Teaching Reform and Practice of Animal Products Processing under the Background of Intelligent Agriculture

Effective food professional personnel training strategies are explored and implemented,and interdisciplinary talents of food science and engineering in accordance with the background of intelligent agriculture are cultivated from the aspects of construction of teaching staff,reform of teaching content,upgrading of teaching model,construction of industry-education integration platform,which is of great significance to the modernization development of Chinese animal products processing industry.

Review of operational management in intelligent agriculture based on the Internet of Things

This review aims to gain insight into the current research and application of operational management in the area of intelligent agriculture based on the Internet of Things(IoT),and consequently,identify existing shortcomings and potential issues.First,we use the Java application CiteSpace to analyze co-citation networks in the literature related to the operational management of IoT-based intelligent agriculture.From the literature analysis results,we identify three major fields:(1)the development of agricultural IoT(Agri-IoT)technology,(2)the precision management of agricultural production,and(3)the traceability management of agricultural products.Second,we review research in the three fields separately in detail.Third,on the basis of the research gaps identified in the review and from the perspective of integrating and upgrading the entire agricultural industry chain,additional research directions are recommended from the following aspects:The operational management of agricultural production,product processing,and product sale and after-sale service based on Agri-IoT.The theoretical research and practical application of combining operational management theories and IoT-based intelligent agriculture will provide informed decision support for stakeholders and drive the further development of the entire agriculture industry chain.

China National Research Center of Intelligent Equipment for Agriculture

China National Research Center of Intelligent Equipment for Agriculture （NRCIEA） was established in 2009 on the basis of Beijing Research Center of Intelligent Equipment for Agriculture. According to the development trend of world Intelligent Equipment for Agriculture （lEA） and China＇s needs of modern agriculture, NRCIEA is engaged in solving the key, fundamental and common technical problems in lEA.

Application of Deep Learning to Production Forecasting in Intelligent Agricultural Product Supply Chain

Production prediction is an important factor influencing the realization of an intelligent agricultural supply chain.In an Internet of Things(IoT)environment,accurate yield prediction is one of the prerequisites for achieving an efficient response in an intelligent agricultural supply chain.As an example,this study applied a conventional prediction method and deep learning prediction model to predict the yield of a characteristic regional fruit(the Shatian pomelo)in a comparative study.The root means square error(RMSE)values of regression analysis,exponential smoothing,grey prediction,grey neural network,support vector regression(SVR),and long short-term memory(LSTM)neural network methods were 53.715,6.707,18.440,1.580,and 1.436,respectively.Among these,the mean square error(MSE)values of the grey neural network,SVR,and LSTM neural network methods were 2.4979,31.652,and 2.0618,respectively;and theirRvalues were 0.99905,0.94,and 0.94501,respectively.The results demonstrated that the RMSE of the deep learning model is generally lower than that of a traditional prediction model,and the prediction results are more accurate.The prediction performance of the grey neural network was shown to be superior to that of SVR,and LSTM neural network,based on the comparison of parameters.

A Survey on Smart Agriculture:Development Modes,Technologies,and Security and Privacy Challenges

With the deep combination of both modern information technology and traditional agriculture,the era of agriculture 4.0,which takes the form of smart agriculture,has come.Smart agriculture provides solutions for agricultural intelligence and automation.However,information security issues cannot be ignored with the development of agriculture brought by modern information technology.In this paper,three typical development modes of smart agriculture(precision agriculture,facility agriculture,and order agriculture)are presented.Then,7 key technologies and 11 key applications are derived from the above modes.Based on the above technologies and applications,6 security and privacy countermeasures(authentication and access control,privacy-preserving,blockchain-based solutions for data integrity,cryptography and key management,physical countermeasures,and intrusion detection systems)are summarized and discussed.Moreover,the security challenges of smart agriculture are analyzed and organized into two aspects:1)agricultural production,and 2)information technology.Most current research projects have not taken agricultural equipment as potential security threats.Therefore,we did some additional experiments based on solar insecticidal lamps Internet of Things,and the results indicate that agricultural equipment has an impact on agricultural security.Finally,more technologies(5 G communication,fog computing,Internet of Everything,renewable energy management system,software defined network,virtual reality,augmented reality,and cyber security datasets for smart agriculture)are described as the future research directions of smart agriculture.

Popular Science Animation for the Future Intelligent Agricultural Ecology of Agricultural Products Innovation Design and Promotion

The form of agricultural products promotion is also constantly updated with the continuous development of science and technology in recent years.Intelligent agriculture gradually leads the scientific and technological process of agricultural products planting,production,promotion and other fields,making agricultural production more efficient and controllable.The use of popular science animation in the innovative design and promotion of agricultural products will help to drive the agricultural economy,conform to the current new situation,and improve the competitiveness of agricultural products with the help of scientific and technological strength and innovation consciousness in this environment.

Design and experiment of intelligent sorting and transplanting system for healthy vegetable seedlings

Healthy vegetable seedlings are surviving seedlings with good biological characteristics.Selective planting of healthy seedlings in the mechanized transplanting process can effectively avoid the reduction in yield caused by missed planting.Aiming at the current transplanting machinery that cannot achieve the selective planting of healthy seedlings,a healthy seedling intelligent sorting and transplanting system was proposed.The system consisted of a seedling delivery mechanism,sorting mechanism,photoelectric sensor,image sensor,PLC control system,and computer control system.It can realize automatic transmission of seedling trays,automatically identify the information of healthy seedlings in the trays and selectively transplant them.Also it can reduce the missed planting rate caused by the poor quality of plug seedlings after planting and the lack of seedlings in the hole.A sorting test of plug seedlings was carried out for the age-appropriate pepper plug seedlings cultivated in the factory.The results showed that the system had an average recognition accuracy rate of 89.14%and an average sorting success rate of 93.20%in the process of sorting suitable age pepper plug seedlings.The whole system can identify,sort and transplant the plug seedlings of appropriate age according to healthy information,and effectively avoid missing planting.This research can provide technical support for the intelligent upgrade of transplanting equipment.

Study on the Intelligent Control Model of a Greenhouse Flower Growing Environment

Intelligent control of the greenhouse planting environment plays an important role in improving planting efficiency and guaranteeing the quality of precious flowers.Among them,how to adapt the air humidity,temperature and light intensity in greenhouses to the different needs of the flower growth cycle is the key problem of intelligent control.Therefore,an intelligent flower planting environment monitoring and control system model(named)based on the Internet of Things and fuzzy-GRU network adaptive learning is proposed.The above three parameters in the greenhouse were used as model input parameters.The optimal growth humidity,temperature and illumination intensity of flowers are determined by the model,and the output temperature,humidity and illumination intensity act on the executing organ of the greenhouse room by the single-chip microcomputer.The model was evaluated using field greenhouse crops.The results show that the performance of this model is better than that of the PID model and fuzzy control model in simulation experiments and actual scene control.Compared with the flowers in the natural state,the plants of the flowers under systematic control were approximately 6 cm higher than those in the natural state on average,the blooming time of the flowers was approximately two days longer than that in the natural state,and the quality of the flowers was stable.

Remote monitoring system for maize seeding parameters based on Android and wireless communication

Most traditional maize seeding parameter monitoring devices use wired data transmission.The problems include wiring troubles,short transmission distances.And human-computer interaction display terminals are unique and usually customized rather than universal.A remote monitoring system for maize seeding parameters based on Android and wireless communication was developed in this study.The system used a single-chip microcomputer as the main controller and an infrared photoelectric sensor to capture seed information.The Android terminal application was used to set and display the seeder’s seed parameter information and monitor it.The Air202 communication module enabled remote data transmission,while the Global Positioning System(GPS)monitored the speed of the planter.By establishing a message queue telemetry transmission(MQTT)cloud served as a data freight station,data reception,storage and forwarding can be performed.Seeding parameters can generate Excel spreadsheets in real-time for easy data processing and storage.In order to verify the reliability of the system,the seeding parameter monitoring comparison test and the multi-terminal remote monitoring test were designed.The results of the seeding parameter monitoring comparison test showed that the monitoring system of this study had higher monitoring accuracy.The maximum average relative error of seeding parameter monitoring was 0.4%,which had high monitoring accuracy.The multi-terminal remote monitoring test showed that the monitoring system of this research can adapt many types of Android terminals,realize the wireless connection,and realize remote synchronous monitoring at different distances.This study provides a reference for intelligent remote monitoring and intelligent agriculture on unmanned farms.