than 10%, with Italy having the highest percentage of core collaborators at 7.42%, followed by Japan, France and Canada, while the US has the lowest percentage of core collaborators. In terms of the total number of collaborators, there is no significant difference between China, the US and Germany, while there is a significant difference among all five other countries. In terms of the number of core collaborators, China is not significantly different from Italy and is significantly different from all other six countries. The number of core collaborators can be estimated by using the formula of the product of the number of publications and the power of the average number of collaborators per article, which has a good fit of 0.8 or more. Among China and the G7, the US, Germany and the UK have a lower proportion of core collaborators, with more frequent mobility and exchange of talent, while Italy, Japan and China have a higher proportion of core collaborators, indicating a lack of talent mobility and a relative consolidation of research collaboration.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： To meet the needs of environmental monitoring and regulation in rabbit houses, a real-time environmental monitoring system for rabbit houses was proposed based on narrow band Internet of Things (NB-IoT). The system overcomes the limitations of traditional wired networks, reduces network costs, circuit components, and expenses is low. An Arduino development board and the Quectel BC260Y-NB-IoT network module were used, along with the message queuing telemetry transport (MQTT) protocol for remote telemetry transmission, which enables network connectivity and communication with an IoT cloud platform. Multiple sensors, including SGP30, MQ137, and 5516 photoresistors, were integrated into the system to achieve real-time monitoring of various environmental parameters within the rabbit house, such as sound decibels, light intensity, humidity, temperature, and gas concentrations. The collected data was stored for further analysis and could be used to inform environmental regulation and monitoring in rabbit houses, both locally and in the cloud. Signal alerts based on circuit principles were triggered when thresholds were exceeded, creating an optimal living environment for the rabbits. The advantages of NB-IoT networks and other networks, such as Wi-Fi and LoRa were compared. The technology and process of building a system based on the three-layer architecture of the Internet of Things was introduced. The prices of circuit components were analyzed, and the total cost of the entire system was less than 400 RMB. The system underwent network and energy consumption tests, and the transmission stability, reliability, and energy consumption were reasonable and consistent across different time periods, locations, and network connection methods. An average of 0.57 transactions per second (TPS) was processed by the NB-IoT network using the MQTT communication protocol, and 34.2 messages per minute were sent and received with a fluctuation of 1 message. The monitored device was found to have an average voltage of approximately 12.5 V, a current of approximately 0.42 A, and an average power of 5.3 W after continuous monitoring using an electricity meter. No additional power consumption was observed during communication. The performance of various sensors was tested through a 24-hour indoor test, during which temperature and lighting conditions showed different variations corresponding to day and night cycles. The readings were stably and accurately captured by the environmental sensors, demonstrating their suitability for long-term monitoring purposes. This system is can provide equipment cost and network selection reference values for remote or large-scale livestock monitoring devices.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： To achieve rapid on-site detection of aflatoxin B1 (AFB1) in agricultural and sideline products, a portable detection system based on differential pulse voltammetry (DPV) and STM32F103ZET6 as the core processor was designed. The system consists of two main parts: hardware detection devices and a mobile App, which are connected through Wi-Fi communication. The hardware detection equipment includes a DPV waveform generation circuit, constant potential circuit, and micro current detection module. The upper computer App was developed in an Android environment and completed tasks such as signal acquisition and data storage. After completing the design, experiments were conducted to verify the accuracy of the constant potential circuit and micro current detection module. The constant potential circuit accurately applied the voltage set by the program to the electrode, with a maximum error of 4 mV. The micro current detection module converts the current into a voltage signal according to the theoretical formula and amplifies it according to the theoretical amplification factor. The laboratory-made AFB1 sensor was used to effectively detect AFB1 in the range of 0.1 fg/ml to 100 pg/ml. The maximum relative error between the test results in the standard solution and the electrochemical workstation CHI760e was 7.37%. Furthermore, peanut oil samples with different concentrations of AFB1 were tested, and the results were compared to the CHI760e detection results as the standard, with a recovery rate of 96.8%~106.0%. Peanut samples with different degrees of mold were also tested and compared with CHI760e, with a maximum relative error of 7.10%.The system's portability allows it to be easily transported to different locations for on-site testing, making it an ideal solution for testing in remote or rural areas where laboratory facilities may be limited. Furthermore, the use of a mobile App for data acquisition and storage makes it easy to track and manage testing results. In summary, this portable detection system has great potential for widespread application in the rapid on-site detection of AFB1 in agricultural and sideline products.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Crops have diverse structures and complex growth environments. RGB image data can reflect the texture and color features of plants accurately, while 3D data contains information about crop volume. The combination of RGB image and 3D point cloud data can achieve the extraction of two-dimensional and three-dimensional phenotypic parameters of crops, which is of great significance for the research of phenomics methods. In this study, potatoe plants were chosen as the research subject, and RGB cameras and laser scanners were used to collect 50 potato RGB images and 3D laser point cloud data. The segmentation accuracy of four deep learning semantic segmentation methods, OCRNet, UpNet, PaNet, and DeepLab v3+ , were compared and analyzed for the RGB images. OCRNet, which demonstrated higher accuracy, was used to perform semantic segmentation on top-view RGB images of potatoes. Mean shift clustering algorithm was optimized for laser point cloud data processing, and single-plant segmentation of laser point cloud data was completed. Stem and leaf segmentation of single-plant potato point cloud data were accurately performed using Euclidean clustering and K-Means clustering algorithms. In addition, a strategy was proposed to establish a one-to-one correspondence between RGB images and point clouds of single-plant potatoes using pot numbering. 8 2D phenotypic parameters and 10 3D phenotypic parameters, including maximum width, perimeter, area, plant height, volume, leaf length, and leaf width, etc., were extracted from RGB images and laser point clouds, respectively. Finally, the accuracy of three representative and easily measurable phenotypic parameters, leaf number, plant height, and maximum width were evaluated. The mean absolute percentage errors (MAPE) were 8.6%, 8.3% and 6.0%, respectively, while the root mean square errors (RMSE) were 1.371 pieces, 3.2 cm and 1.86 cm, respectively, and the determination coefficients (R2) were 0.93, 0.95 and 0.91, respectively. The research results indicated that the extracted phenotype parameters can accurately and efficiently reflect the growth status of potatoes. Combining the RGB image data of potatoes with three-dimensional laser point cloud data can fully exploit the advantages of the rich texture and color characteristics of RGB images and the volumetric information provided by three-dimensional point clouds, achieving non-destructive, efficient, and high-precision extraction of two-dimensional and three-dimensional phenotype parameters of potato plants. The achievements of this study could not only provide important technical support for the cultivation and breeding of potatoes but also provide strong support for phenotype-based research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Pre-training refers to the process of training deep neural network parameters on a large corpus before a specific task model performs a particular task. This approach enables downstream tasks to fine-tune the pre-trained model parameters based on a small amount of labeled data, eliminating the need to train a new model from scratch. Currently, research on named entity recognition (NER) using pre-trained language model (PLM) only uses the last layer of the PLM to express output when facing challenges such as complex entity naming methods and fuzzy entity boundaries in the agricultural field. This approach ignores the rich information contained in the internal layers of the model themselves. To address these issues, a named entity recognition method based on progressive convolutional networks has been proposed. This method stores natural sentences and outputs representations of each layer obtained through PLM. The intermediate outputs of the pre-trained model are sequentially convolved to extract shallow feature information that may have been overlooked previously. Using the progressive convolutional network module proposed in this research, the adjacent two-layer representations are convolved from the first layer, and the fusion result continues to be convolved with the next layer, resulting in enhanced sentence embedding that includes the entire information dimension of the model layer. The method does not require the introduction of external information, which makes the sentence representation contain richer information. Research has shown that the sentence embedding output of the model layer near the input contains more fine-grained information, such as phrases and phrases, which can assist with NER problems in the agricultural field. Fully utilizing the computational power already used, the results obtained can enhance the representation embedding of sentences. Finally, the conditional random field (CRF) model was used to generate the global optimal sequence. On a constructed agricultural dataset containing four types of agricultural entities, the proposed method's comprehensive indicator F1 value increased by 3.61% points compared to the basic BERT (Bidirectional Encoder Representation from Transformers) model. On the open dataset MSRA, the F1 value also increased to 94.96%, indicating that the progressive convolutional network can enhance the model's ability to represent natural language and has advantages in NER tasks.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： To deal with the issues of low accuracy of apple leaf disease images recognition under weak supervision with only image category labeling, an improved CBAM-ResNet-based algorithm was proposed in this research. Using ResNet18 as the base model, the multilayer perceptron (MLP) in the lightweight convolutional block attention module (CBAM) attention mechanism channel was improved by up-dimensioning to amplify the details of apple leaf disease features. The improved CBAM attention module was incorporated into the residual module to enhance the key details of AlphaDropout with SeLU (Scaled Exponential Linearunits) to prevent overfitting of its network and accelerate the convergence effect of the model. Finally, the learning rate was adjusted using a single-cycle cosine annealing algorithm to obtain the disease recognition model. The training test was performed under weak supervision with only image-level annotation of all sample images, which greatly reduced the annotation cost. Through ablation experiments, the best dimensional improvement of MLP in CBAM was explored as 2. Compared with the original CBAM, the accuracy rate was increased by 0.32%, and the training time of each round was reduced by 8 s when the number of parameters increased by 17.59%. Tests were conducted on a dataset of 6185 images containing five diseases, including apple spotted leaf drop, brown spot, mosaic, gray spot, and rust, and the results showed that the model achieved an average recognition accuracy of 98.44% for the five apple diseases under weakly supervised learning. The improved CBAM-ResNet18 had increased by 1.47% compared with the pre-improved ResNet18, and was higher than VGG16, DesNet121, ResNet50, ResNeXt50, EfficientNet-B0 and Xception control model. In terms of learning efficiency, the improved CBAM-ResNet18 compared to ResNet18 reduced the training time of each round by 6 s under the condition that the number of parameters increased by 24.9%, and completed model training at the fastest speed of 137 s per round in VGG16, DesNet121, ResNet50, ResNeXt50, Efficient Net-B0 and Xception control models. Through the results of the confusion matrix, the average precision, average recall rate, and average F1 score of the model were calculated to reach 98.43%, 98.46%, and 0.9845, respectively. The results showed that the proposed improved CBAM-ResNet18 model could perform apple leaf disease identification and had good identification results, and could provide technical support for intelligent apple leaf disease identification providing.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Waterfowl farming in China is developing rapidly in the direction of large-scale, standardization and intelligence. The research and application of intelligent farming equipment and information technology is the key to promote the healthy and sustainable development of waterfowl farming, which is important to improve the output efficiency of waterfowl farming, reduce the reliance on labor in the production process, fit the development concept of green and environmental protection and achieve high-quality transformational development. In this paper, the latest research and inventions of intelligent waterfowl equipment, waterfowl shed environment intelligent control technology and intelligent waterfowl feeding, drinking water, dosing and disinfection and automatic manure treatment equipment were introduced. At present, compared to pigs, chickens and cattle, the intelligent equipment of waterfowl are still relatively backward. Most waterfowl houses are equipped with chicken equipment directly, lacking improvements for waterfowl. Moreover, the linkage between the equipment is poor and not integrated with the breeding mode and shed structure of waterfowl, resulting in low utilization. Therefore, there is a need to develop and improve equipment for the physiological growth characteristics of waterfowl from the perspective of their breeding welfare. In addition, the latest research advances in the application of real-time production information collection and intelligent management technologies were present. The information collection technologies included visual imaging technology, sound capture systems, and wearable sensors were present. Since the researches of ducks and geese is few, the research of poultry field, which can provide a reference for the waterfowl were also summarized. The research of information perception and processing of waterfowl is currently in its initial stage. Information collection techniques need to be further tailored to the physiological growth characteristics of waterfowl, and better deep learning models need to be established. The waterfowl management platform, taking the intelligent management platform developed by South China Agricultural University as an example were also described. Finally, the intelligent application of the waterfowl industry was pointed out, and the future trends of intelligent farming with the development of mechanized and intelligent equipment for waterfowl in China to improve the recommendations were analyzed. The current waterfowl farming is in urgent need of intelligent equipment reform and upgrading of the industry for support. In the future, intelligent equipment for waterfowl, information perception methods and control platforms are in urgent to be developed. When upgrading the industry, it is necessary to develop a development strategy that fits the current waterfowl farming model in China.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Agriculture is a basic industry deeply related to the national economy and people's livelihood, while it is also a weak industry. There are some problems with traditional agricultural risk management research methods, such as insufficient mining of nonlinear information, low accuracy and poor robustness. Artificial intelligence(AI) has powerful functions such as strong nonlinear fitting, endto- end modeling, feature self-learning based on big data, which can solve the above problems well. The research progress of artificial intelligence technology in agricultural vulnerability assessment, agricultural risk prediction and agricultural damage assessment were first analyzed in this paper, and the following conclusions were obtained: 1. The feature importance assessment of AI in agricultural vulnerability assessment lacks scientific and effective verification indicators, and the application method makes it impossible to compare the advantages and disadvantages of multiple AI models. Therefore, it is suggested to use subjective and objective methods for evaluation; 2. In risk prediction, it is found that with the increase of prediction time, the prediction ability of machine learning model tends to decline. Overfitting is a common problem in risk prediction, and there are few researches on the mining of spatial information of graph data; 3. Complex agricultural production environment and varied application scenarios are important factors affecting the accuracy of damage assessment. Improving the feature extraction ability and robustness of deep learning models is a key and difficult issue to be overcome in future technological development. Then, in view of the performance improvement problem and small sample problem existing in the application process of AI technology, corresponding solutions were put forward. For the performance improvement problem, according to the user's familiarity with artificial intelligence, a variety of model comparison method, model group method and neural network structure optimization method can be used respectively to improve the performance of the model; For the problem of small samples, data augmentation, GAN (Generative Adversarial Network) and transfer learning can often be combined to increase the amount of input data of the model, enhance the robustness of the model, accelerate the training speed of the model and improve the accuracy of model recognition. Finally, the applications of AI in agricultural risk management were prospected: In the future, AI algorithm could be considered in the construction of agricultural vulnerability curve; In view of the relationship between upstream and downstream of agricultural industry chain and agriculture-related industries, the graph neural network can be used more in the future to further study the agricultural price risk prediction; In the modeling process of future damage assessment, more professional knowledge related to the assessment target can be introduced to enhance the feature learning of the target, and expanding the small sample data is also the key subject of future research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Enhancing resource use efficiency in agricultural field management and breeding high-performance crop varieties are crucial approaches for securing crop yield and mitigating negative environmental impact of crop production. Crop stress sensing and plant phenotyping systems are integral to variable-rate (VR) field management and high-throughput plant phenotyping (HTPP), with both sharing similarities in hardware and data processing techniques. Crop stress sensing systems for VR field management have been studied for decades, aiming to establish more sustainable management practices. Concurrently, significant advancements in HTPP system development have provided a technological foundation for reducing conventional phenotyping costs. In this paper, we present a systematic review of crop stress sensing systems employed in VR field management, followed by an introduction to the sensors and data pipelines commonly used in field HTPP systems. State-of-the-art sensing and decision-making methodologies for irrigation scheduling, nitrogen application, and pesticide spraying are categorized based on the degree of modern sensor and model integration. We highlight the data processing pipelines of three ground-based field HTPP systems developed at the University of Nebraska-Lincoln. Furthermore, we discuss current challenges and propose potential solutions for field HTPP research. Recent progress in artificial intelligence, robotic platforms, and innovative instruments is expected to significantly enhance system performance, encouraging broader adoption by breeders. Direct quantification of major plant physiological processes may represent one of next research frontiers in field HTPP, offering valuable phenotypic data for crop breeding under increasingly unpredictable weather conditions. This review can offer a distinct perspective, benefiting both research communities in a novel manner.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Accurate and efficient monitoring of animal information, timely analysis of animal physiological and physical health conditions, and automatic feeding and farming management combined with intelligent technologies are of great significance for large-scale livestock farming. Deep learning techniques, with automatic feature extraction and powerful image representation capabilities, solve many visual challenges, and are more suitable for application in monitoring animal information in complex livestock farming environments. In order to further analyze the research and application of artificial intelligence technology in intelligent animal farming, this paper presents the current state of research on deep learning techniques for tag detection recognition, body condition evaluation and weight estimation, and behavior recognition and quantitative analysis for cattle, sheep and pigs. Among them, target detection and recognition is conducive to the construction of electronic archives of individual animals, on which basis the body condition and weight information, behavior information and health status of animals can be related, which is also the trend of intelligent animal farming. At present, intelligent animal farming still faces many problems and challenges, such as the existence of multiple perspectives, multiscale, multiple scenarios and even small sample size of a certain behavior in data samples, which greatly increases the detection difficulty and the generalization of intelligent technology application. In addition, animal breeding and animal habits are a long-term process. How to accurately monitor the animal health information in real time and effectively feed it back to the producer is also a technical difficulty. According to the actual feeding and management needs of animal farming, the development of intelligent animal farming is prospected and put forward. First, enrich the samples and build a multi perspective dataset, and combine semi supervised or small sample learning methods to improve the generalization ability of in-depth learning models, so as to realize the perception and analysis of the animal's physical environment. Secondly, the unified cooperation and harmonious development of human, intelligent equipment and breeding animals will improve the breeding efficiency and management level as a whole. Third, the deep integration of big data, deep learning technology and animal farming will greatly promote the development of intelligent animal farming. Last, research on the interpretability and security of artificial intelligence technology represented by deep learning model in the breeding field. And other development suggestions to further promote intelligent animal farming. Aiming at the progress of research application of deep learning in livestock smart farming, it provides reference for the modernization and intelligent development of livestock farming.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： In recent years, China's cold chain logistics industry has entered a stage of rapid development. At the same time, with the increase of greenhouse gas emissions, green and low-carbon transformation has become a new feature and direction of high-quality and healthy development of the cold chain industry to meet the future development needs of China's low-carbon economy. In view of this, in order to ensure the scientificity of China's low-carbon cold chain logistics evaluation system, in this paper, 30 indicators from the four levels of energy transformation, technological innovation, economic efficiency, and national policy based on different relevant levels were first preliminarily determined, and finally 14 indicators for building China's low-carbon cold chain logistics development evaluation system through consulting experts and the possibility of data acquisition were determined. Data from 2017 to 2021 were selected to conduct a quantitative evaluation of the development level of low-carbon cold chain logistics in China. Firstly, the entropy weight method was used to analyze the weight and obstacle degree of different indicators to explore the impact of different indicators on the development of low-carbon cold chain logistics; Secondly, a weighted decision-making matrix was constructed based on the weights of different indicators, and the technology for order preference by similarity to ideal solution (TOPSIS) evaluation model was used to evaluate the development of low-carbon cold chain logistics in China from 2017 to 2021, in order to determine the development and changes of low-carbon cold chain logistics in China. The research results showed that among the 14 different indicators of the established evaluation system for the development of low-carbon cold chain logistics in China, the growth rate of the use of green packaging materials, the number of low-carbon technical papers published, the proportion of scientific research personnel, the growth rate of cold chain logistics demand for fresh agricultural products, and the reduction rate of hydrochlorofluorocarbon refrigerants account for a relatively large proportion, ranking in the top five, respectively reaching 0.1243, 0.1074, 0.1066, 0.0982, and 0.0716, accounting for more than half of the overall proportion. It has a significant impact on the development of low-carbon cold chain logistics in China. From 2017 to 2021, the development level of China's low-carbon cold chain logistics was scored from 0.1498 to 0.2359, with a year-on-year increase of about 57.5%, indicating that China's low-carbon cold chain logistics development level was relatively fast in the past five years. Although China's low-carbon cold chain logistics development has shown an overall upward trend, it is still in the development stage.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Ensuring the stability of agricultural products logistics is the key to ensuring people's livelihood. The forecast of agricultural products logistics demand is an important guarantee for rational planning of agricultural products logistics stability. However, the forecasting of agricultural products logistics demand is actually complicated, and it will be affected by various factors in the forecasting process. Therefore, in order to ensure the accuracy of forecasting the logistics demand of agricultural products, many influencing factors need to be considered. In this study, the logistics demand of agricultural products is taken as the research object, relevant indicators from 2017 to 2021 were selected as characteristic independent variables and a neural network model for forecasting the logistics demand of agricultural products was constructed by using Informer neural network. Taking Henan province, Hubei province and Hunan province in Central China as examples, the logistics demands of agricultural products in the three provinces were predicted. At the same time, long short-term memory network (LSTM) and Transformer neural network were used to forecast the demand of agricultural products logistics in three provinces of Central China, and the prediction results of the three models were compared. The results showed that the average percentage of prediction test error based on Informer neural network model constructed in this study was 3.39%, which was lower than that of LSTM and Transformer neural network models of 4.43% and 4.35%. The predicted value of Informer neural network model for three provinces was close to the actual value. The predicted value of Henan province in 2021 was 4185.33, the actual value was 4048.10, and the error was 3.389%. The predicted value of Hubei province in 2021 was 2503.64, the actual value was 2421.78, and the error was 3.380%. The predicted value of Hunan province in 2021 was 2933.31, the actual value was 2836.86, and the error was 3.340%. Therefore, it showed that the model can accurately predict the demand of agricultural products logistics in three provinces of Central China, and can provide a basis for rational planning and policy making of agricultural products logistics. Finally, the model and parameters were used to predict the logistics demand of agricultural products in Henan, Hunan, and Hubei provinces in 2023, and the predicted value of Henan province in 2023 was 4217.13; Hubei province was 2521.47, and Hunan province was 2974.65, respectively. The predicted values for the three provinces in 2023 are higher than the predicted values in 2021. Therefore, based on the logistics and transportation supporting facilities in 2021, it is necessary to ensure logistics and transportation efficiency and strengthen logistics and transportation capacity, so as to meet the growing logistics demand in Central China.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： The new generation of information technology has led to the rapid development of the intelligent level of the cold chain, and the precise control of the development level of the smart cold chain is the prerequisite foundation and guarantee to achieve the key breakthrough of the technical bottleneck and the strategic layout of the development direction. Based on this, an evaluation index system for China's intelligent cold chain development from the dimensions of supply capacity, storage capacity, transportation capacity, economic efficiency and informationization level was conducted. The entropy weight method combined with the technique for order preference by similarity to ideal solution (TOPSIS) was used to quantitatively evaluate the development of intelligent cold chain in 30 Chinese provinces and cities (excluding Tibet, Hong Kong, Macao and Taiwan) from 2017 to 2021. The quantitative evaluation of the level of intelligent cold chain development was conducted. The impact of the evaluation indicators on different provinces and cities was analysed by exploratory spatial data analyses (ESDA) and geographically weighted regression (GWR). The results showed that indicators such as economic development status, construction of supporting facilities and informationization level had greater weight and played a more important role in influencing the construction of intelligent cold chain. The overall level of intelligent cold chain development in China is divided into four levels, with most cities at the third and fourth levels. Beijing and the eastern coastal provinces and cities generally have a better level of intelligent cold chain development, while the southwest and northwest regions are developing slowly. In terms of overall development, the overall development of China's intelligent cold chain is relatively backward, with insufficient inter-regional synergy. The global spatial autocorrelation analysis shows that the variability in the development of China's intelligent cold chain logistics is gradually becoming greater. Through the local spatial autocorrelation analysis, it can be seen that there is a positive spatial correlation between the provinces and cities in East China, and negative spatiality in North China and South China. After geographically weighted regression analysis, it can be seen that the evaluation indicators have significant spatial and temporal heterogeneity in 2017, with the degree of influence changing with spatial location and time, and the spatial and temporal heterogeneity of the evaluation indicators is not significant in 2021. In order to improve the overall development level of China's intelligent cold chain, corresponding development countermeasures are proposed to strengthen the construction of supporting facilities and promote the transformation and upgrading of information technology. This study can provide a scientific basis for the global planning, strategic layout and overall promotion of China's intelligent cold chain.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： The global energy is increasingly tight, and the global temperature is gradually rising. Energy efficiency assessment and carbon emission accounting can provide theoretical tools and practical support for the formulation of energy conservation and emission reduction strategies for the food cold chain, and is also a prerequisite for the sustainable development of the food cold chain. In this paper, the relationship and differences between energy consumption and carbon emissions in the general food cold chain are first described, and the principle, advantages and disadvantages of three energy consumption conversion standards of solar emergy value, standard coal and equivalent electricity are discussed. Besides, the possibilities of applying these three energy consumption conversion standards to energy consumption analysis and energy efficiency evaluation of food cold chain are explored. Then, for a batch of fresh agricultural products, the energy consumption of six links of the food cold chain, including the first transportation, the manufacturer, the second transportation, the distribution center, the third transportation, and the retailer, are systematically and comprehensively analyzed from the product level, and the comprehensive energy consumption level of the food cold chain are obtained. On this basis, ten energy efficiency indicators from five aspects of macro energy efficiency are proposed, including micro energy efficiency, energy economy, environmental energy efficiency and comprehensive energy efficiency, and constructs the energy efficiency evaluation index system of food cold chain. At the same time, other energy efficiency evaluation indicators and methods are also summarized. In addition, the standard of carbon emission conversion of food cold chain, namely carbon dioxide equivalent is introduce, the boundary of carbon emission accounting is determined, and the carbon emission factors of China's electricity is mainly discussed. Furthermore, the origin, principle, advantages and disadvantages of the emission factor method, the life cycle assessment method, the input-output analysis method and the hybrid life cycle assessment method, and the basic process of life cycle assessment method in the calculation of food cold chain carbon footprint are also reviewed. In order to improve the energy efficiency level of the food cold chain and reduce the carbon emissions of each link of the food cold chain, energy conservation and emission reduction methods for food cold chain are proposed from five aspects: refrigerant, distribution path, energy, phase change cool storage technology and digital twin technology. Finally, the energy efficiency assessment and carbon emission accounting of the food cold chain are briefly prospected in order to provide reference for promoting the sustainable development of China's food cold chain.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-08 Cooperative journals: 《农业图书情报学报》
Abstract： [Pupose/Significance] Evidence-informed decision-making is a means to bridge the gap between research and policy andevidence synthesis has become an important tool for evidence-based decision-making in many fields. However, evidence synthesis isresource-intensive, especially when it comes to scientific knowledge on complex issues.The efficiency of evidence synthesis cunrentlycannot meet the needs of decision makers.Crowdsourcing is seen as a potential way to improve the productivity of evidence synthesis.At present, the research and practice on the applications of crowdsourcing in evidence synthesis is still in its infancy.This study takes theapplication of crowdsourcing in the Cochrane Crowd citizen science project as an example to summarize the practical applications ofcrowdsourcing in evidence synthesis. The comprehensive analysis of the application mechanism of crowdsoucing in Coctrane Crowdproject will provide certain reference and inspiration for the use of crowdsourcing in evidence synthesis, so as to improve the productionefficiency of evidence synthesis and provide timely and powerful scientific informaticn for evidence-based decision-making.[MethodProcess]The application mechanism of crowdsourcing in the Cochrane Crowd citizen science project was analyzed from fivedimensions: crowdsourcer,vounteers, crowdsourcing task, Cochrane Crowd platform and effectiveness evaluation,using literatureresearch, network investigation, case analysis and other methods. Cochrane Crowd provides an easy-to-use interface for contributors toengage volunteers to participate and design , in addition to task-focused learming activities,diverse ways of accessing tasks, interactive online training modules and feedback mechanisms to improve the likelihood of volunteers' perfoming tasks corecty. At the same time,an agreement algorithm is provided at the platform level to ageregate the crowd classification results, which further improves thepossibility of correct classification of records. In addition, the platform has used the records identified by the crowd to build amachine-leaming model called as RCT classifier which can predct how likely a new citation is to be described an RCT to reduce themanual burden.[Resuts/Conclusions]Crowdsourcing is aneffective method to improve the efficiency of evidence synthesis and shortenthe production cycle. With comprehensive paticipant training and appropriate quality control mechanisms, it is possible to produce lighquality crowdsourcing results that meet the "gold standard" of evidence synthesis. In crder to motivate voluteers to paticipate andpromnote contnued engagement,participants are suggested to be provided with clear goals, clear tasks, and timely feedback or rewards.Interest and activity in introducing crowdsourcing into evidence synthesis is growing rapidly, and new tools and platfoms to faclitatecrowdsourcing also need to be further developed as researchers from different disciplines use crowdsourcing in the evidence synthesisprojects. In the future, the application of crowdsoucing in evidence synthesis in different fields and in different stages of evidencesynthesis should be further studied.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-08 Cooperative journals: 《农业图书情报学报》
Abstract： [Pupose/Significance] We used to process soybean leaf data by looking at them and process ata manually, but this method isvery inefficient. In order to improve the classication accuracy and efficiency of soybean leaf images, fiurther for storage and manage-ment of these images, we used the deep leaming technique to make an in-depth study of text data and image data of soybean leaves forthe image recognition and classication The application of deep leaming in agricultural data management mainly focuses on the imagerecognition and classification of plants and plant phenoftypes in karge-scale data, detection and classificaticn of agricutural diseases andpests, detection and classification of crops and weeds, and prediction of crop yield.Through case analysis, our sample data demonstratedthe application process of deep learning technology.[MethodProcess]This paper systematically described the whole process of classifi-cation and recognition of agricultural data by using the deep leaming technique.Through reconition and disease monitoring of plantleaves, the leaf momphology of soybean plants in the soybean experimental field of Heilongjiang Academy of Agricultural Sciences wastaken as an example. We analyzed the image features of soybean leaf mophology, and camied out the classification and recognition re-seach of soybean leaf morphology based on deep leaming. Deep learming techniques have replaced shallow classifiers that use manua1feature training and can identifly soybean leaves with a tigh degree of accuracy as long as suficient data are available for training.: Weadopted DenseNet model, which is suitable for common network model.The advantages of this model are that it has the best perfor-mance and the least storage requirements.First，we selected support vector machine (SVM) and random forest (RF) in traditional ma-chine leanming methods to identify soybean leaf mophology. Second, AlexNet and ResNet were selected to identify soybean leaf mor-phology.Finally, the recognition accuracy of different methods were compared with the DenseNet network adopted in this paper.[Re-sufts/Ccnctusions]Through the training of DenseNet model, the recognition accuracy of 94% was achieved, which successfully sotvedthe problems oflong time, low eficiency and low recognition accuracy of traditicnal methods in processing image classification of soy-bean leaves, and could meet the actual needs of agricultural image data classification.Future research efforts willstrive to colect a widerange of large and diverse data sets to facilitate soybean leaf recognition, and focus on developing reliable background removal tech-niques and incorporating other forms of data to improve the accuracy and reliability of soybean leaf recognition systems.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-08 Cooperative journals: 《农业图书情报学报》
Abstract： [Pupose(Significance] China has accelerated the construction of new infrastructure such as 5Gnefworks and data centers, andincltuded "smart library" in the 14t Five Year Plan and the Vision Outline for 2035, bringing significant development opportunies tothe construction of smart libraries. "No one can create a smart lirary except smart liborarians".Smart librarians are the source of vitality and power for the development of smart fibraries. The construction of smart libraries puts forward higher requirements for the qualityand ability of smart librarians. Developing the core elements of smart librarians is the focus of smart library construction, and also theprimary task of smart library construction.Method/Process] The research focus of the acadenmic community on smart libraians is nostlyfocused on the research on the capacity building and capacity evaluation system of smart librarians.Accordimg to the connotation oftheconcept of core literacy, ccmbined with the characteristics of smart litraries and the working characteristics of smart fbrarians, thispaper puts forward four core litrarian literacy elements of smart libraries: professional literacy, information service lieracy, cooperaticnand communication literacy, and self-development literacy.Among them professional literacy is the basis for the effective operation ofsmart fibraries; infomation service literacy is the guarantee of the service quality of smart libraries; the quality of cooperation andconunication is the condtion of open sharing and cooperation of smart libraries; self development literacy is the guarantee for thesustainable development of smat liboraries. On this basis, the development path of the core literacy of smart fibrarians is given in atargeted way. The paper contributes to the iterature by providing the first case study on the cutivation of smart librarians.[Results/Conclusions] Developing the core literacy of smat librarians is a dymamic, complex and systematic project, invowving thecoordinated development of pre-service education, post-service training, self-leaning and other aspects.By adopting nmutiple trainingmethods, creating a "meaning construction" environment, building a community of practice, establishing a progressive mechanism andother training measures, we will be able to improve the core quality of smart tibrarians, provide human resources guarantee for thesustainable development of smartlibraries, explore a new idea and new way for the training of smart litrarians, and has certain referencevahue and reference fiunction for the training of smart litrarians and team building. With the transformation and development of the smartlibrary to the meta-universe fibrary, new requirements will be put forward for the core quality of the smart fibrarians, and the strategy onthe culivation of the core quality of the smart librarians will also need to be changed. The process of cultivating the core quality of thesmart librarians is a process of sustainable development.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-08 Cooperative journals: 《农业图书情报学报》
Abstract： [Purpose/Significance] Digital live streaming is an important way for enterprises to cany out online activities.Incorporatingclear set qualitative comparative analysis into the research on improving the effectiveness of enterprise digital lve streanming activities is aninnovation in the application of configuration analysis methods and a new breakthrough in the field of improving activityeffectiveness. By identifying elements or combinations of elements that can create greater value for enterprise digital live streamingactivities, and adopting various ways to enhance these element combinations, the goal of improving the effectiveness of enterprise digitallive streaming activities can be achieved, which has innovative guiding significance for enterprise development.MethodProcess]Basedon the consideration of sample homogeneity, the background data of the digital live streaming activity of Guangxi Zhongyan IndustryCo., Ltd. was used as the data souce.Four influencing factors related to the dgital live streaming activity were selected as conditiona1variables using literature induction and problem oriented methods, and some conditional variables were assigned values using lineardiscriminant dimensionaity reduction. At the same time , the activity effect was used as the outcome variable, configuration analysis wasconducted on digital live streaming activities in the tobacco inustry through clear set qualitative comparative analysis, and combinedwith SOR model and the 4I theory, a configuration path for improving the effectiveness was generated.[ConchusicnsResufts] Researchhas found that a single factor does not constitute a necessary condition for improving the effectiveness of digital live streaming activities.There are two configuration paths: game assisted and topic supported, which can improve the effectiveness of digital live streamingactivities.The mechanism of action can be summarized as follows: rich topic types are conducive to meeting users' curiosity psychology,topic popularity and game quantity can ensure users' participation in live streaming, and user interaction work is the basic guaranee forimproving user retention.Meanwhile, these two paths can be applied to different foms of digital live streaming , and enterprises shouldchoose according to their own needs and refne the paths in practice. In addition, athough this article explores the key paths to improvethe effectiveness of digital live streaming activities, due to limitations in research samples and industry fields, it has not fully revealed thevarious factors that affct the efectiveness of digital live streaming. and there is a certain degree of subjectivity in the criteria forassigning variables. Therefore, further revisions and improvements are needed in future research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-08 Cooperative journals: 《农业图书情报学报》
Abstract： [Pupose/Significance] Digital communities have become a way for enterprises to manage users efficiently. The exitingresearch on digital community rarely considers the importance of user behavior information and user's customer life cycle value to themining of user preferences in digital community. This research aims to give full play to the digital communityts characteristics such asintuitive, convenient, interestng. and interactive properties so that the research resuts can benefit every user in their use of the digita1community and every enterprise in their user management.[MethodProcess] Aiming at the user groups in digital conmunity, this paperproposes a preference nining model ClV-Prefcrence mining (CLV-PM) based on Customer Lifetime Vatue (CLV). First, in order to reflect the real preferences of users, the three indicators of the RFM model are used to quantify user behavior information, and the groupcharacteristics of users are mined through K-mean t+t algonithm to generate user vahue category labels. Second, in order to consider thetimeliness and difference of users and enhance the model's cognition of preferences, this paper uses the entropy weight method to sotvethe indicator weights of each activity, obtains user CLV to constuct user-project scoring matrix, and uses the collaborative filteringalzorithm to predict user preferences.Finally, based on the user value category, user historical preference and user forecast preference,the user preference profile of target users in dgital community is generated, and feasible suggestions are put forward for the cperationand maintenance of target users according to the user prefcrence profile.[ResutsiConchusions] The user dataset of the "Wechatcommunity" management platfom can be divided into four user vahue categories: important vatue users, ow valbue users, rehuned usersand important retention users. Target users 16254 are important value users, and the operation strategy of "retention and maintenance" isadopted. The historical preferences are happy hop, sec-kill and other activities; the prediction preference is flying chess battle, guessingcode map and other activities; the target user preference sketch provides the basis for the operation and maintenance of users in thedgital community. In terms of data source, the CLV-PM model proposed in this paper drectly reflects user preferences based on userbehavior information and reduces the problem of score distotion.To provide a new perspective for the research of user behavior indigital community, the construction of user-project scoring matix based on userCLV fully considers the user value of digital communityand provides a new direction for the extension and application of CLV.However, due to limited research space, this paper did notconduct model evaluation research on the proposed model which can be further discussed in subsequent studies.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-08 Cooperative journals: 《农业图书情报学报》
Abstract： [PurposelSignificance] Muti-modal feafure analysis and service efficiency improvement of digital community consumers willhelp to provide a new vision for the construction of digital intelligent online ccmmunifies and provide new impetus for relevantdepartments to make decisions. In addition, although the curent research on digital consumption inchudes the relevant content of useranalysis, it mainly aims at the fomulation of detailed operation plans, and lacks the analysis of service efficiency improvement of digitalconmunities. On the other hand, the research on user value orientation for online service quality optimization is mostly based on profletechnology, which only considers the difference characterstics of a single target user, and lacks the horizontal comparison and differenceattribution research of omulti-modal features among groups.Based on this, this paper, from the perspective of vatue discovery, achievesclustering by aggregating user profles， analyzes the nulti-modal characteristics of consumer groups in digital communities, andproposes a serice efficiency improvement scheme.[Methodprocess] First, this paper analyzed the target consumers in the dgita1conmunity and established a cluster indicator system Then, users were grouped, and the muli-modal infcrmaticn profile of the targetgroup was restored based on group characteristics and inter-group interaction characterstics.Finally, it proposed the path to improve theefficiency of digital community services.In tems of technical implementation , the data related to consumer activities were extractedfrom the digital community, integrated, cleaned, and distributed to the storage bucket. The clustering indicator system was built throughfeafure mining and existing indicators, and the indicators were mapped to aims, and DBSCAN clustering was canried out on the basis ofusing AP to realize the image. After grouping and naming the characteristics analysis, interaction analysis, and dift and penetrationphenomenon analysis were carried out according to the characteristics of various groups. We extracted various parameters of the designof digital community consumption activities, and buit a decision variable finction to find the optimal behavior equifitrium conditions ofthe digital product supplier, consumer and digital community.Based on this, we built an efficiency improvement tree, and proposedcommunity service efficiency inmprovement stategies at the initial, middle and later stages of consumption activities.[Resuts/Conclusions] The empirical analysis results show that the model in this paper can first generate reascnable and efectiveclustering results, and then realize the classication of group characteristics and the analysis of inter-group infitration and dift. Theclustering results show six types of consumer groups: focus, center, special, sleeping. loss and general groups.Most groups wil haveuser penetraticn, and only general user groups will have inter-group drift. The service efficiency improvement model shows that the mostvaued group is the center and key group. The inadequacy of this study is that the applicatbility of the model to muti-souceheterogeneous data needs to be tested and there is still room for improvement in clustering granularity.
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