With the wide applications of modern information technology in agriculture, agricultural intelligent technology revolution with manifestation of smart agriculture is coming. Smart agriculture is an advanced stage in the development of agricultural informatization from digitalization to networking to intelligence, it forms a new way of agricultural production, i.e., taking information and knowledge as the core elements, and integrating modern information technology such as internet, internet of things (IoT), big data, cloud computing, artificial intelligence, intelligent equipment, and so on, to realize agricultural information perception, quantitative decision-making, intelligent control, precise input, and personalized service. Smart agriculture is a milestone in the development of agriculture and has become the development trend of modern agriculture in the world. In this article, the policies, measures, and programs for encouraging the development of smart agriculture issued by Japan, the European Union, the United Kingdom, Canada, the United States, and other countries and regions were summarized, the development history from 1.0 version to 4.0 version of agriculture and development status of smart agriculture in China were also analyzed: remarkable results has achieved, at the end of 2017, the proportion of internet access in administrative villages reached 96%, 204,000 villages established the AgroSciences Information Agency, the retail sales of rural networks reached RMB 1.25 trillion Yuan, 426 cost-effective agricultural IoT products and technologies have been formed by the implementation of IoT pilot project. Behind the rapid development, smart agriculture in China still faces the problems of lack of basic research and technology accumulation, technologies such as sensors for agriculture, animal and plant models with intelligent decision-making, intelligent and precise operation equipment are the main short-boards. The pilot construction projects for the application of smart agriculture have been carried out all over the country, however, the role of display was greater than the actual effect. In order to solving the problems and achieving development demand, the strategic objectives of breaking through the core technologies, realizing the three major changes of "machine replacing manpower", "computer replacing human brain", and "independent technology replacing imports", improving the agricultural production level of intelligence and management network, accelerating the popularization of information services, and reducing application cost, providing farmers with personalized and precise information services that are affordable, and well-used, greatly improving agricultural production efficiency, and guiding the development of modern agriculture were proposed. Based on the analysis above, finally, eight key tasks including developing agricultural sensors, large-load agricultural UAV (unmanned aerial vehicle) protection systems, smart tractors, agricultural robots, agricultural big data, agricultural artificial intelligence, integrated applications and smart agricultural industry, and five policy recommendations including strengthening government support, formulating relevant subsidy policies, strengthening technical standards, and opening data sharing for the future development of smart agriculture in China were proposed.
Smart agricultural is a new form of agriculture that makes full use of human wisdom to develop agriculture. It is a new stage, new model and new pattern of agricultural development. The development of agricultural information technology is an inevitable requirement for smart agricultural. The new generation of core information technology, such as agricultural big data, cloud computing, Internet of things, artificial intelligence, can enable the innovative development of smart agricultural. It can provide new technologies, new methods and new solutions for the healthy development of smart agricultural. Agricultural informationization standardization is the premise to guide the progress and innovation of agricultural science and technology. It can lead the progress of agricultural science and technology and standardize the process of agricultural production. It is an urgent need for the development of smart agricultural. Agricultural Internet of things and agricultural application-specific chip are the core technologies and equipment for the development of smart agricultural. The application demand of agricultural Internet of things can promote the development of agricultural application-specific chip technology. The technological innovation of agricultural application-specific chip will promote the technological upgrading of agricultural Internet of things. Agricultural big data and cloud computing are powerful technical support for massive and complex agricultural information processing. The computing requirements of big data algorithms can promote the innovation and development of cloud computing technology. The improvement of cloud computing capability is more convenient for the application of big data algorithms and applications. Agricultural information security and blockchain are the key to guarantee the security of agricultural information, agricultural product quality certification system and agricultural. Agricultural artificial intelligence is the inevitable choice to improve agricultural labor productivity, reduce resource consumption, and efficient production. The innovation and application of artificial intelligence algorithm is an effective measure to realize smart agricultural. Agricultural plasma technology provides a new technological means for smart agricultural to produce more safer and more reassuring green organic agricultural products. It can be used in different stages of agricultural production, includes before, during and after production, to protect the healthy development of the whole agricultural production chain. The original innovation and autonomous control of the key technologies of smart agricultural will surely lead the healthy development of smart agricultural.
Intelligent equipment for livestock production is one of the components of intelligent agricultural machinery equipment, and is the focus of technology development in international agricultural equipment industry. This paper reviewed the current situation and development trend of intelligent equipment for livestock production systems nationally and internationally, including electronic feeding stations, animal farming robots, and many supporting intelligent facilities within the animal house. The features and performance characteristics of the equipment were discussed. The development of intelligent equipment for livestock production systems mainly focused on pigs and dairy cows including electronic sow feeding station, lactating sow precision feeding system, electronic cattle feeding station, automatic cattle feeding system, cattle feed pusher and dairy cow milking robot. The development and application of intelligent livestock equipment such as the electronic feeding stations and feeding robots, have significantly increased the production efficiency and saved labor cost in both pig and dairy farms. In addition, it also contributed to improve both of the animal and farmer welfare. However, there is still considerable room to get the application of intelligent livestock equipment improved in practice. For example, the animals have to be trained to get used to the intelligent facilities. On the other hand, the intelligent facilities are also required to identify individual animal or animal organ more accurately in order to further increase the production efficiency. Therefore, the key features in the further development of intelligent livestock equipment would be smarter, more convenient, more reliable, and more economical. At the meantime, it should be a highly integrated and coordinated intelligent system including intelligent facilities, well trained staff, good animal welfare, and comfortable environment. Therefore, the industrial application of the intelligent livestock equipment should be integrated with the local farming practice and fitted with the layout of animal houses in order to increase the efficiency of the equipment, and consequently, to improve animal welfare. The systematical combination of intelligent facilities and animal physiology, animal growth, and animal behavior could contribute to the dynamic interactions between the equipment and animal. Finally, it was concluded that the development of intelligent equipment should be coordinated with the theory of animal production, the function of animal products and the innovation of farming practice. And it also should be continuously updated to promote the transformation and upgrading of animal husbandry industry.
With the development of China's modern agriculture, information agriculture and smart agriculture, and the implementation of national rural revitalization strategy, there are very strong demands for timely and effective retrieving information for agricultural environment, production conditions, status, and procedure. Because of the inherent characteristics of agriculture, satellite remote sensing is one of the critical techniques in agricultural information acquisition. Based on the analysis of the applications of agricultural remote sensing satellites abroad and in China, the authors analyzed the technical demand and engineering demand of China's remote sensing satellites development according to the demand of modern agricultural development, in order to provide suggestions for the construction agricultural remote sensing satellite system in the national digital agriculture system. In developed economies, remote sensing satellites that can be used for agricultural applications have formed constellations or systems for integrative observation. Their designs of payloads and sensors onboard remote sensing satellites have taken full account of the demand for agricultural applications. Their technical innovation and information retrieval capability have been greatly enhanced in agricultural applications of satellite remote sensing. In contrast with that in the advanced foreign countries, the agricultural satellite remote sensing applications in China have quite a few problems and shortcomings. We rely mainly multi-spectral remote sensing systems, which leads to inadequate observation elements in agricultural remote sensing applications. Limited by the performance of remote sensing sensors and the inadequate ability of remote sensing satellite ground application system, there is a certain gap between quantitative remote sensing monitoring means in China and foreign developed countries. Based on a comprehensive analysis of the current and future demands of agricultural remote sensing applications in China, this paper suggests the agricultural requirements for the application capability and equipment of remote sensing satellites. It is suggested that a constellation system of agricultural satellites flying in a tandem sequence should be constructed. The constellation has multi-spectral, hyperspectral, infrared and microwave sensors, which can acquire the comprehensive features of the same objects in the same temporal phase, and thus obtain the data with high spatial-temporal consistency and consistency of solar illumination conditions. The precision of multi-source data fusion can comprehensively provide multi-scale remote sensing products with different bands, different polarization, active/passive, microwave/optical fusion. With help of this advanced agricultural remote sensing satellite system and national spatial infrastructure in China, it will enhance the capability to promote the rapid development of agricultural remote sensing technology and the integration of three-dimensional space-air-ground based digital agriculture in China.
In modern agriculture production, to obtain real-time, accurate and comprehensive information of the farmlands is necessary for farmers. Unmanned Aircraft System (UAS) is one of the most popular platforms for agricultural information monitoring, especially the multi-rotor aircraft due to its simplicity of operation. It is easy to control the speed and altitude of multi-rotor aircraft, even at low altitude. The above features enable multi-rotor UAS to acquire high-resolution images at low altitudes by integrating different imaging sensors. The aim of this work was to develop an octocopter UAS for agricultural information monitoring. In order to obtain the high-resolution aerial images of the entire experimental field, the Sony Nex-7 camera was attached to the aircraft. According to the real-time position of the aircraft got from global position system (GPS) and inertial measurement unit (IMU), the flight control system of the aircraft will send signals to control the camera to capture images at desired locations. Besides, position and orientation system (POS) and an illuminance sensor were loaded on the aircraft to get the location, shooting angle and ambient illumination information of each image. The system can be used to collect the remote sensing data of a field, and the performance was comprehensively evaluated in the field of oilseed rape experimental station in Zhuji, Zhejiang Province, China. The result shows that the system can keep the camera optical axis perpendicular to the ground during the operation. Because the effective communication was established between the mission equipment and the flight control system, the UAS can accurately acquire the images at the pre-defined locations, which improved the operation efficiency of the system. The images collected by the system can be mosaicked into an image of the whole field. In summary, the system can satisfy the demand for the agricultural information collection.
According to the demand of digitized analysis and visualization representation of crop yield formation and variety adaptability analysis, aiming at improving the timeliness, coordination and sense of reality of crop simulation model, key technologies of crop growth process simulation model and morphological 3D visualization were studied in this research. The internet of things technology was applied to collect the field data. The multi-agent technology was used to study the co-simulation method and design crop model framework. Winter wheat (Triticum aestivum L.) was taken as an example to conducted filed test, the 3D morphology visualization system was developed and validated. Taking three wheat varieties, Hengguan35 (Hg35), Jimai22 (Jm22) and Heng4399 (H4399) as research objects, logistic equation was constructed to simulate the change of leaf length, maximum leaf width, leaf height and plant height. Parametric modeling method and 3D graphics library (OpenGL) were used to build wheat organ geometry model so as to draw wheat morphological structure model. The R2 values of leaf length, maximum leaf width, leaf height and plant height were between 0.772-0.999, indicating that the model has high fitting degree. F values (between 10.153-4359.236) of regression equation and Sig. values (under 0.05) show that the model has good significance. Taking wheat as example, this research combined wheat growth model and structure model effectively in order to realize the 3D morphology visualization of crop growth processes under different conditions, it will provide references for developing the crop simulation visualization system, the method and related technologies are suitable for other field crops such as corn and rice, etc.
Agriculture of China is a typical agricultural system of small producer and large market. Producers are too scattered. Agricultural foundation remains weak in the vast rural areas, especially poverty-stricken areas. All of the above have built up a hedge for the high input of information industry and been a serious impediment to agricultural informationalization. As the underlying technology of electronic tokens, blockchain has become the current research hotspot, and been applied in finance, logistics, electronic commerce, information traceability and so on. Blockchain is a distributed storage and computation system, which is decentralized, so it is highly compatible with the distributed economic system. It will be a comprehensive solution to the agricultural status that is "scattered, small and weak". The book China Blockchain Technology and Application Development Whitepaper 2016 gives guidance suggestions for the application of blockchain in different industries including agriculture. This article is proposed by combining the specific status of Chinese agriculture and the technical characteristics of the block chain. Blockchain will play a very important rule in information gathering, resource integrating, profits sharing and backtracking information. Framework of blockchain for agricultural product with 7 levels of information gathering layer, data layer, network layer, consensus layer, excitation layer, contract layer and application layer was designed based on generalized blockchain according to actual situation of agriculture in China. The function of information gathering layer and data layer weve used to storage encrypted information that is acquired by IoT node in a distributed way. The Network layer was designed with a semi-distributed topological structure based on the original Peer-to-Peer distributed network structure in the blockchain by adding the supper notes. In the consensus layer, the DPOS was implied install of POW, therefore, there is no need for intensive computing. Due to the use of smart contact in the contract layer, transactions can be completed automatically in the absence of intermediaries. Furthermore, members of the Blockchain Union can also get repay by participating in the consensus with the smart contracts. The application layer was designed to provide the interface for application of government, bank, enterprise, producer and consumers. This framework can provide flexible mechanism of distributed storage, complete information consensus system, reliable information tamper-proof function and practical incentive reward measures. Subsequently, the above functions have been explained in the agricultural products quality safety traceability and application for the agricultural product market information transparency in more detail. However, the application of block chain in the field of agriculture is still in the stage of exploration, the technology is far from mature, and still need to be perfected in the process of application gradually.
In aquaculture, feeding intensity can directly reflect the appetite of fish, which is of great significance for guiding feeding and productive practice. However, most of the existing fish feeding intensity evaluation methods have problems of low observation efficiency and low objectivity. In this study, a fish feeding intensity evaluation method based on near-infrared machine vision was proposed to achieve an automatic objective evaluation of fish appetite. Firstly, a near-infrared image acquisition system was built by using near-infrared industrial camera. After a series of image processing steps, the gray level co-occurrence matrix was used to extract the texture feature variable information of the image, including contrast, energy, correlation, inverse gap and entropy. Then the data set were constructed by using these five feature variables as input vectors, and the support vector machine classifier was trained. Among them, the optimal penalty coefficient c and kernel function parameter g were selected by grid search. Finally, the trained images were used to classify the feeding images of fish. And ultimately, the evaluation of fish feeding intensity was realized. The results show that the accuracy of the evaluation could reach 87.78%. In addition, this method does not need to consider the impact of reflections, sprays and other factors on image processing results, so it has strong adaptability and can be used for automatic and objective evaluation of fish appetite, thus provide theoretical basis and methodological support for subsequent feeding decisions.
With the development of computer and automation control technology, robots have gradually entered the field of agricultural production. The application of agricultural robots can improve labor productivity, product quality and working conditions, solve the problem of labor shortage, and promote the intellectualization of agricultural production process. Fruit harvesting is the most time-consuming and laborious part of agricultural production. Since the skin of fruit is relatively fragile, it is easy to cause damage in the process of grasping. Therefore, some flexibility is necessary for the grasping device. As the end of the picking, robot directly acts on the part of the grasping object, the manipulator has attracted more and more attention of scientific researchers because of its light weight, small size, low energy consumption, high flexibility and low cost. Manipulator is the core component of robot, which is installed on the end of picking robot and acting on the object directly. In order to improve universality and flexibility, reduce the damage to the fruits, and shorten the design cycle, the flexible manipulator with simple structure and self-adaptive function was designed to achieve favorable grasp of fruits. The manipulator developed based on 3D printing has the advantages of rapid prototyping, low experimental cost and easy to assemble, etc. Flexible manipulator consists of flexible finger, wrist, base and pneumatic components. Its general action process is opening, grasping, moveing and putting down. However, flexible manipulator combines the two processes of moving and putting down into swallowing, which reduces the execution of the motion and improves the grasping performance and efficiency of the manipulator. Pneumatic components and wrist were printed from flexible materials and the material is thermoplastic urethane and polylactic acid respectively. The wrist is an integral part with flexibility. The use of pneumatic components can achieve the wrist bending, driving flexible fingers self-adaptive deformation to grasp the fruit. The manipulator is placed on the vertical sliding platform of the four-wheel platform, which can move up and down, and the four-wheel platform can move freely in all directions. The single wrist has two rotational degrees of freedom. The kinematics model of single wrist was established by combining constant curvature deformation and D-H coordinate method. On this basis, the functional validation test and safety test of flexible manipulator were carried out. In the safety test, the thin pressure sensor was used as the detection element of the contact force signal between the finger and the grasping object. The experiment results show that the pneumatic components of the flexible manipulator meet the design requirements and the driving wrist is flexible. The manipulator has certain flexibility, and can adapt to the shape of the fruit for self-adaptive grasping. The self-adaptive grasping effect of the manipulator is remarkable, and the fruit skin is intact. Moreover, the flexible manipulator has a favorable self-adaptive function based on the structural design and the complexity of the control system is deduced. In addition, it will provide reference for the design of the flexible grasping mechanism.
Rapid acquisition and analysis of crop information is the precondition and basis for carrying out precision agricultural practice. Variable spraying and agricultural operation management based on the actual degree of crop diseases, pests and weeds can reduce the cost of agricultural production, optimize crop cultivation, improve crop yield and quality, and thus achieve precise agricultural management. In recent years, with the rapid development of UAV industry, UAV agricultural remote sensing technologies have played an important role in monitoring crop diseases, insects and weeds because of high spatial resolution, strong timeliness and low cost. Firstly, this research introduces the basic idea and system composition of precision agricultural aviation, and the status of UAV remote sensing in precision agricultural aviation. Then, the common UAV remote sensing imaging and interpreting methods were discussed, and the progress of UAV agricultural remote sensing technologies in detecting crop diseases, pests and weeds were respectively expounded. Finally, the challenges in the development of UAV agricultural remote sensing technologies nowadays were summarized, and the future development directions of UAV agricultural remote sensing were prospected. This research can provide theoretical references and technical supports for the development of UAV remote sensing technology in the field of precision agricultural aviation.
UAV plant protection operation faces very complicated environmental conditions. On one hand, its ultra low altitude operations are vulnerable to ground structures and basic hydropower facilities; on the other hand, the effectiveness of plant protection operation is strong, and it is necessary to spray the pesticides to the specific parts of crops at the prescribed time so as to ensure good pesticide application effect. At present, UAV plant protection technology mainly refers to the existing mature technology and flight platform in general aviation field to basically "fly and spray". However, the lack of penetrating research and theoretical guidance on environmental perception in farmland operation, the movement mechanism of droplets under the rotor airflow, and the penetrability of the droplet to different crops canopy lead to low penetration rate of the UAV plant protection operation, easy drifting, frequent accidents, large damage probability and low comprehensive operational efficiency. Benefiting from the breakthroughs in artificial intelligence, parallel computing technology and intelligent hardware, the UAV plant protection technology is developing in the direction of intellectualization, systematization and precision. The real-time perception of the environment under non established conditions, intelligent job decision method based on intelligent recognition of crop diseases and pests, the control of the toward-target pesticide spraying control based on the variable of wind field droplet deposition model and the data based job evaluation system have gradually become the key technology of the UAV intelligent plant protection. The manuscript analyzed and summarized the research status and technical achievements in the field of UAV intelligent plant protection from the field information perception, the modeling and optimization control of accurate pesticide application, the evaluation and monitoring of the operation effect. Based on the existing research, the research also predicted the development trend of the key technologies of intelligent UAV plant protection in the future. The clustering method of hyper-spectral image acquisition and computational intelligence based deep learning recognition will become the key technology for real-time and efficient acquisition of crop target information in plant protection work, which greatly improves the accuracy of remote sensing information inversion recognition; machine vision and multi machine cooperative sensing technology can acquire dynamic information of field operation at multiple levels and time; the high precision droplet spectrum control technology independently controlled by nozzle design and the precision variable spraying control technology based on the wind field model can further improve the droplet deposition effect and reduce the liquid drifting; the breakthrough of high accuracy mesh solution technology will change the prediction mode of droplet drift from artificial experience judgment to computer simulation and numerical deduction; the job path planning technology will greatly improve the efficiency of multi machine and multi area operation and reduce the distance of invalid operation; the job quality evaluation based on the real-time data of the sensor and the operation supervision system of large data technology will replace people to effectively control the process of the UAV plant protection operation, achieve data and transparency of plant protection, and ensure the process is observable and controllable.
Corn is one of the most important food crops in China, and the occurrence of disease will result in serious yield reduction. Therefore, the diagnosis and treatment of corn disease is an important link in corn production. Under the background of big data, massive image data are generated. The traditional image recognition method has a low accuracy in identifying corn plant diseases, which is far from meeting the needs. With the development of artificial intelligence and deep learning, convolutional neural network, as a common algorithm in deep learning, is widely used to deal with machine vision problems. It can automatically identify and extract image features. However, in image classification, CNN still has problems such as small sample size, high sample similarity and long training convergence time. CNN has the limitations of expression ability and lack of feedback mechanism, and data enhancement and transfer learning can solve the corresponding problems. Therefore, this research proposed an optimization algorithm for corn plant disease recognition based on the convolution neural network recognition model combining data enhancement and transfer learning. Firstly, the algorithm preprocessed the data through the data enhancement method to expand the data set, so as to improve the generalization and accuracy of the model. Then, the CNN model based on transfer learning was constructed. The Inception V3 model was adopted through transfer learning to extract the image characteristics of the disease while keeping the parameters unchanged. In this way, the training process of the convolutional neural network was accelerated and the over-fitting degree of the network was reduced. The extracted image features were used as input of the CNN to train the network, and finally the recognition results were obtained. Finally, the model was applied to the pictures of corn diseases collected from the farmland to accurately identify five kinds of corn diseases. Identification test results showed that using data to enhance the CNN optimization algorithm and the migration study on the average recognition accuracy main diseases of com (spot, southern leaf blight, gray leaf spot, smut, gall smut) reached 96.6%, which compared with single CNN, has greatly improved the precision and identification precision by 25.6% on average. The average processing time of each image was 0.28 s, shortens nearly 10 times than a single convolution neural network. The experimental results show that the algorithm is more accurate and faster than the traditional CNN, which provides a new method for identification of corn plant diseases.
Accurate segmentation and localization of apple objects in natural scenes is an important part of wisdom agriculture research for information perception and acquisition. In order to solve the problem that apples recognition and positioning are susceptible to occlusion of leaves in natural scenes, based on the K-means clustering segmentation algorithm, the object recognition algorithm based on convex hull theory was proposed. And the algorithm was compared with the object recognition algorithm based on removing false contours and the full-contour points fitting object recognition algorithm. The object recognition algorithm based on convex hull theory utilized that apples were like circle, combining K-means algorithm with Otsu algorithm to separate fruit from background. The convex polygon was obtained by convex hull theory and fit it circle to determine the position of the fruit. To verify the effectiveness of the algorithm, 157 apple images in natural scenes were tested. The average overlap rates of the object recognition algorithm based on convex hull theory, the object recognition algorithm based on removing false contour points and the full-contour points fitting object recognition algorithm were 83.7%, 79.5% and 70.3% respectively, the average false positive rates were 2.9%, 1.7% and 1.2% respectively, and the average false negative rates were 16.3%, 20.5% and 29.7% respectively. The experimental results showed that the object recognition algorithm based on convex hull theory had better localization performance and environmental adaptability compared to the other two algorithms and had no recognition error, which can provide reference for occluded fruits segmentation and localization in the natural scenes.
Wireless sensor network (WSN) has been widely deployed in precision agriculture to improve the crop production. However, we still face many challenges for large-scale farmland habitat monitoring, the energy shortage for battery-powered sensor nodes, the complicated propagation environment for wireless signals during different growing-up periods of the crops, the optimization of the coverage of the sensor nodes, etc. In order to guarantee the holeless coverage of the sensor nodes during the whole life of the crops, the WSNs are typically deployed with high density. Therefore, some of the sensors in the network are redundant in certain growing-up periods of the crops. And also the data collected by each sensor node may have strong temporal correlation. Recently, compressive sensing (CS) has received much attention due to its capability of reconstructing sparse signals with the number of measurements much lower than that of the Nyquist sampling rate. With the rapid progress of CS based sparse representation, matrix completion (MC) theory was proposed very recently. According to the MC theory, a low-rank matrix can be accurately rebuilt with a few number of entries in the matrix. Matrix completion provides the advantage of sampling small set of data at sensor nodes without requiring excessive computational and traffic loads, which meets the requirement of energy-efficient data gathering and transmitting in WSNs. In this research, by considering the characteristics that the sensor nodes are redundant in some growing-up periods of the crops and the data collected by sensor-nodes usually share a strong spatial and temporal correlation among them in WSNs for large-scale farmland habitat monitoring, we put forward a MC based two-step energy saving optimization algorithm to reduce both the energy consumption of the data acquisition and data transmission process in WSNs and achieved the purpose of prolonging the network lifetime. Firstly, through the measurement of the sensor node's data information, we found the non-redundant nodes by considering the spatial correlation of the data from the sensor nodes. We would close the data acquisition units of the remaining redundant nodes and make them only transmit data as relay nodes. Secondly, we took advantage of the partial sampling scheme in matrix completion to further reduce the quantity of data. Thereby, we could reduce the energy consumption on both data collection and transmission process of the wireless sensor network. The experiment results show that the proposed algorithm reduces 83% working nodes in the network, and therefore reducing the energy cost of the network.
The human society is entering the era of big data and data is becoming one of the key production elements. It is thus critical to develop the China's data-driving digital agriculture system, which would greatly promote the construction of digital China, stimulate the agriculture high-quality development and improve the agricultural competitiveness at the global market. To achieve this goal, strong integration of information is needed from multi-sources, multi-sensors, and multi-scales. This research, from the perspective of agricultural information science, describes the new framework of satellite, aerial, and ground integrated (SAGI) digital agriculture system for comprehensive agricultural monitoring, modeling, and management. The SAGI system differs from traditional digital agriculture systems and includes 5 important functionalities which are resource survey, production controlling, disaster monitoring, market early-warning and decision supporting. To make the system running in operation, it is necessary to first build an observation system, which integrates the satellite, aerial, and ground in-situ observation systems to capture more sophisticated, accurate and reliable data at different scales. The system is extremely needed for China, a large country with a great geographic difference, diverse agricultural cultivation and multiple agricultural traditions. This observing system helps to form the agricultural big data for subsequent data analysis and data mining. Secondly, using the big data collected, 4 key digitalization and monitoring tasks targeting at resource property right, production process, natural disaster and market status should be implemented so as to transform the data to knowledge. In this process, some diagnosis algorithms and models are developed to understand the growth and health of crops and animals, as well as their interaction with the agro-environment. With the above support, a management system covering the full range of agricultural production, processing, selling, management and services should be established to provide the rapid and reliable information support to decision-making as well to the local farming management, thereby guaranteeing agricultural sustainability and national food security. Thirdly, some key fields for future science and technology innovation to support the applications of the SAGI system should to be enhanced such as the standardization designing, innovation in technologies and instruments, system integration and platform development. Finally, considering the complicated and integrative characteristics of this SAG system, this research also proposed some recommendations such as holistic planning, science-technology innovation, resource sharing, multi-stakeholders participation, and expansion of application fields, so as to drive this idea to the reality.
Accurate identification of agricultural production environment information and agricultural production characteristics, comprehensive classification of meteorological, soil and crop multi-source data, are the bases for improving the efficiency of agricultural resource utilization and optimizing the structure of agricultural cultivation. Based on the meteorological data of nearly 20 years and the statistics of com yield, this study first constructed a database of spatial and temporal distribution characteristics of climate resources and com production in North China Plain, and there were significant spatiotemporal changes in rainfall, activity accumulated temperature, sunshine hours, solar radiation and corn yield. By using the method of fine crop planting regionalization, the summer com planting areas in the North China plain were divided into 5 categories: the extremely unsuitable area, the unsuitable area, the less suitable area, the suitable area, and the most suitable area, the proportions of each type of area in the total area is about 10%, 11%, 25%, 30%, 24%, respectively, further through using the Environmental Category attribution analysis method, each large class was divided into 5 subcategories, the probability was greater than 75% the relatively stable region accounts for about 63% of the total area, the fluctuation area of less than 75% is about the stable spatial and temporal distribution of 37%; the extremely unsuitable area, the unsuitable area and the less suitable area, these three kinds of spatial and temporal distributions were relatively stable, the belonging degree was 100%, accounting for 87.67%, 70.41% and 84.28%, respectively, the fluctuation zone mainly occurs between the extremely suitable zone and the suitable zone, and between the suitable zone and the relatively suitable zone. The fine zoning of summer com in North China Plain has important guiding significance for improving the utilization efficiency of local resources and optimizing the layout of com industry.
In recent years, the technology of automatic tractor navigation based on satellite positioning has greatly improved the efficiency and accuracy of tractor field work. Automatic steering contributes is one of the key technologies to realize the automation and intelligentization of agricultural mechanization. It costs much time to install and test automatic steering systems for tractors in the field due to complicated conditions. An automatic steering test bench was developed to reduce time consumed in the field by conducting simulation tests on accuracy and reliability. The developed automatic steering system can be applied to the tractor after obtaining satisfactory results on the test bench, which will greatly shorten the development cycle and improve the precision of the system. In this study, a 120-horsepower tractor front axle was selected. Through the design and calculation of mechanical structure, hydraulic system and electrical control system, the tractor automatic steering test bench was built. The mechanical body consist of a tractor front axle assembly, a loading device and a mechanical frame. The hydraulic interface was reserved for post-installation of automatic steering devices. Two inertial measurement units were used to test the steering system performance by recording the rotation angle of front wheels and the steering wheel. The steering wheel had a steering clearance of 16.48° and an average wheel delay time of 0.14s. In the general range and small angle range, there is a small deviation between the actual corner and the theoretical corner. Responsibility and stability met requirements for agricultural machinery steering. Experimental results show that the test bench has stable performance in terms of status detection, steering control and measurement analysis, which could meet requirements for verifying working parameters of automatic steering devices. The research provides an efficient and reliable test bench for commissioning and performance testing of agricultural machinery automatic steering.
The fine breeding of livestock and poultry facilities is the frontier of the development of modern animal husbandry. The core of the fine breeding of livestock and poultry facilities lies in the deep integration of the "Internet of Things+" with traditional farming facilities. In recent years, with the withdrawal of more and more individual family-based breeding models, the management methods of livestock and poultry farms in China have gradually moved towards intensification, large scale,and automated facilitation. The traditional family-style livestock and poultry management experience is falling behind and gradually withdrawing from the historical stage. The refined farming of livestock and poultry facilities based on the individual animal management and quality assurance of farmed animals and animal welfare requirements have become the latest development trend of livestock and poultry farming industry. The rapid development of digital and network technology will provide new opportunities for the organic combination of animal husbandry production, animal welfare, information management and sustainable utilization of natural resources. Economic benefit, animal health and welfare, refinement of production process management and product quality are three key factors that affect the sustainable development of animal husbandry. In this paper, based on expounding the importance of the information sensing and the environmental regulation and control of the fine breeding livestock and poultry facilities, a cutting-edge technology of the information sensing and the environmental regulation and control of the livestock and poultry facilities was introduced; problems and challenges to be faced with were analyzed; and it was concluded that the smart sensor technology would become the base driving force for progress of livestock and fine poultry breeding facilities, taking account of the welfare of livestock and animal performance of animal anthropomorphizing intelligent control technology and strategy is facing significant challenges. In the field of pig farming, the core direction is mechanized production mode, which is light simplification, feed hygiene and animal health. In the field of cattle farming, the main direction is the automation of the whole chain of forage and the safety of its enclosure facilities. In the field of milking technology, the frontier of technological innovation is to further improve milking efficiency and quality, milking process, low disturbance milk metering, and cow individual milk production prediction. In the field of poultry production, similar to cattle farming, more attention is paid to the improvement of engineering processes such as bedding, environment and drinking water. Finally the paper put forward suggestions on how to implement the key technologies of fine farming of livestock and poultry facilities in China, with purpose of providing theoretical reference and technical support for the transformation, upgrading sustainable development of livestock and poultry breeding industry.
Along with the increasing awareness of environmental protection and growing demand for green and pollution-free agricultural products, it has a great need to explore new ways to apply greener pest control methods in agricultural production. Researching on Solar Insecticidal Lamps (SILs) has continuously received incremental attentions from both the academia and industry, which brings a new mode for the preventing and controlling of agricultural migratory pests with phototaxis feature, and now is becoming to a hot research topic. Towards the fast development of "precision agriculture" and "smart agriculture" as well as the increasing demands for agricultural informatization, Wireless Sensor Networks (WSNs) have been widely used for agricultural information collection and intelligent control of agricultural equipment. WSNs are suitable for large-scale deployment and regional monitoring, which can be easily combined with SIL nodes. Based on the combination, a new type of agricultural Internet of things - Solar Insecticidal Lamps Internet of Things (SIL-IoTs) was proposed and the technology of WSNs for the prevention and control of phototactic migratory pests in agricultural applications were surveyed. Firstly, the state-of-art insecticidal lamps applications was reviewed and their characteristics deployment manners and working lifetime in the production of crops (e.g., forest, fruits, rice, vegetables) were summarized. Secondly, the characteristics of existing GSM/3G/4G-enabled SIL nodes and their latest research status on SIL-IoTs were summarized. Furthermore, the research status was analyzed concerning the energy harvesting mode and deployment characteristics of SIL, which are solar energy SIL harvesting mode for energy saving and the heuristic mode for node deployment, respectively. Finally, towards the fast-developed vision of smart agriculture, in which various emerging IT and automation technologies are maturely applied, SIL-IoTs can be considered as a new and important component to contribute to the green agricultural pest monitoring and control. To further enhance SIL-IoTs' capability and enrich SIL-IoTs' function, four open research issues on SIL-IoTs were proposed, i.e., 1) optimized deployment scheme of SIL-IoTs with multiple constrains, 2) optimized and adaptive energy management strategy for ensuring normal working hours of SIL node, 3) lack of algorithms for pests outbreak area localization, and 4) interference on data transmission because of dense high voltage discharge during severe pest disaster. To sum up, SIL-IoTs is one of the representative applications of "precision agriculture" and "smart agriculture" based on WSNs, which is a new model on prevention and control of pests. The combination of both optimized deployment algorithms of SIL-IoTs nodes and artificial intelligence techniques will provide a theoretical basis for SIL-based applications in terms of optimized deployment and energy management. Intelligent pest information collection, alarm, and node' senergy management via SIL-IoTs will facilitate decisions-makings for precise agricultural applications in prevention and control of pests.
China has high total apple output, but the export volume is low. The high-end apple market is mostly occupied by imported apples. The main reason of this situation is the lack of technologies and equipments for fruit quality classification, and the degree of automation after picking stands low. The apples enter the consumer market without simple roughing processing, and the quality of the apple is unstable, which greatly reduces its market competitiveness. In this paper, the status quo of non-destructive detection and grading technology of apple quality was analyzed, then the development was forecasted. Apple non-destructive detection technology mainly includes spectrum, electrical characteristics, CT, chromatography, electronic nose and computer vision technology. According to the functional characteristics, advantages and disadvantages of various technologies, it is proposed to develop apple odor detection method based on new sensor technology; adopting multi-feature grading method based on machine vision, the combination of apple quality non-destructive testing technology and grading technology can promote the improvement of apple's industrial competitiveness. Overall, the needs of apple quality non-destructive detection and grading technology development in China are urgent. Detections with new technologies such as nanotechnology, biotechnology and artificial intelligence methods of sensor technology and products in apple non-destructive, quality grading detection and multi-technology have great potential. A real-time, efficient, high-precision grading systems in apple quality which integrates electricity, light, gas and computer vision may be an important development direction for improving apple's quality and enhancing the competitiveness of the apple industry.
The internet is a huge resource base and a rich knowledge base. Aiming at the problem of small agricultural samples, the utilization technology of network resources was studied in the research, which would provide an idea and method for the research and application of crop disease identification and diagnosis. The knowledge transfer and deep learning methods to carry out research and experiments on public data sets (ImageNet, PlantVillage) and laboratory small sample disease data (AES-IMAGE) were introduced: first the batch normalization algorithm was applied to the AlexNet and VGG of Convolutional Neural Network (CNN) models to improve the over-fitting problem of the network; second the transfer learning strategy using parameter fine-tuning: The PlantVillage large-scale plant disease dataset was used to obtain the pre-trained model. On the improved network (AlexNet, VGG model), the pre-trained model was adjusted by our small sample dataset AES-IMAGE to obtain the disease identification model of cucumber and rice; third the transfer learning strategy was used for the bottleneck feature extraction: using the ImageNet big dataset to obtain the network parameters, CNN model (Inception-v3 and Mobilenet) was used as feature extractor to extract disease features. This method requires only a quick identification of the disease on the CPU and does not require a lot of training time, which can quickly complete the process of disease identification on the CPU. The experimental results show that: first in the transfer learning strategy of parameter fine-tuning: the highest accuracy rate was 98.33%, by using the VGG network parameter fine-tuning strategy; second in the transfer learning strategy of bottleneck feature extraction, using the Mobilenet model for bottleneck layer feature extraction and identification could obtain 96.8% validation accuracy. The results indicate that the combination of CNN and transfer learning is effective for the identification of small sample crop diseases.
The basis and premise of developing intelligent agriculture is digitization, especially digitization of agricultural land resources utilization, agricultural land ownership, agricultural production and other agricultural elements. At present, China's agriculture digitization is at a low level, the spatial information of agricultural land resources are applied few. It is necessary to accelerate the application of the big data with respect to agricultural land for agricultural production information collecting and agricultural policy implementing to promote the development of China's intelligent agriculture. One typical case is "food production function zone" and "important agricultural product production protection zone"(Two-Zone) demarcation. In order to realize the study on the technologies of "Two-Zone" demarcation, in this research, the following work was conducted . Firstly, the basic concept of the multi-source spatial data with respect to agricultural land was elaborated, and the existing multi-source spatial data with respect to agricultural land were summarized into four categories. Then, the workflows of "Two-Zone" demarcation was summed up. Considering the topological requirements of digital mapping for "Two-Zone" and the business requirements of intelligent management for agricultural production in "Two-Zone", a three-level spatial structure of "zone-patch-plot" was designed for "Two-Zone" demarcation. The key technology of digital mapping was proposed, based on the analysis of the functions of "Two-Zone" and the "zone-patch-plot" spatial structure, which integrate existing multi-source farmland spatial data depending on the relevance of spatial distribution and semantic attributes and then realize "Two-Zone"'s spatial distribution map at a specific spatial scale. The key technology of establishing the database for "Two-Zone" demarcation was also proposed, which realizes the abstraction of the geographical space entity delimited by "Two-Zone" from the perspective of spatial information structure. Therefore, the key technologies of "Two-Zone" demarcation based on multi-source agricultural land spatial data was scientifically designed, through the key work such as data acquisition, digital mapping and database construction. Finally, the key scientific problems in the technical links were extracted, which shown that "Two-Zone" demarcation requires a comprehensive consideration of data sources and user requirements, and it is necessary to analyze the availability of data with respect to multi-source agricultural land, decreasing the influence derived from the bias and partial loss of data with respect to multi-source agricultural land. Further consideration about statuses of the land use and crop planting in the farmland of "Two-Zone" is also needed. This study will provide a basic support for the intelligent management of agricultural land resources in the "Two-Zone".
Dissolved oxygen (DO) is vital to aquaculture industry and affects the yield of aquaculture. Low DO in water can lead to death of crabs, therefore, it is important to measure DO accurately. However, the DO sensors are usually expensive and often lost function due to corrosion in water environmental and adsorption of different materials on their surface, which result in the inaccuracy in measured DO values. It is thus important to develop effective methods to estimate DO concentrations by using other environmental variables, which may reduce farmers' cost because DO sensors are not used. In this research, the collected environmental data, including temperature, pH, ammonia nitrogen, turbidity, were used to estimate DO concentrations in crab ponds. The data were preprocessed to eliminate missing values and outlier. Correlation analysis was applied to determine the relationship between environmental variables (temperature, pH, ammonia nitrogen, turbidity) and DO to show the rationale of using these four variables to forecast DO concentration. Principal component analysis was used to reduce the dimension of environmental data to reduce computation cost. For DO concentration estimation, it is more important to make the estimation of DO concentration at low values more accurate because DO concentration at low values is dangerous to crabs. This implies that estimation of DO concentrations at low or high values should be treated differently and applied different rates. Based on the Long Short-Term Memory (LSTM), a low DO concentration estimation model of Low Dissolved Oxygen Long Short-Term Memory(LDO-LSTM), which can improve the estimation accuracy of low DO values was proposed by optimizing the loss function of LSTM back propagation. The loss function of LDO-LSTM was based on the Mean Absolute Percentage Error (MAPE). According to the trend of DO, the true DO and the estimated DO values were applied weight functions. The Root Mean Square Error (RMSE) and the MAPE were used to evaluate the performance of LDO-LSTM and LSTM in DO estimation. Experimental results show that the value of RMSE and MAPE were stable at about 0.1 for LSTM and LDO-LSTM in forecasting DO when dissolved oxygen was higher than 6mg/L and the value of RMSE and MAPE of LDO-LSTM were lower than LSTM by 0.25 and 0.139. The results prove that the proposed method can not only provide desirable estimation accuracy for DO concentrations at high values but also make the estimated DO concentrations at low values more accurate. This research is expected very useful in reducing aquaculture costs and improving accuracy in forecasting DO especially at low values.
In order to detect citrus Huanglongbing (HLB, also named citrus greening) quickly, Auto-fluorescence and Raman spectra of HLB leaf samples and healthy ones were collected and analyzed. PLS-DA models based on Auto-fluorescence spectra, Raman spectra and mixed spectra were established and compared respectively. Finally, ROC curves of the three models were drawn, and the performance of the models were further evaluated by using the area under curve AUC parameters. The results demonstrated spectral differences between Huanglongbing samples and healthy ones could be seen. With 785 nm laser irradiation, citrus leaf samples produced strong Auto-fluorescence and Raman peaks. The Auto-fluorescence of HLB leaves was weaker than that of healthy samples in the range of 800-1203 cm -1, but stronger in the range of 1206-1800 cm -1, and the slope of decline (absolute value) was smaller than that of healthy samples. The similar shapes were found in the Raman spectra of typical HLB samples and healthy ones. But the HLB samples had larger Raman peak intensity and spectral bandwidth at 1257 cm -1, 1396 cm -1, 1446 cm -1, 1601 cm -1 and 1622 cm -1 than healthy ones. The Raman peak intensity of HLB samples was weaker than that of healthy samples at 1006 cm -1, 1160 cm -1, 1191 cm -1 and 1529 cm -1 positions, suggesting that the carotenoid content of HLB samples was lower than healthy ones. The Auto-fluorescence model, the Raman spectral model and the mixed spectral model could distinguish two kinds of samples with the accuracy of 86.08%, 98.17% and 94.75%, respectively. Furthermore, AUCs of Receiver Operating Characteristic Curve (ROC) were calculated. The AUCs for the Auto-fluorescence model, the Raman spectral model and the mixed spectral model were0.9313、0.9991 and 0.9875, respectively. Through further analysis of ROC curve, the identification effect of the Raman spectral model was optimal. Raman spectroscopy could be a new way to explore the rapid diagnosis of citrus HLB.
The greenhouse vegetable industry play an important strategic role in the adjustment of agricultural transformation mode and the reform of supply side in Heilongjiang Province. Facility horticulture in Heilongjiang Province develops rapidly in recent years, technical support is in great demand, but the experts' technology support for facility horticulture is far from enough. Experts' on-site guidance costs much time and money in the countryside, while the service efficiency is very low. To solve this urgent problem, the architecture of "greenhouse vegetable intelligent terminal system based on cloud service" and the key technologies of implementation (low-cost IoT, distributed real-time operating architecture, virtual expert service, neural network image recognition and mobile terminal service) were put forward. Based on expert services, supplemented by data mining technology, IoT devices were used as expert's remote perception means, smart phones as user terminals, cloud service for integrating knowledge, resources and Internet of Things data to provide vegetable experts and greenhouse vegetable users with high information acquisition, storage, analysis,decision-making capabilities and effective solutions. Experts could view vegetable production status in greenhouses remotely through the Internet, get image and growth environment data, then provide remote guidance to vegetable farmers through the system, expert knowledge would be stored, mined and reused by the system. The Internet of Things system could automatically send out early warning information by judging the air temperature, humidity, illumination intensity and soil moisture in greenhouse. The application of knowledge map and neural network technology would reduce the workload of experts and increase concurrent processing capability of services at the same time. At present, part of this research has been applied in different user groups such as agricultural research departments, enterprises, vegetable cooperatives and farmers in Heilongjiang Province. The system can provide experts with remote inquiry means of greenhouse vegetable production environment, and has the characteristics of simple deployment and low cost. It is suitable for various greenhouse vegetable scenarios, including fruit and edible fungi. In order to popularize this technology in greenhouse vegetable production in China, and achieve an efficient experts' technical support, this research also proposed technical solutions of a large-scale application scenario through cloud computing in future.
Wheat stripe rust is an important biological disaster that affects the safe production of wheat in China for a long time. The number of spores of wheat stripe rust is a direct factor affecting its pathogenesis and transmission. At present, it mainly relies on the field sampling and investigation of agricultural technicians to predict and forecast. It is time-consuming and laborious, and difficult to achieve long-term monitoring of diseases, thus affecting the accuracy of forecasting and the timeliness of prevention and control. The existing automatic spore monitoring device also has the problems that the collecting device is mostly in the form of manual replacement of slides, and the direct acquisition of components in the air by a limited area of the slide may result in inaccurate sample collection and too small sample size. In order to further improve the monitoring and forecasting ability of wheat stripe rust, a wheat stripe rust monitoring device was designed and implemented, which based on the internet to build a wheat stripe rust monitoring platform, and based on the embedded system to establish a complete set of wheat stripe rust spore collection and image transmission processing device. Spore acquisition was performed using a slide adsorption device of "Six prism column + Electromagnet + Microscope". Control the up and down movement of the electromagnet to control the up and down movement of the slide; update the slide by controlling the rotation of the hexagonal shaft; obtain the image by controlling the time synchronization of the microscope and the shaft; control the cleaning solvent the smear and the movement of the cleaning block enable the slide to be cleaned. At the same time, a spore counting program based on the server platform was designed to process and analyze the collected slide images. The spore counting program used in this design is based on Python 3.6 and combined with the Skimage image processing package for spore image analysis and processing. The geometry factor feature based method was used, and the number of spores in the microscope field was finally obtained based on the regional attribute values. The experimental results show that the platform server image processing algorithm can achieve accurate counting of spores, the accuracy of counting the test images is 100%; the success rate of the slide switching system is 95%.This study can lay a foundation for the real-time monitoring of wheat stripe rust in the field, and can also provide references for the monitoring of other airborne diseases in the field.
High-quality operation of plant protection UAV is the premise of precision operation in agricultural aviation, so it is particularly important to study the characteristics of spray system. In order to explore the factors that affect the spray quality, the comprehensive experimental platform of spray performance (developed by Jilin Agricultural Machinery Research Institute) was used to test the droplet deposition distribution and droplet diameter under different UAV rotor speed, spray height and centrifugal nozzle speed in this research, and regression analysis on the deposition characteristics and particle diameter data of 12 groups of tests was conducted. The results showed that the three repeated tests of the same set of parameters had good consistency. Droplets had obvious drift and the maximum effective deposition rate was 46.31% and minimum 31.74%, which shows that the effective deposition rate of droplets was lower than 50%. Compared with the regression analysis results of droplet diameters DV10, DV50 and DV90, the spray height P value is greater than 0.5, and the nozzle speed and rotor speed P value are less than 0.5. So it can be inferred that spray height had a very significant effect on deposition, no significant effect on droplet size. The nozzle speed and rotor speed had very significant effect on droplet size, no significant effect on deposition. The test results of this research can provide theoretical basis and data support for improving the operation quality and spraying efficiency of UAVs.
Global change and natural disturbances have already caused a severe co-epidemic of crop pests and diseases, such as aphids, fusarium, rust, and powdery mildew. These threats may result in serious deterioration of grain yield and quality. Traditionally, crop pests and diseases are monitored by visual inspection of individual plants, which is time-consuming and inefficient. Besides, the distribution of different infected wheat patches are hard to identify through manual scouting. However, the spatial scale difference of remote sensing observation directly affects the remote sensing diagnosis mechanism and monitoring method of pests and diseases. The differences in pest and disease characterization and monitoring mechanisms promote the development of the remote sensing-based monitoring technology at different spatial scales, and the complementarity of multi-spatial data sources (remote sensing, meteorology, plant protection, etc.) increase the chance of the precision monitoring of the occurrence and development of pest and disease. As a non-destructive way of collecting ground information, remote sensing technologies have been proved to be feasible in crop pests and diseases monitoring and forecasting. Meanwhile, many crop diseases and pests monitoring and alarming systems have been developed to manage and control agricultural practices. Based on the description of physiological mechanism that crop diseases and pests stressed spectral response, some effective spectral wavelengths, remote sensing monitoring technologies, and crop pests and disease monitoring and forecasting system were summarized and sorted in this paper. In addition, challenge problems of key technology on monitoring crop diseases and pests with remote sensing was also pointed out, and some possible solutions and tendencies were also provided. This article detailed revealed the researches on the remote sensing based monitoring methods on detection and classification of crop pests and diseases with the challenges of regional-scale, multi-source, and multi-temporal data. In addition, we also reviewed the remote sensing monitoring of pests and diseases that meet the characteristics of different remote sensing spatial scale data and precise plant protection and control needs. Finally, we investigated the current development of the pest and disease monitoring systems which integrated the research and application of the existing crop pest and disease monitoring and early warning model. In summary, this review will prove a new perspective for sustainable agriculture from the current researches, thus, new technology for earth observation and habitat monitoring will not only directly benefit crop production through better pest and disease management but through the biophysical controls on pest and disease emergence. Application of UAVs, image processing to insect/disease detection and control should be directly transferable to other pests and diseases, with feedbacks into UAV and EO capabilities for the mapping and management of these agricultural risks. Similarly, these vision systems open other possibilities for farm robotics such as mechanical rather than manual pesticide usage for below crop canopy pest surveying.
Crop production management has advanced into the stage of smart agriculture, which is driven by state-of-the-art agricultural information technology, intelligent equipment and massive data resources. Smart agriculture inherits ideas from precision agriculture and brings agricultural production and management from mechanization and informalization to intelligentization with automatization. Precision agriculture has been developed from strategic monitoring operations in the 1980s to tactical monitoring and control operations in the 2010s. In its development, agricultural aviation has played a key role in serving systems for spray application of crop protection and production materials for precision agriculture with the guidance of global navigation through geospatial prescription mapping derived from remotely-sensed data. With the development of modernized agriculture, agricultural aviation is even more important for advancing precision agricultural practices with more efficient soil and plant health sensing and more prompt and effective system actuation and action. This paper overviews the status of agricultural aviation for precision agriculture to move toward smart agriculture, especially in the Mississippi Delta region, one of the most important agricultural areas in the U.S. The research and development by scientists associated with the Mississippi Delta region are reported. The issues, challenges and opportunities are identified and discussed for further research and development of agricultural aviation technology for next-generation precision agriculture and smart agriculture.
With the rapid development of information technology and the steady growth of the agricultural and rural economy, agricultural information technology has attracted more and more attention, and the trend of capital and technology playing important roles in the agricultural field has gradually formed. In recent years, large Internet enterprises have begun to enter the agricultural industry and smart agriculture has developed strongly. This paper analyzed the status and technical application characteristics of large-scale Internet companies engaged in agriculture; explained the reasons why the current technology and capital entered the agricultural field in large numbers, especially in the context of the world science and technology revolution and China's economic and social status, analyzed the key areas and problems of the combination of technology, capital and agricultural industry; analyzed the application boundary, application prospects of information technology in the agricultural field. In view of the digital development and application of new technology in agricultural and rural areas, this paper put forward some policy suggestions. Firstly, strengthen policy guidance and support to prevent market speculation risks; secondly, built a system and mechanism for the convergence and integration of Internet enterprises and agricultural industries; thirdly, focus on cutting-edge key technologies and strengthen efforts to promote scientific and technological innovation; finally speed up the dynamic follow-up of technology achievement transformation, strengthen supervision and do a good job in leading and demonstration drive. The key priority is to focus on the world’s cutting-edge technology and key application technology, strengthen the dominant position of technological innovation of enterprises, and combine with the specific practice of production, circulation and consumption of China’s agricultural industry to fully promote the innovation and application of China’s agricultural information technology. And the main research contents included summarize the successful examples carefully, doing a good job in publicity and guidance, and promoting the typical leads vigorously so that they can be copied, popularized and applied; for the failure cases, learn from the insufficient lessons to prevent the recurrence of similar cases; for the advanced practical technology formed by Internet enterprises, promote technology sharing and information sharing on the premise of protecting intellectual property rights and turn it into a new driving force for the development of agricultural modernization. Only by applying the latest achievement of modern information technology to the practice of agricultural production and becoming the representative of agricultural productivity, can we truly contribute to the development of modern agriculture and rural areas in China and the wing of information.
Intelligent recognition of greenhouse vegetable diseases plays an important role in the efficient production and management. The color, texture and shape of some diseases in greenhouse vegetables are often very similar, it is necessary to construct a deep neural network to judge vegetable diseases. Based on the massive image data of greenhouse vegetable diseases, the depth learning model can automatically extract image details, which has better disease recognition effect than the artificial design features. For the traditional deep learning model of vegetable disease image recognition, the model recognition accuracy can be improved by increasing the network level. However, as the network level increases to a certain depth, it will lead to the degradation / disappearance of the network gradient, which degrades the recognition performance of the learning model. Therefore, a method of vegetable disease identification based on deep residual network model was studied in this paper. Firstly, considering that the super parameter value in the deep network model has a great influence on the accuracy of network identification, Bayesian optimization algorithm was used to autonomously learn the hyper-parameters such as regularization parameters, network width, stochastic momentum et al, which are difficult to determine in the network, eliminate the complexity of manual parameter adjustment, and reduce the difficulty of network training and saves the time of network construction. On this basis, the gradient could flow directly from the latter layer to the former layer through the identical activation function by adding residual elements to the traditional deep neural network. The deep residual recognition model takes the whole image as the input, and obtains the optimal feature through multi-layer convolution screening in the network, which not only avoids the interference of human factors, but also solves the problem of the performance degradation of the disease recognition model caused by the deep network, and realizes the high-dimensional feature extraction and effective disease recognition of the vegetable image. Relevant simulation results show that compared with other traditional models for vegetable disease identification, the deep residual neural network shows better stability, accuracy and robustness. The deep residual network model based on hyperparametric self-learning achievesd good recognition performance on the open data set of tomato diseases, and the recognition accuracy of 4 common diseases of tomato leaves reached more than 95%. The researth can provide a basic methed for fast and accurate recognition of tomato leaf diseases.
Terraces achieve water storage and sediment function by slowing down the slope and soil erosion. This kind of terraced or wave-section farmland built along the contour line on is a high-yield and stable farmland facility with key construction in the dry farming area. It provides a strong guarantee for increasing grain production and farmers' income. In recent years, Gansu province has carried out a large amount of construction on terraces, however, due to the poor quality of the previous construction and management, the terraced facilities are in danger of being destroyed. In order to prevent and repair the terraces, it is necessary to timely and accurately extract the terrace information. The segmentation of terraces can be obtained by edge extraction, but the effect of satellite data is not ideal. With the continuous development of remote sensing technology of drones, the acquisition of high-precision terrace topographic information has become possible. In this research, the slope is extracted from the digital elevation model data in the data preprocessing stage, then the orthophoto data of the three experimental areas are merged with the corresponding slope data, respectively. Then the rough edge extraction method based on Canny operator and the fine edge extraction method based on multi-scale segmentation are used to perform edge detection on two data sources. Finally, the influence of slope on the extraction of terraced edges of remote sensing images of UAVs was analyzed based on the overall accuracy of edge detection and user accuracy. The experimental results showed that, in the rough edge extraction method, the data source accuracy of the fusion slope and image was improved by 23.97% in the OA precision evaluation, and the average improvement in the UV accuracy was 20.68%. In the fine edge extraction method, the accuracy based on the data source 2 was also increased by 17.84% on average in the OA accuracy evaluation of the data source 1, and by an average of 19.0% in the UV accuracy evaluation. The research shows that in the extraction of terraced edges of UAV remote sensing images, adding certain terrain features can achieve better edge extraction results.
In order to study the development mode and realization way of smart agriculture, the technical route of agricultural information fusion of agricultural machinery in different production stages before, during and after wheat production was designed. Pre-production: use Beidou precision navigation technology and motion planning optimization method to realize the full area coverage path planning of the field operation of the automatic navigation tractor, combine the laser leveling equipment to realize the accurate and standardized land leveling and laser leveling, and realize the accurate and standardized operation of the land. On this basis, the spatial interpolation technology was used to make the variable fertilization prescription map and combining variable rate fertilizer machine and realized variable rate precise application of fertilizer and precise seeding. At the same time, combining with the optimal design of planting scheme, based on the prenatal database and knowledge base, it optimizes the decision-making of variety configuration and sowing time and seeding amount were optimized, and the software intelligent decision-making technology was used to recommend the varieties and sowing time and seeding amount suitable for planting at the decision-making point, and constructs the wheat and maize prenatal information service recommendation system based on WebGIS was constructed. In production: based on the image technology of automatic segmentation and color feature extraction of wheat image in the field environment, a remote monitoring model of wheat nutritional status with the function of wheat population image segmentation and nutritional estimation was established to realize the non-destructive monitoring of wheat nutritional status in the field environment. After production, the integrated measurement sensor, speed sensor, header height sensor and GPS were adopted, and controller area network bus was adopted with wireless communication technology, a real-time wheat yield measurement system was developed, which was installed on a large-scale combine harvester to carry out the real-time prediction service of wheat yield, so as to realize the synchronous process of wheat harvest and yield measurement, with the error less than 5%. The intelligent transformation of common agricultural machinery equipment and the research and development of sowing and harvesting equipment adapted to agricultural production were completed and realized, and the small scale with high-efficiency utilization of light and heat resources, increase of output and green development were studied. The model of wheat planting production was optimized .A real time measurement and prediction system for postpartum yield was developed, which included the selection of sowing date, fertilization recommendation, seedling growth and nutrition diagnosis. The experimental results show that the adoption of agricultural information fusion technology can increase wheat yield by 18.4%, input-output ratio by 16.6% and 7.9%, which shows that the intelligent agriculture of Henan province is effective and feasible.
