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KISAN*: An Expert System for Soil Nutrient Management

Rajkishore Prasad Lecturer, University Department of Electronics, B.R.Ambedkar Bihar University, Muzaffarpur, Bihar, India.

E-mail: rkishore2k@hotmail.com Dr A.K Sinha

Professor, Deptt of Electrical Eng, MIT.Muzaffarpur, Bihar, India. Dr.Rajeev Ranjan

Lecturer, Deptt of Physics, College of Commerce, Patna, Bihar, India.

Abstract: This paper describes the suitability of application of expert system technology in agriculture and propose the development of a rule-based expert system, named KISAN, for the nutrients management of he soil. The proposed system uses the knowledge of visual deficiency symptoms, evident in the plants, to diagnose the nutrients deficiency/excess in the soil. This system has been developed in the Borland’s Turbo-prolog 2.0 environment. The visual deficiency symptoms of all the essential elements have been included in the knowledge base of the expert system. The system first finds out the deficiency/excess of nutrients in the soil and then recommends the name of suitable fertilizer taking in consideration some chemical properties of the soil. The results given by the system have been found to be consistent and sound. However, the diagnosis result has been found to be more accurate in the case of sever deficiencies. Keywords: expert system; knowledge base; deficiency symptoms; turbo-prolog. 1 Introduction All of us directly or indirectly depend on agriculture from where come commodities to feed the living beings. In the developing countries like India, Bangladesh, Pakistan, Egypt etc agriculture is the occupation of major portion of population. However, agricultural practices are more manual and technically non-advanced in comparison to developed countries. Farming in the developed world has become technologically highly advanced as a result the decision that farmers face are complex. The days of printed notes are gone and computer based solutions are welcomed [1]. In the modern time agriculture has come up as complex business and its success requires application of the best available technologies. In reality, the science and engineering of agriculture is interdisciplinary platform, as shown in Fig.1, and it requires gathering, compilation and rational application of knowledge and information from all such diverse areas. Since the area of arable land is limited, the proper management of other factors influencing the agricultural production is essential and important to meet the increasing demand of food. The production of crop depends on so many factors like fertility of the soil, type of seed, climatic condition, water logging, application of fertilizers, pest and disease control etc. For better yields every factor requires proper planning and management that in turn needs correct decision making from farmers based on information and knowledge obtained from different related areas. Soil plays role of mother in the production of any crop as it supplies different essential elements, called nutrients, needed by the plant. The growth of a plant depends on sufficient supply of these elements to it. The fertility of soil is judged by the availability of nutrients in it in a form that can be used by the plant. The unavailability or availability in a form unsuitable for plant or insufficient availability of these elements in soil results in malnutrition of plant. The proper growth of plant is retarded, which may lead to poor production or no production if not checked. Since the plants need varying amounts of different nutrients at different stage of growth, the maintenance of fertility at the proper level in the soil is especially essential during the cropping period [2]. So the knowledge of deficiency/excess of the nutrients in the soil is very important in taking care of plants. Different methods like visual symptoms, soil test, plant tissue analysis; biological test and irrigation water analysis [2,3] are used to test the availability of nutrients in the soil. Each method has its own merits and shortcomings and no single method is capable in fully diagnosing the nutrient deficiency. Except first method every other method is complicated and beyond the direct reach of a farmer.

* KISAN is a Hindi (National language of India) word and it means farmer

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The observation of visual symptoms in the plant is direct approach and can be used as an immediate and first aid clue in recognizing the nutrient deficiencies. This method is very practical when crop is in the field. Such symptoms are caused by the unbalanced nutrients supply to the plants and more or less symptoms are common in all the plants species. Farmers use, roughly, such symptoms as rule of thumb to find out lack of nutrients. In this paper our main concern is with the development and application of expert system in the maintenance and management of soil fertility when the crop is in the field. In the proposed expert system such visual symptoms have

been used to develop knowledge base. The Knowledge base also contains knowledge about fertilizer application and system is capable in recommending the appropriate fertilizer for the found deficiency. Several other expert systems have been developed for the same purpose using different diagnosis keys to assess the nutrient excess/deficiency in the soil. Fertilizer Advice and Consulting System (FACS) is based on soil analysis and plant analysis result [4], a decision support system for manure management in [5], Fertilizer Report Writer expert system [6] and WISPer Ver.2 [7] exploit soil analysis report to diagnose the deficiency. The expert system Farm Nutrient Planner described in [8] uses soil analysis result, crop to be grown, previous farming history etc. to decide nutrient requirement of the field. The rest of the paper is organized as follows. Second section presents necessity and role of expert system in agriculture. Section 3 presents rule based expert system architecture and development of the proposed expert system. Consultation and result be presented in the 4th section .In the same section discussion, conclusion and future work have also been presented at last.

2 Role of Expert Systems in Agriculture Expert system evolved as first commercial product of Artificial Intelligence and is now available in large number of areas [9] specially related with decision-making. The appropriateness of this technology has also been recognized and realized in the field of agriculture and several successful systems have been developed [10]. The modern time agriculture requires information and application of knowledge from different interacting fields of science and engineering to do appropriate decision-making that in turn depends

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on interplay of these information and knowledge. As shown in Fig.2, we need to do several processing on information and knowledge to take decision. There is need to make adequate flow of new information and knowledge from laboratory to farmer to put them in practice for better production. This needs agricultural specializations and technical awareness in farmer or a human expert to help the farmers in decision-making. Existence of agricultural specializations and full awareness with technological progress in a farmer is a very rare thing in developing countries. Human experts are not always available, may not be accessible to every farmer or if available consultation may be highly expensive. The other complications are that the decisions in agriculture practice depend on large number of factors. In fact, decisions depending on a single factor are rare. Thus even for human expert it becomes

cumbersome to take all the factors into consideration while taking decision. All such problems have resulted in the development and evolution of the concept of computerized farm management and precision agriculture [11,12], for which expert system holds good scope with extensive potential. The use of modern information technologies improves the knowledge base and increases the ability to control the production practices which in turn reduces risk and uncertainty, improves the efficiency of decision making and better identifies the variations in various influencing factors thereby rendering better management strategies for the farm [13]. It is also important to note that it is not possible to encode all the decision making process of a human expert in the software but on a Computer it is possible to store much of the information that an expert needs to make decisions and can make them available for others. So the concept of knowledge-based agriculture has enough potential to revolutionize the agricultural production. 3 Expert System Architecture & Development An expert system is a computer program that uses domain specific knowledge and inference techniques to simulate the problem solving behavior of a human expert of the same field. The system allows the modeling of information and knowledge at higher level of abstraction to implement the human logic of problem solving. Rule based programming is one of the commonly used techniques to develop expert system and the same has been used in the present work too. The rule based expert system [14,15] store large body of facts called declarative knowledge and rules i.e. procedural knowledge to manipulate the facts. The system is capable in using its internal knowledge and rules to formulate its own solution procedure based on problem definition. The proposed system, like the other rule-based system, has the following four functional modules:

(i) Knowledge base (KB) (ii) Inference engine (iii) User`s interface and (iv) Explanation module

The interconnection and arrangement of these modules is shown in Fig.3. The present system has been developed in the turbo- prolog [16] environment, which works as an inference engine in backward chaining. The development process of the expert system is very systematic and can be carried out in different stages [17]. The whole process followed has been presented in the flow chart diagram shown in the Fig.4.The KB of the present system consists of two modules. First module of the knowledge base for identification of lacking nutrients and other is for recommending appropriate fertilizer. Each module

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consists of two parts namely rule base and dynamic database. Dynamic database is common for each module. The rule base of first module contains self-standing statements and rules related with deficiency symptoms and identification of lacking nutrients. The second module contains facts and rules for selection of fertilizer. The dynamic database is used by the system during the consultation period to store the

information supplied by the user and decisions reached by any of the two modules. The dynamic databases are automatically cleared when the consultation is over. The whole process of KB development has been carried out in the following way 3.1 Knowledge Acquisition In this phase of expert system development cycle, domain specific knowledge is transferred to the knowledge engineer. The acquisition of knowledge for the proposed system has been obtained from two sources namely human expert and by gleaning the useful knowledge from the standard references [2,18,19]. Efforts have been made to collect more and more heuristic knowledge to identify the lack of nutrients based on deficiency symptoms. The deficiency symptoms related with all the macro- and micronutrients have been included. Knowledge regarding recommendation of fertilizer name, based on identified deficiency and chemical nature (pH value of the soil) of the soil has also been acquired in the same way. 3.2 Knowledge Representation: The proposed system is a rule-based system and makes inferences with symbols, which requires translation of domain specific knowledge in the standard symbolic form. The facts and rules have been represented using first order predicate calculus in the compliance with the syntax of turbo-prolog [20,21]. The rules are in the format of If – Then statement i.e. If C then A which implies if the condition part C is evaluated to be true then the action part A should be performed. However, the prolog allows the representation of the same as then/if conditional [22]. For example the deficiency symptom of nitrogen, as represented in the knowledge base, is given below using two predicates deficiency and symptom in compliance with the syntax of turbo-prolog. Each symptom predicate invokes the system to place questions, related to visual symptoms in the plant, before the user.

deficiency (nitrogen): - symptom (flower_ bud, pale_ green);symptom (fruit_ size, poor),symptom (roots, thinner

reddish_ brown), symptom (branching, poor),symptom (leaf, white_spot),symptom (plant_ growth, stunted).

Similarly, the deficiency symptoms of all other essential elements have been placed in the knowledge base. These production rules are placed in the first come first serve strategy [23]. However, while arranging rules maximum weight has been given to the expert’s opinion. Since the dynamic database of the system

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contains the facts related to particular consultation, at the beginning of the consultation the dynamic database is empty and is itself re-empted at the end of the consultation.

3.3 User’s interface This module provides way for interaction between the user and the expert system .In the proposed system, query based user interface [16] has been implemented. Efforts have been made to gear the questions up to the simplest level and the set queries are able in conveying the message of knowledge base to the user. The logical sequence of symbols has been arranged such that the system is capable in making right questions at the right moment during the consultation. 3.4 Explanation Module This module of the system provides explanation of its own reasoning and decision. This is important for validation and understanding of the solution and reasoning process of the expert system. In the proposed system this facility can be availed by using in-built trace facility of the inference engine. The system provides trace of steps through it self in a separate trace window. Most of the rule-based expert system provides its explanation in same way [24]. 4 CONSULTATION AND RESULT The system starts consultation by introducing itself and begins to put questions from the first module of the knowledge base and stores the response of the user and decision reached, if any, in the dynamic database. After making investigation for the lack of nutrients system goes, on the request of user, to the second module to find out the name of suitable fertilizers. In this stage of consultation, system uses results of the first module as well as current response from the user. The transition inside the system during consultation is shown in Fig.5 and in Fig.6.Dashed line in Fig.6 shows the implemented depth first search path through the knowledge base during consultation. The system makes inference from the known facts using response of the user to its queries. So for better the diagnosis, correctness in the user’s response is one of the most important things. The deficiency symptoms in the plant becomes more obvious in the case of severe lacking of nutrients so the performance of the system improves in that case and decision of the system becomes

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more reliable The decision made by the system have been found sound and consistent. The knowledge base of the system is large and it can be modified and extended easily if required. The fertilizer recommendation module of the system considers the chemical properties of the soil. So use of fertilizer of its recommendation abolishes the risk of making soil unnecessarily alkaline or acidic. However, the deficiency diagnosis is based on visual symptom only so

results are not always reliable. Visual symptoms may be confusing because sometimes visual symptoms of other diseases, insects and pesticide damages are similar to that of nutrient deficiency. Also even in nutrient deficient plants do not show visual symptom and hidden hunger of the plant cannot be diagnosed with visual symptoms. The visual symptoms vary from plant to plant and are also affected by environmental conditions too. The diagnostic result can be improved by integrating the knowledge for diagnosis based on soil analysis and plant analysis. Also the collection of photographs of different plants under different nutrient deficiency can improve the performance of the system. Our work is going on to implement such system in Visual Prolog. However, to best facilitate the use of expert system in agriculture the knowledge base based on remote sensing information for deficiency symptoms detection can be developed and in coming days, especially, in developing countries such system will be very effective tool to inform the farmer about their crop condition and fertilizer applications by the concerned authority at global, national or state level Acknowledgement We express our thankfulness to CSIR, NewDelhi, India for their support. Also, we are thankful to Prof. A. Ali Khan, University Deptt of Botany, Prof A. Hamidi, RMLS College, Deptt of Botany, B.R.A.Bihar University, India, Muzaffarpur & Ramesh Kumar, research scholar Ragendra Agricultural University, Pusa, Muzaffarpur for their help and assistance in carrying out this work.

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