Fuzzy based Agriculture expert system for Soyabean Chandra Prakash1, Amar Singh Rathor2, Gaur Sunder Mitra Thakur3 Lovely Professional University, Phagwara, Punjab.
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[email protected] Abstract. Agriculture forms the backbone of the Indian economy. Expert system can play important role in elating the yield of agriculture in India. The paper introduces a fuzzy based prototype expert system named as “Prithvi” for Soybean. This expert system is signified to help the farmers, researchers and students through graphical user interface and provides an efficient and goal-oriented approach for improving yield of Soybean, by suggesting optimal decision farming methods like sowing period, crop selection, sowing method, fertilizer selection, insect and pest selection according to variety. The system gives correct and consistent results. The results of implementing the designed fuzzy expert system at Panchayat level in Rajasthan were affirmative. Keywords: Expert System in agriculture, Fuzzy Logic, Prithvi, Soybean Crop.
1
Introduction
From agricultural point of view, India is a singular country. The Indian economy is considered as agrarian economy. It contributes about 12.3 percent of the gross domestic product. Vast area of level land, rich soils, climatic variations and a long growing season are some of the prosperous circumstance that favors various types of crop in India. It has the highest percentage of land under cultivation in the world [1’=ECONOMY Survey 2011-2013]. In spite of this, the productivity of agriculture is still very low. Dependency upon monsoon rain and old cultivation methods are some of the key reasons responsible for the low productivity of agriculture. Inputs likebetter quality of seeds, land preparation, time of sowing, weed management, diagnosis of insect, disease and nutritional disorders, storage, marketing of the produce are also not used by most of the farmers. Farmers are not using artificial ways of cultivation methods. Farmer need advance expert knowledge to take decision during land preparation, sowing period, seed selection, seed management, fertilizer management, irrigation management, integrated pest management, storage etc. for higher crop production. Based on land use statistics, approximately 34262192 ha of Rajasthan state in India has been developed for agriculture. Out of this total cropped area is approx. 22208291 ha used [1]. Rajasthan produce 10 % Soybean on an average in a year and ranked third in India followed by Madhya Pradesh and Maharashtra as shown in figure 1. To increases the production of Soybean crop farmers need mean time advice and solution from experts [2]. In helping farmers to pick the most suitable variety of crop, the Department of agriculture has provided the customer service camps. However there is a need of an expert system that can assist the farmers and reduce the consultation time. This system will be an attempt to compile the agriculture knowledge and make it more readily available.
An advance system like expert system is more flexible and gives the end user more choices for farming methods. Expert systems are being used in agriculture which assists the farmers to make right decisions. Expert system has worked as knowledge distributer among farmers, researchers and students through graphical user interface and provides an efficient and goal-oriented approach for improving yield of crop. Expert system is most powerful approach that simulates human knowledge from an expert in specific domain for assist human to make decision at the level of or greater than human expert [3].
Figure 1 : Average yearly Production of Soybean in India
This paper introduce a Fuzzy based prototype expert system named as “Prithvi” for Soybean. Prithvi is a Sanskrit word meaning earth. This expert system can act as a powerful tool to alleviate the farmers, researchers and students by providing a graphical user interface to process its input and output sets. This provides an efficient and goal-oriented approach for improving yield of Soybean. It will suggest optimal decision farming methods like sowing period, crop selection, sowing method, fertilizer selection, insect and pest selection according to variety especially in situations where agricultural specialist assistance is not readily available when the farmer needs it at most. Details of this system are described in the remainder of this paper. The paper is organized as follows: Section 2 covers a brief description of Soybean and previous expert systems in agriculture methodology. Section 3 is about design and development of expert system. Section 4 covers the concept of fuzzy logic, section 5
contains experimental results of evaluating the developed techniques are presented. Finally, conclusions and future scope are discussed in section 6.
2
Background
2.1
Soybean (सोयाबीन)
The scientific name of Soybean/Soyabean is Glycine max. In Hindi it is called as सोयाबीन. Soybean belongs to legume family and it is an erect bushy hairy annual herb having trifoliate leaves and purple to pink flowers; extensively cultivated for food and forage and soil improvement but especially for its nutritious oil-rich seeds. It is considered to be native to Asia. India ranked fifth across the world in production of Soybean after USA, Brazil, Argentina and China. Soybean has gained a vital status in agriculture and oil economy of India. The country presently produces about 6 million tons of Soybean per annum. Out of this, 5% is used for food and feed, 10% for seed and 5% for oil extraction [3]. For the proper yield soybean needs 13 different mineral nutrients namely nitrogen, phosphorous, potassium, sulphur, calcium, magnesium, zinc, manganese, copper, iron, boron, chloride and molybdenum. Among these Nitrogen, phosphorous and potassium are the most important nutrients. On the basis of desirable plant features like high yield, ability to withstand drought, color, or ability to with stand wind and weather farmers can select type of seed. In India soybean is a Kharif crop. Life cycle of Soybean plant varies from 90-120 days mainly from July to November. The Soybean sowing period is from July to August. Soybean varies in growth and habit. Seed should be planted about 1.5 inches deep in rows, which may be at a desirable distance of 30 inches from each other in cultivated or tilled land by a tractor or a planter. The height of a soybean plant can be from 0.2 to 2.0 m. The pods, stems, and its trifoliate leaves are covered with fine brown or gray hairs. Depending upon seed type its small flowers color may vary from a white to a violet or purple. From these flowers, the soybean plant grows small pods that contain the young seeds. The plant drop leaves before the seeds mature. 2.2
Expert Systems in Agriculture
Agriculture decision making activates are often vague and based on intuition. This makes agriculture a complex problem and thus requires very complicated optimization and modelling steps when agriculture is attempted through conventional techniques. The agricultural production management problem includes identification of correct sowing period, crop variety selection; land preparation, sowing method, fertilizer and pest selection according to variety. It also includes lack of experts to support the agricultural growers, and the heavy dependence upon the experiences of
these experts. Thus there is need of expert system approach which is more flexible and gives the end user wide choices for farming methods. Expert system in agriculture is not a new idea. It is been used in agriculture since the early 1980s. Agriculture Expert systems (AES) are being developed by various Agricultural Research Institutes and researchers from different countries to assists the information needs to the farmers for plant-disorder diagnosis, management and other production aspects for agriculture. An expert system is software that manipulates encoded knowledge to solve problems in a specialized domain that normally requires human expertise [4]. In case of Soybean a web based fuzzy expert system SOYPEST (Soybean pest Expert System) was proposed by Harvinder Singh. Et..al. in year 2002 for Integrated Pest Management(IPM) [5]. Identification and Diagnosis has been divided in four phases. Phase 1 includes the identification of insect pest on the basis of crop damage symptoms. Phase 2 includes insect pest morphology and phase 3 checks from pest image and phase 4 Suggesting control measures for expert system Soypest (Raj et.al.,2002). Dr. A. Vinaya Babu et.al conduct a comparison study on various Expert Systems in Agriculture in year 2006 [6]. The central laboratory for agriculture expert system (CLAES) developed an expert systems with provides many feature such as disorder diagnosis, disorder treatment, irrigation scheduling, fertilization scheduling and Plant care, assessment of a farm and pest control. Most of the expert system includes these features in it. A web based expert system “Dr. Wheat” is used for diagnosis of diseases and pest in Pakistani wheat in year 2008 by Fahad Khan Et.al. It covers the two major classes of problem namely diseases and pests that normally occur in wheat crop. The expert system a help to farmers for providing help in problem solving. Diagnosis or diagnostic problem solving is the process of understanding what is wrong in a particular situation [7]. In 2009 S.Helen Et.al. developed an Agricultural Expert System, ‘Diagnos-4’ for tackling the problems in transfer of technologies related to plant protection aspects of nine important crops of Kerala. Since extension personnel are expected to use the ‘Diagnos4’, the study was conducted among the extension personnel in the Palakkad district of Kerala, India. The sample of the study constituted sixty extension personnel. The respondents were selected purposively who were mainly dealing with the cultivation of rice, coconut and banana as major crops. They were divided into two groups. First group was exposed to Agricultural Expert System alone (T1) and the other group were exposed to Agricultural Expert System with human expertise (T2) on the plant protection technologies of rice, coconut and banana crops [8]. A Survey on Identification of Soybean Crop Diseases was conducted by Pratibha and Toran in 2012. They discussed about the identification practices of diseases in Soybean crop by checking symptoms. Author proposed a neural network model to calculate all the result generated by the observation and then detect the affected and non-affected areas in Soybean plant [9]. Another Expert System in the area of agriculture designed by Sarma and Singh that describes the design and development of the rule based expert system, using the shell ESTA (Expert System for Text Animation). The designed system is intended for the diagnosis of common diseases occurring in the rice plant. ESTA programming is
based on logic programming approach. The system integrates a structured knowledge base that contains knowledge about symptoms and remedies of diseases in the rice plant appearing during their life span. An image database is also integrated with the system for making the decision support more interactive. The pictures related to disease symptoms are stored in the picture database and the intelligent system module prompts these with the interface based on rule based decision making algorithms [10]. There is a need of an expert system that can assist the farmers in choosing the farming techniques for improving yield of crop. A Fuzzy based prototype expert system named as “Prithvi” for Soyabean has been introduced in this paper. It will suggest optimal decision farming methods like sowing period, Crop selection, Sowing method, Fertilizer selection, especially in situations where agricultural specialist assistance is not readily available when the farmer needs it at most.
3 Design and Development of Expert System “Prithvi” For Soybean Crop 3.1 Expert System : Usage of knowledge-based system has extensively increased during last decade. An expert system is computer program, which mimics behaviour of an expert in a particular area of knowledge. The main difference between other software and expert system is that they process knowledge instead of data or information (Darlington,2000). Expert system has worked as knowledge distributer among farmers, researchers and students through graphical user interface and provides an efficient and goal-oriented approach for improving yield of crop. An expert system is software that manipulates encoded knowledge to solve problems in a specialized domain that normally requires human expertise.An expert system. mimics behavior of an expert in a particular area of knowledge and is of great use where the expert is not readily available. It has three main component i.e. a Knowledge base (KB), an inference engine and control strategy (CS). Knowledge structure in the form of IFTHEN rules by the person’s known as knowledge engineer’s, is stored in a KB. This knowledge is processed by inference engine under supervision of control strategy for simulating expert advice [4]. Farmers in India still follow the conventional methods and seek advice from the officer of agriculture or from other senior farmers. The concern officer provides useful information to the farmers such as pest control, the suitable crops and others. This information play a crucial role in increasing the production of the crop. First step in the developing any expert system is problem identification. The idea behind developing this AES for Soybean crop is that it can help the farmers to increase the production of Soybean crop. For the same Rajasthan is selected as a problem area. The complete pre-planning and suggestion for Soybean crop could result in higher yield production. The key factors that affect the production are sowing period selection according to rain fall and variety selection depending soil nutrients and many other factors.
This system will enable farmers, researchers and students to benefit from the knowledge of different experts(Farmers and agriculture supervisors) that is integrated into a single Knowledge base. The expert system is based on the information generated from long term research in both laboratory and field conditions. The expert system reduces the time required to solve the problem without waiting for an expert advice. 3.2
Modular Development Approach
Modular approach is used in the development of AES, Prithvi. The complete ES is divided into five modules; it uses the Knowledge Base Inference for the final output generation. 3.2.1.
Module 1: Detection of Nutrient Level in Soil
This module generates the level of soil nutrients available in Rajasthan region. Level of Nitrogen (N), Potassium (P), and Potash (K) is shown in the form of High (H), Low (L), Medium (M) corresponding to particular district from KB.[sample] 3.2.2.
Module 2: Farm Size Calculation
This module takes the input of farm size in Hectare and converts it into Bigha. This Module is helpful for calculating the amount of seed and Fertilizers that will be used per Bigha in farm. 3.2.3. Module 3: Sowing Period Month of Sowing and expected date of sowing is taken as input from user and shows its impact factor for Soybean production for the current season. 3.2.4. Module 4: Sowing Method and Rain Fall In this module the ratio of last rain fall and expected rain fall is taken as input and it will advise users that how deep the seed should be planted at the time of sowing and what kind of sowing method (by tractor or traditional “Kelti” ) should be used for better production. Seed should be planted about 1.5 inches deep in rows. 3.2.5 Module 5: Variety Selection and Advice Generation This is the important module of this ES and it will suggest farmers the best possible selection of variety from given list of Soybean varieties. For intelligent decision Fuzzy logic is used as machine learning technique. The selection of variety depends on the Defuzzy value of previous 4 modules. After the selection of variety Prithvi
generate the disease and insect prevention advice along with possible insecticides and pesticides in corresponding conditions.
Figure 2: Module Design of Soybean Expert System 'Prithvi'
3.3 Knowledge Engineering Knowledge engineer collects knowledge from domain expert and transfers it into production rules and creates knowledge base. A precise domain is required by an expert system. The domain must be compact and well organized. The quality of knowledge highly influences the quality of expert system [11].
Figure 3 System Structure of Soybean Expert System
3.3.1
Knowledge Acquisition
For the development of Prithvi, knowledge has been acquired from agriculture researchers, Soybean experts, and farmers and from published literature. Information can be found in various forms. Text, images, video, audio are forms of media on which information can be found, and the role of information technology is to invent, and devise tools to store and retrieve this information.
3.3.2
Literature References
Published literature consists of books, soil fertility guides, previous research papers, surveys and reports, soil nutrient level database, pesticide and insecticide detail booklets [Pratibha dongre, verma].Preliminary knowledge has been compiled from plant morphology books and research papers in journals and experts. 3.3.3
Expert Interview
Interviewing is a way to collect elicit facts or statements as well as to gain knowledge from individuals. The interviewer can pursue in-depth information around the topic.
Expert interview is very important for knowing about complete Soybean life cycle. Interviews have been beneficial to acquire knowledge about Soil fertility level, water level, irrigation and to understand the current agriculture scenario in Rajasthan. Variety selection ratio and impact of variety on Soybean production have been discussed with experts. Soil nutrients N, P, K levels and linguistic granules have been decided at time of meeting with soil expert. Preliminary knowledge collected from literature has been verified by experts from agriculture research laboratories. Tacit knowledge and experience gained by Agricultural Development Laboratory (ADL) experts has been elicited [Chambal Fertilizers and Chemical Limited]. 3.3.4
User Interview
The problem definition for Expert System has been developed after consultation with farmers from Rajasthan region specially in kota region. Farmers including Sarpanchs form various villages, agriculture supervisors (having knowledge about variety production and diseases). Knowledge from different districts has been consult to elicit the idea for the need of the system. Interview recording of farmers at Gram-Panchyat level helps to observe the farmers different viewpoints and different opinions about sowing methods and variety selection
4. Fuzzy Logic Fuzzy logic is a branch of artificial intelligence that handles imprecision, vagueness and insufficient knowledge. Fuzzy logic is best suited for representation of information extracted from inherently imprecise data. Agriculture decision making activities are often vague or based on intuition. Fuzzy logic can work in this scenario with reasoning algorithms to simulate human thinking and decision making in machines. These algorithms let researcher to build expert system in the areas where data cannot be represented in binary form. Fuzzy logic lets expert systems perform optimally with uncertain or ambiguous data and knowledge This concept of fuzzy logic was proposed by Lotfi A. Zadeh in 1965 (Zadeh, 1965)[]. In contrast to conventional bollean logic where each element must have either 0 or 1 as the membership degree, fuzzy logic can be thought of as gray logic, whose members may have degrees of membership between 0 and 1. In binary logic if value is 0, the element is completely outside the set; if 1, the element is completely in the set. Fuzzy logic associates a grade or level, with a data range, giving it a value of 1 at its maximum and 0 at its minimum. It is used in modelling imprecise concepts and dependences (set of rules). Fuzzy logic surmounted the problem of classical logic by allowing statements to be interpreted as both true and false. Fuzzy logic requires knowledge in order to conclude. This knowledge is stored in the fuzzy system and provided by an expert person who have experience or who knows the process of that specific domain. A fuzzy expert system consists of fuzzification, inference, knowledge base and defuzzification subsystems and set fuzzy logic to reason about data in the inference mechanism.
4.1 Knowledge Representation Knowledge extracted during knowledge acquisition phase (from domain expert or other sources of expertise, like books and journal publications) is needed to be representing in form of case base. The proposed expert system uses a fuzzy logic based reasoning process to analyze the given scenario and prescribes an accurate decision similar as expert does. This AES Prithvi is rule based representation in logical paradigm of simple If-THEN in backward and forward chaining. We have considered forward chaining for knowledge representation in simple IF-THEN rule form. Two major input parameters are considered in Rain fall module. One is Expected rain fall and other one is Last rain fall. The moisture level of soil depends on both parameters. The output of this will be the basis for deepness parameter for sowing. Rule: IF (last_rain is too_close) And (expected_rain is too_close) then (machine_deep is up ) Rule: IF (last_rain is too_close) And (expected_rain is close) then (machine_deep is medium). Rule: IF (last_rain is close) And (expected_rain is close) then (machine_deep is up ) . Fuzzy logic rules with two inputs are often represented in matrix form to represent AND conditions. Figure 4 illustrates an 4 matrix (16 rules) that uses two inputs, X1 and X2, and one output Y. One advantage of this matrix representation is that it makes it easy to represent all the rules for a system.
Figure 4 Fuzzy Logic Out-Put Rule Matrix
These parameters are used as Fuzzy Membership Function and determine the flow of control among the sections in the knowledge base. The value for any parameter is calculated from end user’s response to GUI, through other parameters or as a result of application of Rules. Matlab is used as a tool for implementing this system. The representation of the rules in the Rain Fall module is shown in Figure 5. We have developed the KB for the Rain Fall module to perform machine deepness controls in accordance to with the user’s response.
Figure 5 Screen Shot of the Rule Editor
4.3
User Interface
A Graphical User Interface (GUI) and rule base integration approach is provided for better functionality. Efforts have been made to gear the response up to the simplest level and set queries are able in conveying the message of knowledge base to the user. All the modules have been arranged such that the system is capable in making correct response at the right moment during the consultation.
5.
Conclusion and Result
This paper discusses the need, design and development steps for an expert system ‘Prithvi’ for Soybean Crop. The main objective of expert system ’Prithvi’ is to provide expert knowledge about Soybean crop to user before sowing of Soybean crop. This gives the value added computation results. By using the system end user can get mean time planning suggestion for Soybean crop. It will help farmers for selecting the best variety of Soybean according to their budget. The knowledge base contains the knowledge about different factors in the form of different modules from the domain expert. The architecture presented is an integrated system with user friendly GUI. One of the key challenges is to transfer latest updated information to farmers. But most of the expert systems are in English language, the experience and lessons learned from the development of expert system suggest that system is still need to implement in local language (Hadoti or Hindi). The system needed to be expanded and updated to accommodate the new varieties of Soybean.
6.
Acknowledgement
We express our thankfulness to ADL, Kota, Rajasthan for their support. Also we are thankful to Ass. Prof. Prateek Agrawal, Deptt. Of Computer Science, Lovely Professional University ,Phagwara, Punjab for their help and assistance in carrying out this work.
7.
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8.
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