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In view of the problems such as frequent fluctuation of garlic price, lack of efficient forecasting means and difficulty in realizing the steady development of garlic industry, combined with the current situation of garlic industry and the collected data information. Taking Big Data platform of garlic industry chain as the core, using the methods of correlation analysis, smoothness test, co-integration test, and Granger causality test, this paper analyzes the correlation, dynamic, and causality between garlic price and young garlic shoot price. According to the current situation of garlic industry, the garlic industry service based on Big Data is put forward. It is concluded that there is a positive correlation between garlic price and young garlic shoot price, and there is a long-term stable dynamic equilibrium relationship between young garlic shoot price and garlic price fluctuation, and young garlic shoot price can affect garlic price. Finally, it is proposed to strengthen the infrastructure construction of garlic Big Data, increase the technological innovation and application of garlic Big Data technology, and promote the safety and security ability of the whole industry to promote the development of garlic industry.

With the rapid development of China's economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world., and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this paper, refers to previous research results and different evaluation methods, combined with artificial neural network, fuzzy theory, genetic algorithm, GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality. At the same time, for the problem that the two-level standard of air quality annual evaluation is not suitable for practical application, the four-level standard for annual air quality evaluation has been proposed, and its practicality has been verified through experiments. By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm, it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model, both in efficiency and accuracy.

In view of the problems existing in the processing of duck products, such as complicated technology, difficulties in information collection and information linkage, and lack of dedicated information collection equipment, the duck product traceability information of the Institute of Things automatic collection system was developed. Acquisition system is mainly composed of sensing terminals, bus structure, and host computer management system. Perceiving the terminal can automatically perceive the information of each link and transmit the perception information to the upper computer management system through the bus. The upper computer management system realizes the functions of storing the sensing information, intelligent analysis, and alarm prompting. The long-term operation results show that the system performance is stable and reliable; the collection of data is efficient, complete, and accurate; and the degree of automation of the system is high, which significantly improves the product quality and safety supervision capabilities of the company.

Wireless sensor networks (WSNs) have been pushed to the forefront in the past decade, owing to the advert of the Internet of Things. Our research suggests that the reliability and lifetime performance of a typical application in WSNs depends crucially on a set of parameters. In this paper, we implemented our experiments on the nearest closer protocol with the J-Sim simulation tool. We then analyze the closure relationships among the density, reliability and lifetime, and reveal the trade-off among them based on our analysis on the experiment results. Next, we propose five intelligent evaluation models that are applicable to such situations. Our research allows the WSN users to predict the significant evaluation parameters directly from the settings while costly simulations are no longer necessary.

In order to effectively solve the problems which affect the stable and healthy development of garlic industry, such as the uncertainty of the planting scale and production data, the influence factors of price fluctuation is difficult to be accurately analyzed, the difficult to predict the trend of price change, the uncertainty of the market concentration, and the difficulty of the short-term price prediction etc., the big data platform of the garlic industry chain has been developed. Combined with a variety of data acquisition technology, the information collection of influencing factors for garlic industry chain is realized. Based on the construction of the big data technology platform, the real-time synchronous acquisition, efficient storage and analysis of the planting, market, storage, processing, export and logistics information in five provinces and seven counties are realized. The application of the big data platform for garlic industry chain has realized the accurate acquisition of garlic planting area, the price and trend of market circulation and the information of export information, analyzed the fluctuation regulation of garlic price, and also realized the short-term precision prediction of garlic price.

Agricultural products are the foundation of the people’s survival, and the quality of agricultural products has always been the focus of attention of society and the government; the original agricultural product traceability system is too difficult to tamper with data due to the excessive concentration of data storage, it faces the challenge of fraudulent data tracing, and it is difficult for consumers to trust such traceability results. Moreover, the centralized storage method is not conducive to the centralized management of traceable data from many enterprises, and there will be problems of low traceability and difficulty in government supervision. The emergence of blockchain technology provides a new solution for data security problems of food traceability, its decentralization, anti-tampering and other characteristics and data encryption technology improve the difficulty of data fraud and ensure data security. If the blockchain is combined with the traceability of agricultural products, the safety of traceable data and the tampering of data can be guaranteed to the greatest extent, the producer’s production behavior can be regulated, and consumers’ confidence in food quality can be improved. This paper mainly proposes a framework of agricultural product traceability system based on Hyperledger technology, it uses blockchain to store the traceability data of agricultural products safely, and proposes a traceability model of agricultural products, which can cover the entire industrial chain of agricultural products, and consumers can query the authentic source of traceability of agricultural products [1].

With the gradual maturity of the Internet of Things technology, production intelligence and information has become more common. The level of automation in irrigation systems represents a major factor in the efficiency of agricultural development. This article has studied the intelligent automatic irrigation system of balcony flowers based on Internet of Things. MSP430F5438A single-chip microcomputer is used as the core microcontroller to collect soil moisture information through soil moisture sensors. The humidity information is uploaded to the server by the wireless communication module. The server responds to the client’s instructions and the real-time soil temperature and humidity to water the balcony flowers. The GPRS module sends the control command to the lower position machine, and the microcontroller transfers the instructions through the wireless transmission module. And to control the operation of the switch of solenoid valve. The entire irrigation system is based on the MSP430 single-chip microcomputer and has good features such as low power consumption and high stability.

To explore the conducive to China’s agricultural science and technology park of input and output of the related strategy and promote agricultural supply side structural reform in China, we use cobb-douglas production function model, combined with stepwise regression and principal component regression method, the analysis of 2015–2017 China national agricultural science and technology park enterprise data. We summarized the relationship between the input and output by cobb-douglas production function. The investment of labor force and capital investment and the annual output value were positive correlation, but the elasticity coefficient of labor input three years to show the trend of increasing year by year. Elasticity coefficient of capital in three years presents a decreasing trend year by year. By stepwise regression and principal component regression method, you can see that is not the total amount of investment. The greater the output, the more technology innovation factors will become the future main factors influencing the development of the national agricultural science and technology park. Therefore, China should strengthen agricultural science and technology innovation, cultivate agricultural science and technology talents. Develop agricultural science and technology innovation projects, so as to promote China’s agricultural supply-side structural reform.

In order to strengthen the scientific management of saline-alkali land and the accurate management of agricultural production, a scientific data governance platform for saline-alkali land was developed. Based on the data accumulation of big data platform, the relationship between wheat growth and meteorology was taken as the research object. Thirteen variables including atmospheric pressure, temperature, light, and precipitation were extracted from surface meteorological data for correlation analysis, and temperature, precipitation, and sunshine were selected as the characteristic variables; through discretization processing, we have finally determined the three indicators that can be categorized: accumulative temperature, sunshine hours, and temperature. Finally, the three indicators are combined with months to build a model of farming season and weather based on Apriori. The results show that when judging the farming season with the month as the index, the accuracy of the model is between 78.81 and 100%. When the temperature or accumulated temperature is taken as the index to judge the winter wheat farming time, the accuracy of the model is above 90%. This shows that accurate analysis of farming season can be achieved through big data correlation analysis, which provides technical support for the timely adoption of agricultural production.

With the rapid development of the “Internet plus”, the monitoring service of saline-alkali land has undergone profound changes, intelligent technologies such as big data, cloud computing and data mining are gradually being applied to analyze the changing trend of saline-alkaline lands. In order to study the dynamic changes rule of saline-alkali land, using the synchronous monitoring system of the Internet of things to monitor soil moisture, salt content and other relevant data continuously. Then taking the soil of Yong’an town in Kenli County as the research object, the change trend and correlation of the key factors such as soil temperature, salt and water and pH are studied. The results show that the soil water content and salt content had a relatively obvious seasonal change. The soil was alkaline soil, and the change of pH value was not obvious with the season. Correlation analysis shows that there is a significant positive correlation between water content and salt content in most months, and correlations between the two factors and the other soil factors are different. Through the research on the trend and correlation of key factors such as soil water and salt, revealed the factors that affect the change of water content and salt content in the area which provided reference for the improvement of soil salinization in the next step.

The main body of this article shows a system designed to improve the level of greenhouse irrigation and fertilization automation in China, while improving the utilization of water and fertilizer resources. Tomato greenhouse irrigation was used as an example to design an intelligent control system for tomato water and fertilizer integration in the greenhouse. The system is based on the optimal growth conditions of greenhouse tomatoes, combined with the impact of time on greenhouse tomatoes, using NB-IOT technology, information processing technology, information acquisition technology and other networking technology. The system has two sub-modules: tomato water and fertilizer integration intelligent control module and intelligent warning module. The system improves the precision of smart irrigation and fertilization in greenhouse tomatoes. In addition, in order to facilitate the user’s real-time supervision of the greenhouse, the system is equipped with a dedicated mobile phone APP. Experiments have shown that the system greenhouse environment information collection and uploading are very stable, and tomato growth environment is controlled reliably. The system satisfies the needs of modern greenhouse planting management, and significantly improves the benefits of tomato production.

In order to improve the level of automation and scientific management of greenhouse planting and reduce the output of labor force, greenhouse cucumber production as an example is studied, a kind of intelligent control system of cucumber production in the Internet of things is designed. On the basis of many factors such as weather, time, humidity, temperature and so on, in combination with the specific needs of daily production of cucumber, the greenhouse shutter control model, recovering stage model, early-flowering stage and result stage model is designed by using wireless communication technology, information collection technology, information processing technology and other intelligent technologies. It has realized the precision automatic control of the greenhouse. At the same time, in order to facilitate the user to see the growth of cucumber and the information of the greenhouse environment in real time, the mobile phone APP remote control, monitoring and warning services are provided. Experiments show that the system is stable in data transmission and reliable in environmental regulation, which meets the needs of intelligent control in a modern greenhouse environment, and significantly improves the efficiency of cucumber production.

In response to the increasing food safety problems of the meat product in the industry chain of livestock and poultry, and the actual demand of enterprises and consumers, combining the development status of the enterprises, considering the safety factors of animal breeding and product processing, this paper designed a food safety traceability system, based on modern internet of things technologies such as RFID, QR code, sensor technology and wireless communication technology, of which the traceability information covers all links of the whole industry chain. Compared with other traceability systems, the system in this paper has improved greatly in terms of its breadth, depth, and accuracy, which is an effective way to solve the food safety problem. The system provides the customers, enterprises, and the government department with multi-channel information traceability service, which has the real significance of the reduction food safety risk and improvement of recall efficiency of defective products.

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

In response to various problems existing in the current duck product industry chain. For example, decentralized of the industry chain, lack of data exchange between each link, lack of unified information collection equipment, and so on. An intelligent information management platform for duck product chain was developed, based on the specific needs of each link of the duck and poultry industrial chain. The platform is mainly composed of the information collection equipment and the intelligent management systems. The information acquisition equipment comprehensively uses the current well-perceived and reliable transmission technology of the Internet of Things to achieve the seamless collection of information in each link of the duck product industry chain and the seamless convergence of information in each link. The intelligent management system utilizes big data analysis technology to realize internal automation and digital management. The long-term test of the system shows that the data in each link of the system seamlessly connects. The data collected by the system is accurate and reliable. Its operation is simple and convenient. The system is highly scalable and suitable for use in production.