Proceedings of the 15th International Symposium on Advances in Geographic Information Systems ACM GIS 2007
Development of Web-based Decision Support System for Field-based Crop Management Zongyao Sha
Minghua Zhang
Wuhan University Wuhan Hubei Province, 430079. P. R. China +86 -27-68778132
University of California 1 Shields Avenue Davis CA 95616 +1- 530-752-4953
[email protected]
[email protected]
ABSTRACT A web-based decision support system, GZ-AgriGIS, was developed to assist local farmers in the region of Guangzhou, China make field-based crop management decisions, e.g. fertilizer applications (replacement) and irrigation. The system was aimed to share available site-specific agricultural domain knowledge and analytical models with the local farmers. The representation of domain knowledge and organization of analytical models were outlined. Considering the micro-environmental variations of the region, we paid special attention to the spatial suitability of domain knowledge and analytical models. Through the online service of GZ-AgriGIS, farmers can easily acquire scientific guidance for crop management decisions.
Categories and Subject Descriptors H.4.2 [Types of Systems]: Decision support; J.3 [Life And Medical Sciences]: Biology and genetics
General Terms Management, Design, Experimentation
Keywords Web-GIS; Crop management; Domain knowledge; Models; Decision Support System; Data mining
1. INTRODUCTION Computer-based information systems have been applied in assisting agricultural production for decades. Decision Support System (DSS), for example, uses analytical models to recommend crop management decisions for farmers (Runquist, et al., 2001). Although DSSs are widely studied and practically applied, their limitations are apparent. Such limitations, if not all, include: 1) Lack of extensibility. For example, the DSS developed for soy beans is not suitable for rice, and the DSS developed for California farms is not applicable to those in Florida. 2) Poor integration of domain knowledge and models. For some ill-
structured problems, agricultural domain knowledge is essential. In many cases, they were used independently (El-Najdawi and Anthony, 1993). 3) Limited design. DSSs are designed mainly for pre-delineated farm fields that are regularly shaped (e.g. hexagons or rectangles). This paper introduces a web- and GIS(Geographical Information System) based DSS application, GZAgriGIS, which overcomes the above limitations. GZ-AgriGIS was developed to assist crop management decision-making for farmers in the Guangzhou region of southern China.
2. TECHNICAL DESIGN Agricultural domain knowledge is applied in GZ-AgriGIS to help make crop management decisions. Domain knowledge is logically organized in different but related tables. Among those, the following three are especially important: Table of Crop Properties (TCP, Table 1), Table for Diagnosis of Organ Symptoms (TDOS, Table 2), and Table of Environmental Requirements (including soil and climate requirements) (TER, Table 3). TCP provides information about crop types, crop growing stages, and crop organs (e.g. root, stem, and leaf) which are useful for physical and nutrient diagnoses. TDOS presents normal values of chemical elements for different crop organs and physical symptoms under different conditions, such as color and morphology of organs with nutrient excess, deficiency and medium, and color of insect damage. TER stores information of environmental factors which affect crop growth (e.g. soil and climate). As shown in Table 3, each crop has a number of preferable environmental requirements for its growth. There are also other tables used to represent domain knowledge. For example, a fertilizer table is referenced to make decision on the selection of fertilizer type. All tables are stored in a relational database and logically related to build a knowledge network. Object-oriented method, as proposed by Batanov and Zhuang (1995), is taken to transform the tables into knowledge objects when system runs. In GZ-AgriGIS, database expression (DE) and logical expression (LE) are used to build models. The DE models represent mathematical functions in the form of, F(x1,x2,x3,…,xn)=a0+a1x1n1+a2x2n2+a3x3n3+…+anxnnn
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where x1, x2, x3, … ,xn are continuous factors or variables, a0 is a constant, a1, a2 ,a3 ,…, an are coefficients for each variable, and n1,n2,…,nn are exponents for the corresponding variables. All the information is stored in relational database. This polynomial mathematical model can be easily formatted and fits well for many scenarios for calculating task. To construct such models, a syntax/semantic interpretation module is built to
models (including LE and DE models). For some cases, however, the construction of LE models can answer the question of the task. If concepts are involved, they usually need to be translated by LE models into numerical values.
interpret data (including variables, coefficients, and exponents) retrieved from the database. The LE models take the form of productive rules with “if … then …” declaration. They are responsible for transferring concepts formularized in domain knowledge to digital values, and thus prepare variable values for DE models to make numerical calculations. For example, if a DE model uses soil texture as an input, the concept of soil texture clay, middle clay and sandy has to be transferred into numerical values for calculation, e.g. 0.6, 0.4, and 0.3 respectively.
Table 3 Table of environmental requirements Crop Stage (ID)
Table 1 Table of crop characteristics Crop
Crop Name
(ID)
Crop Stage
Stage-Organ (ID)
(ID)
00101 001
Rice
00101
Climate Requirement
Soil Requirement
Average Temperature
20℃~24.3℃
Type
Clay adobe
Total Rainfall
231mm~278mm
Texture
Clay
15~20MJ/m2·d
Moisture
15~20 %
Root
0010101
Radiation
Stem
0010102
……
Leaf
0010103
……
00102
…… 00102
……
…… 00201 002
00202 ……
……
Table 2 Table for diagnosis of organ symptoms Stage Organ (ID)
Chemical Requirement
Physical symptoms (symptom ID*) 2)
3)
4)
5)
Full 2.1%~4. FN Nitrogen 3% 001
FN 002
FN 003
FN 004
FN 005
Effective 0.4%~0. EN 0010101 Nitrogen 9% 001
EN 002
EN 003
EN 004
EN 005
Mineral 6.3%~9. MC Content 7% 001 ……
MC 002
MC 003
MC 004
MC 005
0010102
1)
Figure 1. Knowledge and model application Spatial Decision Units (SDUs) are the basic units to which crop managements are applied. Natural farm fields are considered SDUs in GZ-AgriGIS. Most current studies pay no attention to spatial suitability for models and domain knowledge. Obviously, it cannot be assumed that models and knowledge are suitable for all SDUs. In the present design, Spatial Units for Knowledge and Models (SUKM) is used to indicate spatial suitability for domain knowledge and models. SUKM, a vector GIS layer composed of polygons, is spatially referenced by SDUs to determine the knowledge and models that are suitable for the SDUs. All knowledge and models are affiliated to certain SUKM polygon(s). Knowledge or models attached to an SUKM polygon can only be applied to those SDUs that are spatially located within the SUKM polygon (Figure 1(b)). SUKM is predefined by local domain experts according to their practical experiences and available literatures. Many factors that influence the micro-environment of SDUs are taken to create the SUKM layer. First, distributions of soil type and annual climate data, such as rainfall, radiation, and
……
…… * each symptom ID is described in detail by other separate tables. 1) Heavy Excess, 2)Excess, 3)Normal, 4)Deficiency, 5) Heavy Deficiency The integration of domain knowledge and analytical models is shown in Figure 1(a). Users enter a crop management decision task (e.g. fertilizer application decisions, irrigation decisions, etc.) through a user interface (UI). Afterwards, the control unit (CU) accepts the task. The task code is then processed to retrieve domain knowledge (DK) which will tell CU the feature of the task. Accordingly, CU initializes the construction of the corresponding knowledge or/and models. For most cases, the construction is the process of instantiation of both domain knowledge and analytical
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average temperature, are considered to build the SUKM layer. Second, diverse topography, such as high mountains and major rivers are also taken into account for building the SUKM layer since they impacts climate and soil properties. Last, digital elevation model is processed to generate a contoured Elevation Spatial Layer (ESL), which is composed of small continuous polygons with a similar surface elevation value. The layers for soil type, annual climate data, major mountains and rivers, and ESL are overlaid to build the SUKM layer.
3. APPLICATION The developed system can be applied to recommend crop management decisions for farmers in fertilizer application (replacement) and irrigation, and it has been used by local farms as well as some agricultural production sites. As decisions are based on domain knowledge and models collected from experts or available literatures, they are usually more reliable than those made from an individual farmer’s experiences. As previously noted, field-based crop management decisions consider farm fields as the basic unit, usually SDU. Initially, a polygon SDU layer is delineated based on existing farm fields either regularly or irregularly shaped. SDU can then be updated when there are any changes to field boundaries. Several steps are involved to make a decision, as shown in Table 4. Some data, such as field boundary and soil type, can be retrieved directly from the database, while temporal data, such as the expected yield, soil water volume, or nitrogen concentration, are entered by users. Spatial analysis uses the field boundary polygon(s) or SDU to overlap SUKM layer. The overlapped SUKM polygon(s) look for spatially suitable analytical models and domain knowledge for the SDU. Table 5 lists the required data.
A hybrid operating structure, viz. C/S (Client/Server) and B/S (Browser/Server), is adopted for GZ-AgriGIS. The C/S program is developed mainly for data management (e.g. spatial and attribute data, analytical models, domain knowledge). Using the C/S program, the system manger can create and edit models and knowledge base. This maintainable feature makes GZ-AgriGIS extensible to include more crops and fit for more locations (spatial suitability). GZ-AgriGIS can be accessed through the B/S program. The browser web pages provide users a way to enter data, allowing mutual interactions between the system and end users. The entered data is submitted as parameters for analytical models and domain knowledge. The processed result is returned in forms of either digital map or/and tables.
Table 4 General steps involved in field-based crop management decisions Task Task description (1) Select desirable farm field(s) and collect the field boundary by mobile device as GPS
Support facilities
Related personnel
Result
GPS & vehicle
Farmer & GPS holder
The boundary of farm field is gathered
Farmer or extension service1)
Field boundaries are corrected
Farmer
Farm fields assigned a unique ID
(2) Process the boundaries collected (differential processing, coordinate and projection transformation, etc.)
Computer & GIS software
(3) Login GZ-AgriGIS. Select field (s) through Internet (by unique IDs)
Computer accessible to Internet
(4) Sample soil and crop of the farm field (identified by ID) and send the samples to the district extension service for assay (5) Extension service makes chemical or physical analysis of the samples. The items assayed are decided by domain knowledge stored in the system database
Field sampling tool & Farmer or extension service Samples (soil & crop) vehicle are collected Tools or device for chemical or physical analysis
Extension service
Items are analyzed by assay device
(6) The result of assayed samples is sent back to the Communication tools like Farmer or extension service Properties of the field farmer (user) email, fax or telephone soil and crop acquired
1)
(7) The assayed samples are entered for GZAgriGIS. Some data are stored in database for further analysis and others only for temporary use
Computer accessible to Internet
Farmer
Required data is entered
(8) GZ-AgriGIS analyzes the data, based on entered data and data retrieved from the database
Computer accessible to Internet
Farmer
Decisions suggested for farmers
The region is divided into several districts, each equipped with a crop-and-soil sampling analysis extension service from the database. Optimum nitrogen fertilizer type, as well as the corresponding rate of the fertilizer, can be calculated by nitrogen application models (DE model) along with the help of domain knowledge regarding crop nutrient requirements. In Figure 2(a) the field with number 000009 is activated. A web page prompts
Take a nitrogen application decision as an example, three questions are expected to be answered: 1) does the field or fields require nutrient application, 2) what kind of fertilizer is best, and 3) how much fertilizer should be applied. Items that affect nitrogen application are either entered by end users or retrieved
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the required items for the field, as shown in Figure 2(b). Some items (crop name Shanyou-61, for example) are retrieved from the database while others (e.g. planned yield) are entered by users. Additionally, other items, such as the environmental data, are not shown on the page. In terms of the questions proposed, the first and second ones will be answered with the domain knowledge by analyzing crop feature (Shanyou-61), the environment (soil, climate, etc.), and possible fertilizers (knowledge table of fertilizer). Based on the first and second answers, the system continues to provide a quantitative assessment for Question 3. The start point is driven by retrieving related domain knowledge. The system builds knowledge objects based on the related knowledge tables. The knowledge objects then find the appropriate models to make a quantitative calculation. Because the last question is a numerical calculation, the DE model (FAR model) is applied to compute the fertilizer rate for the particular field. LE models translate concepts of items into numerical values before the DE model is applied. The output module, which is powered by ESRI’s ArcIMS, formulates a digital map, showing how much fertilizer should be applied for each farm field.
Figure 2. Data input GUI of GZ-AgriGIS
4. CONCLUSION The novelty of GZ-AgriGIS is its power to assist farmers in making field-based crop management decisions with domain knowledge and analytical models that account for the uniqueness of crop and field characteristics. The incorporation of domain knowledge and models, in conjunction with GIS technology and online accessibility, results in highly useful information in an easily understandable format. Currently, due to limited available data, GZ-AgriGIS is constrained to the areas of fertilizer application and irrigation for a restricted amount of crops. However, the support for other crop management decisions can be easily added as the related knowledge and models are available.
Table 5 Data for making field management decisions Attribute data - Crop name (sub breeds), - Crop growth stage, - Expected yield, - Soil properties (chemical & physical data) of farm fields, - Crop properties (chemical & physical data) Spatial data - Field boundary layer (or SDU layer), - SUKM layer, - Main mountain layer, - Main river layer, - Ten year rainfall, ten year radiation, ten year temperature (average values of annual data from previous ten years), - Annual lowest temperature (average of 10 days), - Annual highest temperature (average of 10 days) , - DEM, - Soil type (soil distribution) layer Domain knowledge - Knowledge table of crop, - Knowledge table of organ diagnosis symptom, - Knowledge table of environmental requirement), - Knowledge table of fertilizer Models - Fertilizer application models, - Water application (irrigation) models
5. ACKNOWLEDGMENTS This research is funded by the Geographical Information System (GIS) Key Lab of Chinese Education Ministry.
6. REFERENCES [1] Runquist, S., Zhang, N., Taylor, R. K., 2001. Development of a field-level geographic information system. Comput. Electron. Agric. 31, 201-209. [2] El-Najdawi, M.K., Anthony C. Stylianou, 1993. Expert support systems: integrating AI technologies. Communications of the ACM 36(12), 54-63 [3] Batanov, D.N., and Zhuang, C., 1995. An object-oriented expert system for fault diagnosis in the ethylene distillation process. Computers in Industry, 27(3), 237-249.
GZ-AgriGIS has run well for 3 years since its initial operation in 2003. So far, it has been applied in several agricultural production experiment sites in Guangzhou to help farm managers make crop management decisions, and more are consulting with the developers to authorize their use. Agricultural bureaus from other provinces, such as Jiangxi, Hubei and Shanxi, are also considering adoption of the system.
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