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Paweena Suebsombut1,2, Aicha Sekhari2, Pradorn Sureephong1, and Abdelaziz Bouras3 1College of Arts, Media and Technology, Chiang Mai University, Chiang Mai, Thailand [email protected] 2University Lumiere Lyon 2, Bron, France [email protected] 3Qatar University [email protected] Abstract Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text Key words Precision Agriculture, Data flow, Data chain, Product Lifecycle, Data management Introduction Agriculture or Agricultural Activities includes the tillage of the soil, planting of crops, growing of fruit trees, including the harvesting of any agricultural and horticultural commodities, and other farm activities and practices performed by a farmer on a farm as an incident to or in conjunction with such farming operations, but does not include the manufacturing or processing of farm products (Ben G. Bareja, 2014 Kipps, M.S., 1970). Crop production begins with the sowing of seeds, continues with crop maintenance during growth and development, and ends with crop harvest, storage, and distribution (Madsen, Eugene L., 1995) as shown in Figure 1. Figure 1 Lifecycle of growing crops Previously, farmers cultivate and maintain their crop using indigenous knowledge, natural resources, and cultural beliefs of the farmers. Based on the prediction of the Food and Agriculture Organization (FAO), they predict the world population in the next 40 years that will increase about 1,500 million people (Beecham Research, 2014). Additional, the unpredictable environment, such as climate change, drought, etc., is the major problem for cultivation which affects to crop maintenance process and yields. Therefore, farmers have to more emphasize on the significance to increase the awareness on quality and sufficiency of food for the world population. From these impacts, there is a negative result in the limited number of crops grown. One way to counter the risk from devastating the operation of farming is to apply the concept of Precision Agriculture (PA), which is used in farms to analyse and manage farms by following the farms conditions. The Precision Agronomics or Precision Agriculture (PA) is adopted to enhance crop maintenance process and to improve quality of productivity. According to the concept of Smart Farming from European Network Mainstreaming Smart Farming Technologies among the European farmer community (Smart AKIS Network), PA is one of the three pillars of modern information and communication technologies (ICT) application which is a concept of farm management to observe and measure variance in cultivation using smart technology and decision support system (DDS) for managing whole farm including fertilizing, irrigation, pesticides, etc., and enhancing quality of productivity (Bongiovanni, Rodolfo, et al., 2004). PA techniques use data obtained by closely monitoring related variables using smart technologies like sensors and remote sensing technology (). PA has developed and applied in many countries such as Canada, USA, European, Japan, etc. Adoption of precision agriculture in vineyard is one example of using PA in agriculture that adopts sensors and remote sensing technology and smart technology into farm for monitoring farm environment (temperature, soil humidity, humidity, wind speed and direction, and rainfall) by using sensors nodes via mobile phone which helps farmers to manage farm effectively and to control quality of grapes (Tongrod N, et al., 2009). Another example is proposed by Watthanawisuth N, et al., 2010 which adopts Global Positioning System (GPS) technology for tracking tractor in large farm areas communicating by using ZigBee wireless network so that farmers can track their tractors while it is running on the field. In PA, the data is very significant. Numerous data are relevant for crop maintenance which the management of data chain is necessary. Consequently, this paper studies the methods to manage data for crop maintenance. The paper is structured as follows starting from Data relevance crop maintenance, Methods of data chain management, and ending with the discussion and conclusion. Crop Maintenance Crop management is the process to manage the crop cultivation in whole life cycle of crop production consisting of beginning of life, middle of life and end of life. It is one of the processes on crop production to protect the crop cultivation against issues which affect to yields such as weather, diseases and insect, etc. A variety of cultural treatments also may be required to meet the purpose of the crop cultivation. On the crop maintenance, the factors affecting to crop growth consists of growth regulator and hormone, soil, nutrient, water, climatic, diseases, insect pests, and weeds (Sungcom Techawongstien, 2004). Additional, there are six main methods of crop maintenance (Anucha Chantaraboon, 2010). Firstly, irrigation is one of important methods of crop maintenance that crops need water for growth, dissolving nutrients into the soil, keeping moisture and cool, and carrying nutrients through the crop. Secondly, fertilizing is the method to maintain crop to healthy conditions and to increase their resistance to harm from diseases and pests. Thirdly, pruning is the method for improving structure of crop, controlling size of crop, or removal of diseased, dying, or dead branches. Fourthly, disease control is the method to protect crop by interfering crop as little as possible so that forecasting and avoidance the occurrence of diseases, pests, and weeds are the most reliable way to deal with them. Fifthly, insect pests control is the methods to avoid the insect pest attack to crop including to care crop after attacked. Finally, weeds control is the method to avoid weed occurrence which affect to soil properties, he habitat of poisonous animals like snacks. Crop maintenance is relevant lifecycle of growing crop to care the crop from the beginning to the end of crops life. Generally, the lifecycle of growing crop includes sowing the seeds into soil, young crop, mature crop, flowering, ripening, dormancy, and death (Nikky Tilley, 2015 SpaceHero, 2018 Trevor Hennings, 2018) as illustrated in Figure 2. Some crops like grain, rice, the lifecycle is always starting from sowing the seed and ending with the death of crops. On the other hand, the lifecycle of some crops like horticulture is starting from sowing the seeds at the first time of planting, but it is ending at the dormancy stage, and it will start its next growing with mature crop stage. Figure 2 Lifecycle of growing crops In each stage of crops lifecycle, the process to maintain crop (including irrigation, fertilizing, pruning, diseases control, insect pests control, and weeds control) is different depending on the needs of crop and environment. For example, crop in the flowering stage needs less water than other stages because it helps to stimulate the sprout of flowers. Data Relevance Crop Maintenance Data is information collected using specific methods for a specific purpose of studying or analyzing. In crop maintenance, numerous data are relevant that are classified based on the sources of data comprising on-farm data, off-farm data, and experts knowledge as shown in Table 1. On-farm data is data collected by installing the existing commercial smart sensors and applying smart technologies on farm field such as soil moisture sensor, weather station, Internet of Things (IoTs), etc. Off-farm data is obtained by accessing other open cloud database (open data sources) which are relevant crop maintenance such as weather forecasting, water quality, diseases occurrence, etc. Experts knowledge is knowledge and best practices obtained by interviewing experts such as method for irrigation, method and timing to add fertilizer, etc. Table 1 Classification of crop maintenance data (Putjaika, N., et al., 2016 to Heisel, Torben, et al., 1999 ) Based on these data, from three sources will be collected, processed, and analysed for decision-making system to support the crop maintenance during production cycle as a chain of data. Therefore, the data chain management is significant to manage the flow of data for processing and analyzing. Data Cain Management Approach Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text. Text Concept of data chain (starting from collecting data until analyzing data) Current methods are used to manage data Discussion and Conclusion Discussion Performance of each method to manage data Propose the method that appropriate to agricultural system Conclusion . Text. Text Acknowledgement The authors would like to acknowledge the support of Decision and Information Systems for Production System (DISP), Universit Lumiere Lyon 2 (France), College of Arts Media and Technology, Chiang Mai University (Thailand), and Qatar University (Qatar). We also would like to acknowledge all of our colleagues who worked together and provided encouragement to the authors. References 1 Ben G. Bareja. What is Agriculture, Definition of Agriculture. Cropsreview Welcome and Lets Go Crop Farming, August 2014. 2 Madsen, Eugene L. 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