The agricultural resource and environment is the material basis to guarantee food supply. Since the reform and opening up, China's agricultural production has increased rapidly with the development of land contract system and market economy. Agricultural production scale and product diversification have reached unprecedented levels. However, the rapid development of agricultural production has paid a great resource and environmental cost. This paper mainly analyzes the agricultural resource and environment problems at a regional scale. Firstly, due to the area increase of the dry land turning into paddy field in the northeast China, the water irrigation has caused the water table to decrease continuously as well as the negative ecological impact. Secondly, because of large amount of input such as water resources and fertilizer in the agricultural production of the north China plain the groundwater level declines and water environment pollution occurrs. Thirdly, in the context of the rapid development of industrialization and urbanization, the soil acidification and heavy metal pollution in the south China are prominent in recent years, which has impacted food safety. Fourthly, the relationship between ecological barrier maintenance and grain production in arid and semi-arid regions of northwest China is discussed. Finally, some suggestions are put forward to strengthen the sustainable utilization of agricultural resources and environment.

In order to understand the global development trend of information science and technology in agriculture, this paper analyzes 215 global information sci-tech start-ups in agriculture from perspectives such as country, industry chain, technology, etc. The results show the followings. Information technologies such as Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI), image processing including remote sensing and spectroscopy, and new equipment such as drones and robots are becoming an important driving force for the development of modern agriculture. Currently, the start-ups of agricultural information technologies focus on agricultural production management services, especially precision planting, production decision advice and farm management. The United States is still the leader in agricultural information technologies, especially in precision agriculture and animal health breeding and monitoring. China focuses on improving the industrial system of modern agriculture by using information technologies, however, on a whole, Chinese start-ups in agriculture still lag far behind those in the U.S. with respect to information technologies. Therefore, to narrow the gap, China needs to improve the industry system and production system of modern agriculture with information technologies. in particular financial services for start-ups and commercialization services for agricultural information technologies, and promote the development of information science and technology in agriculture, especially those high technologies and equipment in agricultural production, such as IoT, big data, AI and robots.

Agricultural monitoring and early warning (AMEW) is a high-end tool of modern agricultural management and the high-risk nature of agriculture determines that AMEW must be concomitant with the development of modern agriculture. Scientific and technical issues in AMEW are reviewed in this paper. Firstly, the main contents and difficulties of data acquisition, data analysis and data application and service in AMEW are analyzed and discussed emphatically. Then, major scientific questions are raised including data perception in an uncertain state, the parameter rule under micro-data and big data, influence entanglement in complex system. Finally, important technology progresses including the agricultural standardization technology, advanced data acquisition technology and intelligent analysis system development are reviewed, and the main direction of future agricultural monitoring and early warning is proposed.

This paper analyzes the current situation and developing characteristics of the agricultural Internet of Things (AIOT) industry from the aspects of industry connotation, supply and demand subject, market-entry decision, etc. The result shows that China's AIOT industry has initially formed some agricultural dedicated software platforms, such as complete IOT industrial system, agricultural expert system, formula fertilization system, monitoring and warning system, and quality traceability system. And China's AIOT has developed rapidly in the fields of AIOT consulting and software services and has a broad application prospect. However, it has the problems, such as unbalanced development, lack of technical support, imperfect industrial market system and so on. Combined with China's Internet of things development environment, this paper proposes several countermeasures to accelerate the industrial development of AIOT industry in terms of strengthening research and platform construction of AIOT key technologies, accelerating the development of policy for AIOT special funds, and cultivating AIOT clusters, innovative AIOT business operation modes and service promotion.

Machine vision technology has been widely applied to various fields of agricultural production. In this paper we summarize the outstanding research results at home and abroad and elaborate the main forms of machine vision application in agriculture at this stage. We list the research results of machine vision in crop selection and quality inspection, plant growth information monitoring, field vision navigation, and other application directions. By analyzing its innovative image processing algorithms and the composition of machine vision systems, we show that the current application of machine vision in agriculture is still having problems such as poor reliability, high cost, and low level of intelligence. We analyze its subjective and objective reasons and put forward corresponding suggestions. Combining the current research and application of machine vision in various fields, we look forward to the future development direction of machine vision in agricultural applications. We believe that machine vision systems based on embedded processing modules and multi-technology integration will become the main development trend in the future. The deep learning model represented by convolution neural network will also become the core technology of future image recognition, which can greatly help solve the existing problems of machine vision in agricultural applications.

The paper introduces a number of navigation line image detection methods, including rice transplanting, paddy field manage, sowing wheat and wheat field managing. And it exhibites various navigation line detection results including farming, seeding corn, sowing cotton, wheat harvest, corn harvest and cotton harvesting. A developed prototype machine is also reported on its test in the cotton spraying field. The test result shows that at a speed of 4.7 km/h, the following error of cotton seedling column is 2cm, which proves the vision navigation is practical.

Virtual reality has become more important in the field of information technologies of modern agriculture,However, static geometric modeling predominate current VR applications, and dynamic models and interactions of real physical meanings or induced by real world physical rules have yet not been popularized. Because of the advantages by its nature. physics-based models and simulation an promote the reality of natural environment modeling, such as dynamic modeling of animals, plants, production scenes in agriculture, and enhance immersion of interactions in virtual reality. Based on the concept of virtual plant and digital plant, this paper introduces the innate character and significance of the physics-based method. Furthermore, the paper systematically summarizes the research developments of physics-based models and simulations in agricultural virtual reality environment, and analyzes application principles and common algorithms in morphology, kinematics, dynamics, biomechanics and modeling of environmental physical field. Finally, the possible effects and developing trends are discussed.