IAALD AFITA WCCA2008 WORLD CONFERENCE ON AGRICULTURAL INFORMATION AND IT

DRIS-LT. A set of tools for DRIS application with learning capabilities Antonio J. González-Lázaro1, Miguel Guzmán-Palomino,1 Fernando Bienvenido1 1 University of Almeria, Spain. fbienven@ual.es. Abstract DRIS (Diagnosis and Recommendation Integrated System), it is an alternative technique to evaluate the foliar analysis of a cultivation, which uses the relations between nutrients to interpret their nutritional status. This technique compares the concentration relations of nutrients with those found in populations with maximum/optimal yielding, which are called DRIS norms. Starting with these norms, the method supplies a method to order nutrient relations in expressions called DRIS indexes. Those indexes let us to classify the production factors by their relative significance.

This technique uses leaf diagnosis independently of the age, the variety, or which part of the plant you are using; but it is not sufficiently flexible because usually the norms for a crop are generated via extremely expensive research, which limits their updating.

We propose to surpass this problem via a tool that managing the data can learn from the commercial production using the data generated by the technicians that use DRIS in our area. The software updates automatically the norms, evolving to a more reliable and flexible system for the final user. We started this idea thinking about how to share the knowledge cumulated by the farmers in our area, approximately 30000 H. of greenhouses. The system offers a diagnosis using the shared database, creating furthermore a set of charts and reports with all the details.

The tool has been developed using the OOP language Java. We have assembled two versions: autonomous and distributed. The autonomous one allows work alone, while the distributed ones let work together several users with different profiles. The agro-technician introduces the data about the farms, plots and crops, managing the diagnosis. Laboratory analysts introduce leaf analysis results. System managers include new crops starting from experimental research. The system let us to manage user and farm data taking care of specific sensible data. Keywords: CND, diagnosis, DRIS, foliar analysis, irrigation systems. Introduction Suitable dosage of fertilizers requires an exhaustive control of the elements that take part in a culture. The chemical and/or physical analyzes of soil (or substrate), water of irrigation and plants have become a fundamental tool in order to obtain the maximum production and quality. The norms of Integrated Production force generally to accomplish these analyzes to secure the previous objectives along with environmental care. [Cadahia-Lopez]. Chemical analyzes of vegetal samples are being used more and more to know which is the nutritional state of our culture.

Foliar analysis is a technique that relates the content of an element in the whole plant, or one of its parts, with their physical appearance, speed of growth, yield or quality of the harvested product. It is a more sensible indicator of the answer of the cultures to the environmental changes than the yield, but is much more difficult its interpretation. This is

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because the concentration of the different nutrients in the plant varies during its growth and, therefore, the optimal level or rank of optimal concentration is changing permanently, making difficult comparisons with reference values.

DRIS Technique (Diagnosis and Recommendation Integrated System) is an alternative to evaluate the foliar analysis of a culture, which uses the relations between nutrients to interpret the nutritional state of the same. This technique confronts the relations of concentration of nutrients with those obtained in populations with maximum efficiency or optimal, which are denominated DRIS Norms for those nutrients and cultures. Using them, the method provides the way to order the relations of nutrients in expressions called DRIS indexes. These indexes would allow classifying the considered factors of the production according to their relative importance.

Mathematically, the indices are based on the mean deviation of each relation with respect to their optimal value. For this reason, the optimal DRIS index would be zero for any nutrient. Negative indexes indicate relative deficiencies, whereas positive values show excesses with respect to the nutrients considered in the diagnosis.

The Nutritional Balance Index (NBI) computes the sum of the absolute values of the set of indexes, which are measurements of the relative balance of the analyzed nutrients. This balance improves when NBI approaches the zero value.

Main advantage of the DRIS system over other methods is its ability to do foliar diagnoses independently of the age, variety and part of the plant used. This requires the availability of a wide database (with at least several hundreds or thousands of analysis) in order to obtain the DRIS norms. Nevertheless, in order to improve the precision of DRIS diagnosis, it would be of interest that the established norms (fixed from the beginning with a set of analyzes) would be updated after each new study; this means that the systems would be learning from actual tests. This way the system would adapt to new conditions using the actual analyzes done for actual crops. Using the DRIS system would improve the system itself re-feeding its database with not only experimental but real working data. Including this functionality is the objective of the system we have developed, which description is the main object of this work.

We have used DRIS as was proposed initially by Beaufils [Franco-Hermida] with some improvements by Jones. We have adapted the system to the conditions on several Spanish speaking countries [Landriscini2002][Landriscini1997].

Different learning techniques were analyzed [Russell]. The election of the system depends on the confronted problem, being possible to use artificial neuronal networks (RNA) [8], genetic algorithms, or knowledge based systems [Alonso-Betanzos]; in our case we have just actualized target values using as a feedback the analysis and results of actual plot decisions. Methodology

In this section we will describe the most important decisions taken during the development of the system:

• Support of the application. First decision was related with the form of the application, it could be an independent application sharing data with a general common repository through Internet, or could be a web application associated to a server database managed with PHP. Both options presented advantages and disadvantages. Main restrictions were that the system had to work with a great quantity of data that must be stored in a consistent and safe data base and that some times would not be possible to work connected to Internet. So, we decide to develop and independent application with a local database and connexion with a general repository of data.

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• Loading data into the system. The system is nourished with data introduced by different users (administrator, analysts and agronomical technicians). We evaluated these possibilities:

1) From the keyboard. 2) From some wireless device. 3) From another computer acceding using remote access.

Administrator, usually in their offices, can use their keyboards. Analysts carry out their work in their laboratories, where they usually have their own computers, which could connect with the system. Only the field agronomic technicians could required some sort of wireless device, but they could load data late in their offices.

• Order of the chemical elements in the application. They are introduced by importance order of possible studies to be done using the application.

• Loading foliar data. This duty is carried out by a laboratory analyst. Selected options were: 1) On the screen with the keyboard. 2) Transferring data from a text file (.txt). 3) Transferring data from an Excel spreadsheet (.xls). 4) Transferring data from an Access database (.mdb).

This means that we needed to define the formats of the text, excel and access files that the analyst can use to load experimental data into de system.

• Validation of foliar analysis data from files.. There are three possibilities: 1) Unsupervised load of data. All area added to the database directly. 2) Supervised load of the package of data. The analyst should validate simultaneously the

whole set of data. 3) Supervised load each data value. The analyst must accept to load the data into the

system one by one. Because of the required reliability of the system, last option was selected.

• Numerical data format accepted by the application. We selected the Spanish notation with commas (“,”) and a maximum of ten digits.

• Management of historical data. All data are stored, even when they are repeated. The disadvantage is an always growing database, the advantage it’s the increasing in its reliability. All data are marked as old or new, which would let us to monitor the system behaviour.

• Generation of graphs. [JFreeChart]. There were several options, two main were: 1. Generate an Excel spreadsheet, which can be used directly to generate graphs ourselves. 2. Include into the system a graph generation option using some specific graph library as

JFreeChart. This option is more powerful and independent, so it was selected. Generated .png files could be presented directly using the JPanel or via the options of Windows, we selected the first option because its independence of the operating system.

• Report generation. [IReport] We evaluated the use of the IReport tool integrated with JFreeChart, it is intuitive and powerful when used for JasperReport written in Java, but problems in its connexion with the database, led us to generate directly the reports in web format (as individual web pages).

• Ordering data. Data are loaded from spreadsheets with a different order of that required in the application (where they are ordered by their importance in the evaluation process); so, we decided to create an index of positions of the data between the spreadsheet and the database, and later transfer the data. This let us to work with this double order reliably.

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• Creation of an Auto-executable application. [JSmooth] The system was assembled using a Java development environment. This means that in order to execute the application we are required to activate previously the Java environment. In order to generate independent applications. In this sense we had several options: 1. Translate the Java code to C using Tuba or J2C. 2. Use GJC, a program included in the family of compilers GCC of the GNU project

(under GPL), that let us to generate bytecodes for each platmform from .java files. 3. Use a programa as JtoExe (or specific IDEs) to generate an .exe file, that it a version

where java classes are replaced by platform specific code. This way we lose the multiplatform ability of the java language.

4. Create an script for each platform, for MS/Windows scripts have the .bat extension. Results Now, we are going to describe the developed tool. It has four types of users:

• Administrators. They manage the system, defining mainly crops to be managed and registering other users.

• Lab analysts. They incorporate the foliar analysis data into de systems. • Registered users – Agronomical technicians. They manage farms, plots and

greenhouses data, and generate and interpret diagnosis reports and graphs. • Unregistered users. Usually farmers or technicians they can obtain some information

about the method, but they can not manage specific studies for their farms. First three types of users must enter in the system identifying previously in the main

system screen, shown in figure 1, using de identification fields down the window.

Fig. 1. Main screen of the application.

As shown in figure 2, unregistered users can check optimal ranges of chemical elements in

the crop leaves, just a reference; and he/she can ask to be registered in the system (that requires the validation of the manager of the system).

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a/ User registration screen b/ Checking general actual limits for a crop Fig. 2. Unregistered user options.

Laboratory analysts can introduce the results of their foliar analysis for specific

plots/greenhouses. They can load data directly in their main screen or using spreadsheets or plain text files (using predefined formats), as shown in figure 3.

a/ Analyst main screen b/ Loading directly lab results

c/Loading lab data with an Excel spreadsheet d/ Loading lab data with plain text Fig. 3. Lab analyst ways for loading leaf values for a plot/greenhouse.

Administrators (or system managers) manages users (Fig. 4c) and can accept new users

(Fig. 4e), add new crops to be managed (including their first set of target values if it is possible) (Fig. 4f) and manage crop indexes and relations (Fig. 4b and 4d) from their specific screen (Fig. 4a)

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a/ Administrador main screen b/ Screen for crop managing

c/ Screen for user managing d/ Checking the data of a crop

e/ Registering an user f/ Adding a new crop to the system Fig. 4. Options of the administrator for managing users and crops

Registered users, usually agronomic technicians or farmers, can manage farms and plots

and generate diagnosis reports both textual and graphical from their main screen (Fig. 5a). There is a set of option to manage the different farms (“Gestionar fincas”, Property Management) (Fig. 5.b), and their plots or greenhouses (“Gestionar Parcelas”, Plot Management) (Fig. 5c and 5d). Finally they can ask for a diagnosis report for a plot (included in a farm), generating the textual report (Fig. 5e) and specific graphics (Fig. 5f). Other options include the elimination of farms and plots. Reports include the results for the DRIS and CND methods for a given culture (that must be one otf those assigned to the specific plot). One registered technical user can unsubscribe himself using the specific option of the main screen.

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a/ Registered user main screen b/ Checking one farm data in the farm management screen

c/ Checking one plot/greenhouse data d/ Registering a new plot/greenhouse

e/Generation of a report for a plot/greenhouse f/ Graphical results for a plot/greenhouse Fig. 5. Registered user (agronomist or farmer) functionality.

It is possible to discharge crops, farms, plots and users, but really they are not fully

eliminated; their data are just “frozen” and made invisible for the different users. Main reason of this is facilitating the reincorporation of the different element, mainly crops without losing all the cumulated information (as the results of previous diagnosis).

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Conclusions and futures Works Main conclusions after nearly one year of development efforts are: We have developed a software tool that helps the agronomical technicians to diagnosis

fertilization using DRIS. This sort of tool was not available. This tool incorporates DRIS and CND diagnosis methods creating graphs and textual

reports. It is a distributed work environment, different agents as lab analyst or agronomical

technicians have their role, sharing a single database. The tool takes advantage of all the tests done for a given culture in order to improve

their norms and relations. The system increase its reliability as it incorporates new data from the work analysis that are been done. Even, it offers a great adaptability, as the experimental data are obtained from real crops in the work area (not only from lab experiment in another area).

It is a modular system, facilitating their expansion in the future (including, for example, new diagnosis methods).

It has been registered with code AL-82-2008. It is a tool of diagnosis created meanly by and for the agricultural technicians, and

developed from a narrow collaboration of a multidisciplinary team of agronomists and computer science specialists.

Next step will be to test the system in several areas, West County of Almeria in Spain, Sabana de Bogota in Colombia, Roses plantations in Ecuador and pampean areas in Argentina. Next works will include the translation to Portuguese, English and probably French. References Alonso-Betanzos, A. et al., (2004). Ingeniería del conocimiento. Aspectos metodológicos.

Pearson Prentice Hall. Cadahía-Lopez, C., (1998). Fertirrigación: Cultivos hortícolas, frutales y Ornamentales.

Ediciones Mundi-Prensa. Madrid, Barcelona, Mexico. Franco-Hermida, J.J., (2006). Determinación de normas de diagnóstico nutricional para el

cultivo de rosa de corte en Colombia. Master Thesis. Nacional University of Colombia. Code: 790624.

IReport. Work with information. Tool of automatic generation of information (verified December 2007)

http://www.jasperforge.org/jaspersoft/opensource/business_intelligence/ireport/. JavaMail. Guide of java mail (verified December 2007). http://www.chuidiang.com/java/herramientas/javamail/empezar-javamail.php. JFreeChart. Generation of graphs (verified December 2007). http://www.jfree.org/jfreechart/. JSmooth. Create feasible for java. (verified December 2007) http://two.xthost.info/ytrio40/tutoriales/JSmooth.pdf. Landriscini, M.R., Galantini, J.A. and Rosell, R.A., (1997). Determinación de normas para la

aplicación del sistema DRIS en cultivo de trigo de la región semiárida bonaerense. Ciencia Del Suelo v.15, p.17 – 21. Buenos Aires, Argentina

Landriscini, M.R. and Galantini, J.A., (2002). Aplicación de la Metodología DRIS al cultivo de trigo en la región semiárida Pampeana. Conicet – CIC (Pcia. Bs. As.). Dpto. Agronomía – U.N.S., 8000 Bahía Blanca, Argentina.

Russel, S. and Norving, M. (1995) Russell and Norving, Artificial Intelligence: A modern Approach, Prentice Hall.

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