GEO- INTELLIGENCE SYSTEM: A FRAME WORK FOR AGRICULTURAL IMPROVEMENTS

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1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: doi: /ijpam.v116i12.13 ijpam.eu GEO- INTELLIGENCE SYSTEM: A FRAME WORK FOR AGRICULTURAL IMPROVEMENTS P.Parameswari 1 and M.Manikantan 2 1,2 AP (SRG), Dept. of MCA, Kumaraguru College of Technology Abstract This paper sets out a conceptual framework to guide farmers and to help the stakeholders in agricultural sector. Its prime focus is to increase the productivity in this sector by proper utilization of Information and Communication Technologies (ICT). Geo- Intelligence is an agricultural knowledge base that can help the farmers to be connected with agriculture knowledge services like weather details, soil fertility related information, crop patterns, pest alerts, and government schemes. It reviews and analyzes the past data on agriculture and farming activities to assess and provides the necessities and requirements to the farmers. This framework forge linkages with the stakeholders like farmers, government, experts, and academician that can help them to transfer more timely knowledge and technology enhancements in agricultural enrichment. Key Words: Agriculture, Data Mining, Knowledge Base, Spatial Data, Data Analysis 1. Introduction Agriculture is one of the strong sectors of Indian economy.around 58% of population depends on agriculture. Requirement of food rises as population grows. According to the Economic Survey of 117

2 India, in , agriculture industry contributed 16.5% to the GDP, even though we have high potential we cannot perform well in this area, productivity in rural India is extremely low due to unscientific farming practices that lead to low productivity. Apart from use of scientific technologies in agriculture, information is very essential element in any agricultural activity. It is only useful if it is handy at right time and accessible to users in suitable or understandable way. Computer technology during the last decade has numerous changes in many fields. But today, the popular devices such as laptops, tablets and smart phone are owned and accessed by all ages. The technologies like satellite navigation, sensor network, grid computing, context-aware computing and the ubiquitous computing are supporting the domain for improved monitoring and decision making capabilities. Agriculture is also reaping the benefits of technological innovation which helps in quantitative and qualitative food production. Ubiquitous computing in agriculture is emerging remarkably in this fast processing pervasive environment, owing to wireless sensor network. Some of the technologies included advanced irrigation systems, pesticides, synthetic nitrogen fertilizer, and improved crop varieties that were modified to become hybrid species so farmers could produce higher yields. The agricultural world has evolved to rely so much on seed sellers, weed-killer sellers, fertilizer sellers, and local tractor dealers. Agriculture is at the heart of the social development of India as it provides livelihood to majority of the population. Information and Communication Tools should be utilized for accelerating the growth of agricultural sector which will in turn boost the economic growth of the country. e.g. in the farming, Demand is driving the adoption of technologies; various technologies are increasingly being developed in global market and applied at the farm level. Geospatial 118

3 information is such technology which lends to better decisions, which helps to have higher productivity in agriculture. More than sixty percentages of data is geographical and much of the data can be georeferenced, which indicates the importance of geospatial data. The need for spatial data is very important for both the government organizations and society. The availability of reliable and timely geospatial information is the prerequisites of sustainable agricultural development and food security. The adoption of geo-technologies can eradicate the farmer s problems and come out with good solutions. 2. Related works Nowadays public and private companies are ever more relying on BI and decision support tools to attain better metrics from current systems, that to get more timely decisions, and bring out more transparent results[6]. With the growing food crisis all over the world, food production and supply have become an important task. Information technology (IT) that helps developing countries forecast their production. Currently decision support system greatly relies on data mining [4].Sensors are used to control and collect information that helps in identification of objects and locations [2].Today advanced technologies are used in every field of life and agriculture is one were these technologies are adopted and can be used successfully [1].Geographical Information System and Database Management System plays a major role in agricultural development [3]. Crop diseases can be predicted now and give warning to the farmers to come out from economical losses. The irrational use of chemical pesticides has caused a series of environmental and social issues that are difficult to resolve and avoid such as agricultural costs rising, declining quality, and environmental pollution [4]. The pest control, 119

4 disease control in arable crops is still widely practiced by uniform spraying of the entire field and it is very expensive. An innovative technique for visualisation of data properties and interrelations between variables based on Self-Organizing Maps helps in detecting crop diseases based on neural networks.population pressure, water scarcity, investment in agriculture, modern science and technology and support to research are emerging issues that must be considered for development. Kissan SMS portal System: It send information to farmers by SMS in their language was done through mkisan SMS Portal for farmers to give information/services/advisories to farmers by SMS using Interactive voice recognition System (IVRS) and other mobile applications[11]. E-Choupal: It is an initiative of Indian Tobacco Company Limited (ITC), for agricultural improvements [13]. E-Choupal have direct link with rural farmers through Internet for the improvement of agricultural and aquaculture products. The programme installs computers with Internet access in rural areas of India to offer farmers up-to-date marketing and agricultural information. i-kisan: Nagarjuna Fertilizers and Chemicals Ltd (NFCL) for better farm management practices [12]. Ikisan is an initiative for using information technology for the benefit of agriculture, it involves farmers in its various initiatives, like market development programs and it also conduct various farmer training programs. Digital green: Focuses on training, highlight success stories. It is an initiative of Microsoft research India[14]. It is a not-for-profit international development organization that uses an innovative digital platform for community engagement to improve lives of rural communities across South Asia and Sub-Saharan Africa. Focuses on 120

5 training farmers to make and show short videos where they record their problems, share solutions and highlight success stories 3. Methodology Today the current need in agricultural sector is to refine the policy and step up in full swing. Farmers should be encouraged with necessary support and incentives which helps to do better farming. Data- driven decisions are simple and better way to decide and take appropriate decisions. In order to have high yield, network of extension services are needed that can help the workers, farmers and other stakeholders like research scientists, industrialists and other government agencies can be tied together which can help to understand each other in terms of cultivation area, agricultural promotion schemes, new crop varieties and the current need. To implement this we proposed a Geo-Information System in Figure 1. This framework is on farm and a farmer specific model. Farming Record (FR) for each farmer can be created that consist soil type, PH level, past cultivation details, insurance and yield related information. Soil type in a region plays a critical role in determining the fertility status and crop pattern. Providing easy procedures to use data registration (for inputs, costs, and results), early warning and decision support system facilities through computers can leads to good farm management. Farmers update the crop details whenever they cultivate a crop, by accumulating these data helps them to have a database that helps in decision making process. The power of farming data is extraordinary, If the farmer is aware of his farming record for some 10 to 15 years like the month of cultivation, crop type, yield, pesticides used, soil fertility problem, investment s and profit. Tracking data on a new level would be revolutionary. Knowledge base will be created by integrating data 121

6 from various sources the data absorbed from various systems are evaluated in real time Algorithms are able to detect patterns, trends, and correlations over various time horizons in the data and can help in detecting anomalies in the form of large deviations from the expected trends or relations in the data. Biggest power of data analytics is the predictive capability that can help to determine reliable patterns and forecast what might happen in the future. Message alerts can be send to mobiles about fertilizers and pest attacks.decision will be of farm and farmer specific,decision making are done based on plantation,yield data,weather data, fertilizer applications and soil nutrient density data.farmers can get most up-to-date farming and propagation techniques, pest control knowledge, and can also track the whole process from production, distribution to consumption. They can also leverage the systematic methods of information collection, supply chain logistics, market forecasting and business decisionmaking Informatio n to Farmers Data Visualizatio n Search Information to stakeholders GEO-INTELLIGENCE SYSTEM Data analysis & data mining Knowledge Base (Farmers Records, Soil and Crop details Government Databases Data Collected from farmers Other Data Sources 122

7 Figure 1. Geo- Intelligence System Framework This Geo-Intelligence system consist of (i) Government databases: Includes the data collected from agriculture department and crop related information like new crop varieties from agriculture universities and other climatic information and rain fall data. (ii) Data from farmers: Personal information, soil data like soil Type, soil PH, chemical Composition, Plant and Pest diseases related data and previous yield records etc., (iii) Other data sources: Experts and social media. (iv) Knowledge Base: Integrating the data from different sources. (v) Data Analysis & Data Mining: Farmers can get most up-to-date farming, pest control knowledge, and can also track the whole process from production. They can help in, market forecasting and business decision-making. 4. Conclusion This paper has reviewed data management related to farming practices.information services place a major role in the economic and social development. It is clear that the increasing heterogeneity of networked data sources will increase data that can be applied in future. This proposed framework can play crucial roles in agriculture in managing data that are associated with. I want to conclude that the proposed framework can help to have thorough understanding of data and analyze spatial data more efficiently and effectively that facilitate to better decisions. The knowledge source created is a starting point for the development of big data. Agriculture productivity can be enhanced due to digitalization. 123

8 References [1]. Aqeel-ur-Rehman, Z.A. Shaikh, Smart agriculture, Application of Modern High Performance Networks, Bentham Science Publishers Ltd, (2009), pp [2]. N. Schilit, M.M. Theimer, Disseminating active map information to mobile hosts, IEEE Networks 8 (5) (1994) 22 [3]. A. Suprem, N. Mahalik, and K. Kim, A review on application of technology systems, standards and interfaces for agriculture and food sector, Computer Standards & Interfaces, vol. 35, (2013), pp [4]. Juhua Luo, Wenjiang Huang, Jihua Wang And Chaoling Wei,The Crop Disease And Pest Warning And Prediction System, Computer And Computing Technologies In Agriculture II, Volume 2 IFIP Advances In Information And Communication Technology, 2009, Volume 294/2009, PN: , [5]. Miaoguang Jin, Chaochong Jin, 2008 IEEE Symposium on Advanced Management of Information for Globalized Enterprises (AMIGE) Sept [6]. Anu Peisker and Soumya Dalai, 2015, Data Analytics for Rural Development, Indian Journal of Science and Technology, Vol 8(S4), 50-60, February 2015 [7]. Agriculture Policy: Vision 2020, Indian Agricultural Research Institute, New Delhi. [8]. N H Rao,2011,Digital Agriculture and Food Security: Framework for Integrating Agricultural Knowledge Services with Digital India,National Academy of agriculture research Management. 124

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