In the practice of fine farming, it is necessary to obtain spatial distribution information related to farmland crop production on a finer spatial scale, including collecting data using different sensing technologies, and processing the data by appropriate methods to become easy to understand and The visual space used to distribute graphical information is mainly supported by electronic information hardware and software technology. The main information that needs to be acquired and processed includes the following aspects:

1. Spatial distribution of crop yields in farmland:

Obtaining the yield information of crop plots and establishing the spatial distribution map of plot yields is the starting point for the implementation of “fine cropping”. It is the result of crop growth under the combined influence of many environmental factors and farmland production management measures, and it is the scientific regulation and control in the process of crop production. Invest and develop the basis for management decision-making measures. to this end. It is necessary to install a DGPS satellite positioning receiver and a flow metering sensor on the harvesting machine. The universal DGPS receiver can give the dynamic data of the latitude and longitude coordinates of the geographic location of the DGPS antenna when the harvester is working in the field every second. The flow sensor automatically measures the accumulated time within the set time interval (ie, the machine corresponds to the working stroke interval). The output is then converted into the unit area output of the working area in the corresponding time interval according to the working width (estimation or measurement), thereby obtaining spatial geographic location data (latitude and longitude coordinates) and cell production data of the corresponding cell. These raw data are digitized and stored in a smart card, and then transferred to a computer for further processing using dedicated software. In fact, the processing of production spatial distribution data is a complex process, but can be done quickly by dedicated software. For example, the dynamic position of the antenna position indicated by the GPS receiver and the instantaneous position of the crop harvested by the header have spatial differences depending on the machine structure, and the flow sensor is usually installed after the threshing, sorting, and grain cleaning process. On the output part, the output measurement data of the corresponding position in the crop field should be reflected. It is necessary to take into account various factors such as the logistics process design and the operation speed in the structural size of the harvester, and estimate by establishing a mathematical model. Since the moisture content of the grain at the time of harvesting is different, it is also necessary to simultaneously measure the water content of the grain at the time of harvesting so as to be converted into a standard water content at the time of data processing in order to evaluate the yield level. To date, flow sensors for major crops such as wheat, corn, rice, and soybeans have been generalized, and other production sensors such as cotton, sugar beets, potatoes, sugar cane, pasture, and fruits have been studied in recent years. Some have been used in trials. There are three main types of cereal crop production sensors currently in use: the impact flow sensor (Fig. 1a), the gamma ray flow sensor (Fig. 1b) and the photoelectric volume flow sensor (Fig. 1c). They are used in John Deere and Case, AGCO Massey Ferguson, and fine farming Cobyin products from some European companies. The impact type flow sensor has a measurement error of less than 3%. Based on the gamma ray passing through the grain layer, the ray intensity is attenuated to measure the grain flow. The measurement error is reported to be no more than 1%. The grain moisture measurement applied on the harvester is designed according to the principle of the plate capacitive sensor. The storage devices that collect data on the harvester have turned to the application of universal smart IC card technology. The memory cards in the Massey Ferguson and Case IH systems can continuously store 30 hours of harvesting data. The smart card in the John Deere system can store 250 hours of harvesting data. Each company has specially developed data processing and community output distribution map generation software combined with its own products and supporting intelligent virtual electronic display instruments, which can display relevant information to the operator directly in the cab.

Since the mid-1980s, research and development on the measurement system of harvesting machinery has attracted great interest from manufacturers and has been the first to be promoted on large grain combine harvesters. According to reports, by the end of 1997, more than 20,000 grain combine harvesters in the United States were equipped with production meters, and about half of them were equipped with GPS positioning systems and provided the necessary data output for generating production distribution maps. More than % of the grain combine harvesters will be equipped with a production meter. According to the trend of international technology development, combined with China's national conditions to carry out research on electronic information equipment technology innovation, there have been good opportunities.

2. Farmland soil information collection and processing

The collection of soil information in “fine farming” is to obtain information from the perspective of soil environmental conditions and nutrient levels that affect crop growth, to analyze the reasons for the differences in the spatial distribution of yields in the yield map, and to formulate relevant fertilization, soil improvement and cultivation. Distributed positioning prescription decisions such as planting. This type of information can be divided into two categories due to its temporal and spatial variability:

2.1 Relatively stable, spatial and temporal variability of soil information. Such as terrain slope, soil type, structure, P, K and organic matter (SOM) content, pH value, depth of tillage layer, etc. The collection of these data can be listed as the necessary basic information collection content before the implementation of the “fine farming” project. These parameters can be selectively retested after many years. Some data, such as soil type and soil trace element content, can be referred to the original soil census data for reference; parameters closely related to fine farming prescription decision-making such as P, K, SOM content, tillage layer depth, etc., should be as much as possible in technology, Under economically reasonable conditions, standard grid-type fixed-point sampling methods with smaller spatial scales are used to obtain soil samples, which are analyzed in the laboratory or field-measured with appropriate instruments. The collected data can be selected on the geographic information system (GIS) platform, and the appropriate geostatistical processing method is selected to establish the spatial distribution map of the main parameters, which is stored in the database for analyzing the reasons for the spatial difference of production yield, and is called when the management prescription decision is made.

2.2 Farmland soil information with large spatial and temporal variability. Such as N content and soil moisture content. In addition to the basic data measurement and spatial distribution map generation before fertilization, sowing or irrigation, they also need to make necessary sampling measurements according to the growth period to adjust the input in a timely manner. This requires support for real-time fast acquisition and data processing instruments. So far, the detection of nutrients in farmland soils has mostly followed the laboratory analysis and analysis methods, which is costly and time-consuming. Therefore, the spatial scale of grid sampling of farmland soil is too large, and the density of sampling points is sparse. It is difficult to establish finer. Spatial distribution map of soil parameters. The distribution of soil parameters established by the national soil census basically supports the need to study soil distribution from a geographical perspective, the need for macro-management to serve the socio-economic and agricultural development, and the need to implement fine farming management from an agronomic perspective. Since the 1990s, many researches have been carried out on the new techniques, new instruments and new methods of data processing for soil sampling and measurement, and some new technical ideas and commercial product development for collecting soil spatial information have been proposed. Results, a number of development trends can be listed as follows:

Based on the soil physical and chemical analysis principle of soil solution photoelectric colorimetric method, the development of intelligent soil main nutrient element rapid measuring instrument, there are several practical products in China to promote the application;

Based on new physical principles such as near-infrared (NIR) multispectral analysis technology, polarization-polarized laser technology, semiconductor multi-ion selective field effect transistor (ISFET) ion-sensing technology, etc. Preliminary progress or research results have been obtained, and some have been installed on mobile work machines to support rapid information collection experiments;

Commercialized products have been produced in the mid-1990s using real-time sensor acquisition of soil organic matter content developed by NIR multispectral analysis technology;

Exploring new technical ideas, developing real-time and rapid soil parameter comprehensive evaluation measurement techniques supporting fine-prescription farming, such as an airborne conductivity measurement and an automatic generation system for farmland conductivity spatial distribution map (The Veris 3100 model) It is produced by a company in KANSAS, USA, and is sold to the international market. It can be used to indirectly evaluate the spatial distribution of soil moisture content, SOM content, soil tillage depth, soil structure, soil cation exchange capacity (CEC), etc. The resolution is reduced to about 5m;

The rapid acquisition of sensor technology for soil water content has developed rapidly since the 1990s and can be expected to be satisfactorily resolved in terms of technology and economy. The well-known time-domain reflectometry (TDR) soil moisture rapid measuring instrument based on microwave measurement technology has entered a large number of agricultural research laboratories, and the portable TDR soil moisture measuring instrument with similar performance and price is also available as a commercial product; The principle development is based on the principle of standing wave ratio and frequency domain analysis, and the products with significantly improved performance-price ratio will realize their market value in the near future.

Supporting the development and research of the rapid measurement technology of the main soil parameters of “fine farming” is a hot spot in today's technological innovation. At present, the spatial sampling density of soil parameters is still subject to the real-time and economical nature of measurement technology, and it is necessary to promote the exploration of new technical ideas. Based on the prior art, the spatial scale of the soil grid fixed-point sampling measurement is generally 2-5 mu and 1 sampling point. The spatial distribution map of the soil parameters established by the data processing by the data processing is not possible to implement the prescription finely and objectively. Farming. Therefore, in recent years, the scientific and technological community has proposed a research direction of airborne fast real-time soil parameter approximation sensing technology based on the new physical principle. It does not pursue the individual measurement accuracy that can be achieved with laboratory physical and chemical analysis methods, but it can collect a large amount of raw data quickly, in real time and economically. Through modern mathematical methods and digital signal processing chip technology, the raw data is quickly analyzed and processed. To achieve satisfactory results, this may support the main research and development direction of “fine farming” farmland spatial information collection and processing.

2.3 Farmland crop seedling information collection and processing technology

This type of information includes the collection of crop growth, seedlings, and spatial distribution of pests and diseases. In the practice of traditional farmland fine farming, experienced farmers routinely conduct field observations on farmland crop seedlings and implement positioning management. Modern "fine farming" requires quantitative positioning measurement methods, patrolling observations under DGPS guidance, collecting quantitative information, processing on the GIS platform, superimposing or marking on the production map to support management decisions analysis. For example, the production information detection system of the existing combine harvester has an operation key in the cab. When performing the harvesting operation, the driver can observe the obvious difference between the weed and the mature crop, and the operation key can mark different miscellaneous The spatial location of the grass space is distributed and the weed distribution information is automatically superimposed on the resulting yield map. The narrow-band multi-spectral farmland weed identification detection technology composed of LED plus narrow-band filter has been experimentally studied in several countries. A large amount of accumulated basic data will support breakthroughs in its product development technology. Quantitative detection and evaluation techniques for pests and diseases and crop growth conditions have yet to be developed. At present, it is mainly based on DGPS-guided inspection. The biological object pattern recognition technology supported by computer vision and the high-resolution multi-spectral near-surface measurement technology applied in remote sensing system will have broad application prospects in this field.

The advancement of "fine farming" spatial information collection and processing technology will be an important premise for the practice of this crop management technology. At present, there are still many problems that still need to be broken by science and technology. It requires close cooperation between electronic information hardware, software scientists and agronomists. It is expected to achieve important progress in the early stage of entering the new century. At the same time, it will also support agricultural equipment that supports prescription farming. Technology and so on have far-reaching effects.

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