This article describes current status of agriculture system, how digital technology is going to be implemented in the system, ECE concepts applied in the digital agriculture, and the future goals of agriculture society.
Historians say if they need to pick two most important events in the history of human, first event happened around late 17th century until early 19th century, which is the Industrial Revolution. And the second event happened about 12,000 years ago in the human history. It’s called the Neolithic Revolution or Neolithic Demographic Transition, sometimes called the Agricultural Revolution. Both of the Industrial Revolution and the Agricultural Revolution similarly impacted on human life in sense of how they collected or manufactured their needs. There are different theories about human’s basic needs but historically psychologists distinguish the basic needs in two kinds in general. There are social and psychological needs and physiological and biological needs. In spite of human’s social and psychological needs, without a bare minimum satisfaction of physiological needs, humans won’t survive for a long time. And agriculture basically support humans’ physiological needs by the cultivation of animals, plants, fungi, and other life forms for food, and medicinal.
Introduction of Digital Agriculture
The development of international agriculture has experienced several main stages: primitive agriculture stage, traditional agriculture stage and modern agriculture stage. Undertaking some easy work by stoneware is one of the main characters of primitive agriculture. During traditional agriculture stage, Humans invented ironware and began to produce using tools made of iron and wood. The productivity was improved significantly. While during modern agriculture stage, advanced agricultural machines were used and agriculture economy made great progress. “The so-called Digital Agriculture is featured by digitization of agricultural activity and is agriculture driven by digits.” This system can be also described as an integrated agricultural system. The main keys of the integrated agricultural system includes data processing and digital control machinery for digitization, data transmission, data collection, network and automation of agricultural activity (Tang, 2002, p.1).
Basics of Digital Agriculture
Today’s agriculture industry has expanded from traditional production to the sixth industry. In order to move on to the next generation agriculture system from the traditional agriculture society, which is a simple production method, today’s agriculture industry has expanded to the sixth industry which has higher value added as it converges with technologies. The sixth industry means three multiplied layered industry: 1st (production) x 2nd (manufacturing, distribution) x 3rd (experimental tourism). And basically in order this sixth industry to be achievable; the digital agriculture system needs to be well framed and developed. This industry has developed into a higher value-added as it converges with technologies. The convergence of agriculture and information and communication technology (ICT) is a new growth engine increasing efficiency in every process of production, distribution and consumption. This convergence has become more important as a solution to problems which the agriculture sector faces, such as a labor shortage caused by an aging population, unusual weather phenomena, a high cost structure from complicated distribution systems, and rapidly-changing consumer tastes. The characteristic of current agriculture system and the idea of future agriculture system are well described in a research done by Department of Geography at Beijing Normal University and Beijing Key Laboratory: “Undoubtedly, informatization is a basic characteristic of current agriculture, but informatization is realized by the digitalization of information. For this reason, we put forward the conception of Digital Agriculture, and take it as a turning point to normalize and propel the development of international agriculture.” (Tang, 2002, p.1) The research presents a direction for the future agriculture system, digital agriculture.
Necessity and Goal of Digital Agriculture
As it was described earlier, the current agriculture has converged with technologies such as information technology (IT), biotechnology (BT), environment technology (ET), and nano technology (NT). (Hwang, 2002, p.1-2) And it mainly focuses on areas such as cost reduction during production level, reduction in labor burden, high quality and organic production, and quality management in facility. Second, it is important to meet consumers’ needs at the production and distribution stages through building a system, which delivers food safety information. By applying IT, which is a part of digital agriculture, this can be achieved at the production stage with the livestock and controlled growth environment monitoring and control system. This means, IT applications need to be expanded in the agriculture farming automation system. Furthermore, at the distribution and processing stages, advanced distribution technologies using IT need to be introduced including the convergence of distribution data. These are the very small portions of the digital agriculture system that is part of making big database of the whole agriculture system.
Framework of Digital Agriculture
The framework of digital agriculture is composed of the following parts:
Central Databases of Agriculture
The databases have basic information related to agriculture activities such as agricultural land, germplasm resources (what germs are in the soil and the water), climate changes, etc. These databases should also include social background information as well as farmland and farming, so decisions can be made based on the tie between them.
Realtiem Information Collecting System
The best way to keep farm produces safe is obviously prevent what is going to happen based on forecasts that are not just weather but also predictions of plants behavior. But clearly forecasts are not always right and cannot see everything coming. The system responses realtime based on monitoring agricultural activities and updating of databases. This system is made up of digitized information collectors, which are responsible for the collection of meteorology, vegetation and soil information on or under the ground, airborne, or satellite based on sensors, etc (Tang, 2002, p.2-3).
Central Processing System (CPS)
This system is basically main brain like central processing unit (CPU) of the digital agriculture system. Based on the information collected with geographical information system (GIS), CPS analyzes the collected data, makes appropriate decisions, and distributes out control commands to direct the work of digitized agriculture machinery
Digital network transmission system
As a kind of media, digital network transmission system realizes the collection of information and the distribution of commands (Tang, 2002, p.2-3).
Digitized agricultural machinery (DAM)
DAM includes all of digitized harvesting devices such as sowing device, digitized fertilizer control device. Based on the digital network such as global positioning system (GPS) and geographical information system (GIS), DAM executes the commands given by central processing system and returns the real-time data to real-time information collecting system.
As it’s shown in the Figure 1, the parts that share common data interface are connected. Digital agriculture system in real life can be explained with simple example of planting. Based on the data in the basic information databases, digital agriculture system plans a yearlong planting plan, and monitors the growth vigor of crops, and provides all of important information such as water content, and meteorology by information collecting system. And then CPS analyzes the information that are collected in the past and based on that, makes feasible and reasonable decisions. These decisions get applied on digitized agricultural machinery (DAM) under the direction of CPS. DAMs do series of work such as sowing, water and fertilizer controlling, and harvesting. CPS gets the result from DAM and makes overall analysis report. Digital Agriculture stresses the integrative development of each part. Only when all the parts tie closely and develop cooperatively, they can construct Digital Agriculture (Tang, 2002, p.3-4). This emphasizes that without all five parts being integrated together, any individual parts can’t function as digital agriculture system.
Framework of Digital Agriculture
In order to achieve the basis of digital agriculture system, accurate and reliable agriculture information needs to be collected (Xu, 2012, p.1). And agriculture information can be categorized into two parts: one part is spatial location information, which includes the information about latitude, longitude and elevation in sense of 3 dimension spatial location and the second part is the information such as pest monitoring data, nutrition content data, and crop growth and yield.
Current method of collecting information and follow-up process is quite complicated. For instance, people collecting information are required to carry a variety of devices, such as global positioning equipment, cameras, computers, all sorts of sensors, etc. And processes of collecting data and after finishing collecting data are also very complex as well. It is also said in the research by Xu Chen and Jingyin Zhao from Shanghai Academy of Agricultural Sciences: “The data acquisition and processing is too complicated and Error-prone.” And they also says “It is urgently need to have a high degree of integration and wide range portable agricultural information collection system, provides “one-stop” service of data acquisition and processing.” (Xu, 2012, p.1)
There are many ways to approach to solve this problem. Based on the current intelligence level of mobile phones and with the fast growing smart phone technologies, a smart phone can be easily used as a hardware carrier of agricultural information collection system especially with the aspects of built-in GPS positioning module and wireless network ability. There has been some useful practice and research about using mobile phones to collect information such as the U.S. Trimble computer’s AgGPS EZ-map farm solutions, which can achieve map display and simple control of the sampling based on Pocket PC (Huang, 2006, 145-147); China Agricultural University applied Mobile GIS for precision promotes county seed varieties (Zhang, 2008, 109-112); Chinese Academy of Sciences Guangzhou Institute of Geochemistry applied embedded GIS to collect tobacco field information (Tang, 2010, 188-190). However, the applications like mobile phones are designed for specific purposes that have huge deficiencies in terms of flexibility and expansion to customize new hardware features and software program. In the future, mobile phones would need to have more flexibility to expand their hardware features and software programs. Then the data collected by current devices is limited to the literal text. Mobile GIS is a GIS system based on GPRS and use GPS smart phone as the end, is another hot new technology following the desktop GIS and WebGIS (Tang, 2010, 188-190). It integrates GIS function such as collect geographic information, positioning, and analysis capabilities into phone, provide real-time and efficient technical services for users. In the applications of agriculture, Mobile Phone GIS not only can meet needs of managers to survey agricultural land use situation, but also allows the scene suitability match according to the actual growing conditions, soil nutrient conditions and species in the fields, design planting scheme based on the local conditions for the farmers, and can automatically associate measured data of pests and disease to the disease location for further analysis and early warning. The introduction of mobile GIS technology will improve office efficiency and provide farmers for more accurate information services.
Therefore, it is urgent to develop mobile GIS system supports “one-stop” collection of multi-source agricultural information and easy to extend.
It has been proved that image processing is effective tool for analysis in various fields and applications. From the farmers’ point of concern, parameters like yield, canopy, and quality of product were important measurements. In order to analyze the parameters, the expertise were required most of time. And because of the geographical characteristic of farms, it was definitely time consuming and costlier issue. So often time, the process of decision making with expert advice may not be affordable, and complicated. For the most of time, feedback from experts and their services may consume long time. In evolution towards sustainable agriculture system it was clear that important contributions can be made by using emerging technologies. Image processing was one of the tools which can be applied to measure the parameters related to agronomy with accuracy and economy. In image processing, radiation such as Gamma ray, X-ray was important source. Imaging in UV band, visible band, Microwave band are also from source of radiation. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of the parameters. There are a lot of different areas in agriculture that image processing is very useful and effective such as image techniques, weed detection and fruit grading. Compare to tradition methods to analyze the parameters, it has been proved that using image processing for the analysis is more accurate and less time consuming and these applications can improve decision making for vegetation measurement, irrigation, fruit sorting, etc. Dr. Jayaraman and Dr. Roy introduced about remote sensing technique which is one of the key features for image processing that was widely used in “Remote sensing applications: an overview”. Remote sensing was the science of identification of earth surface features and estimation of geo-biophysical properties using electromagnetic radiation (Vibhute, 2012, p.34-40). Multi-source data fusion and Geographic Information System were also introduced with analytical techniques using digital image processing. These applications with image processing can provide relevant data to groundwater that are useful with flood management, and irrigation. In order to estimate direct and independent crop area, remote sensing data and pattern recognition technique was used (Dadhwal, 2002, p.107-122).
Two areas that image processing is useful in agriculture system were introduced earlier. First one is weed detection. Weeds were the plants growing in wrong place in farm which compete with crop for water, light, nutrients and space, causing reduction in yield and effective use of machinery (Vibhute, 2012, p.34-40). Because of these reasons, weed control was crucial in farming. Numerous methods based on image processing can potentially solve part of this problem by creating weed detection techniques using image processing algorithms based on edge detection, and color detection. The second area is fruit/ food grading. In the past decade, expectations in quality and quantity of food and safety standards have increased. This issue has caused need of more and faster accurate grading, sorting of fruits and foods or agriculture products which causes increased processing and labor work. Digital image processing is nondestructive, accurate and reliable method to achieve the needs. Potentially image processing in agriculture can be applied in areas of detection of defects, sorting, cracks and bruises on agricultural products, grading of fresh products, etc.
With the available functionalities based on image processing techniques, it has been proved that image processing is effective method for digital agricultural system.
Achieving digital agriculture realize a smart farming which will help all of production, distribution, consumption, farm society and village to grow as the world’s trend changes with new developing technologies. Also with the tools like weed detection system and fruit grading system with digital image processing can possibly reduce the cost to achieve ecological and economically sustainable agriculture. As the level of environment of all of these factors gets higher, during the entire process of agricultural production, distribution, and consumption, productivity, efficiency, and Quality of agriculture business would get higher added value industry
- Tang, S., Wu, M., Zhou, X., Zhu, X., A Conception of Digital Agriculture, Geoscience and Remote Sensing Symposium, 2002, IGARSS ’02. 2002 IEEE International, 3026-2028 vol.5. DOI: 10.1109/IGARSS.2002.1026858
- Xu, C., Research of Real-time Agriculture Information Collection System Base on Mobile GIS, Agro-Geoinformatics, 2012. DOI: 10.1109/Agro-Geoinformatics.2012.6311703
- Huang, X., Pan, Y., Wang, M., System for field information collection and update based on GSM and GPS, Computer Technology and Development, 2006,16(9): 145-147. DOI: 10.5815/ijisa.2013.09.10
- Zhang, S., Zhao, J., Li, L., Application of GIS to disease and pest management[J], Acta Agriculturae Shanghai, 2008,24(3):109-112
- Tang, L., Web Service-based Agricultural Economy Information Exchange And Sharing System, Computer Application and Software, 2010,27(11):188-190.
- Vibhute, A., Bodhe S., Applications of Image Processing in Agriculture: A Survey, International Journal of Computer Applications, 52(2):34-40, August 2012. DOI: 10.5120/8176-1495
- Dadhwal, V.K., Singh, R.P., Dutta, S., Parihar, J.S., Remote sensing based crop inventory: A review of Indian experience, International Society for Tropical Ecology, 2002,43(1): 107-122.
- Hwang Y., Techno-Economic Paradigm Shift and Evolution of STI Policy in Korea and The United States, 2002,1-23.
- Chengfeng, J., The application and research of pervasive-based e-agriculture system, ICICTA 2010, p 694-7, 2010. DOI: 10.1109/ICICTA.2010.738
- Chengfeng, J., The Ubiquitous E-Agriculture System Design and Implementation, ICIME 2010, p 453-7, 2010. DOI: 10.1109/ICIME.2010.5477766
- Excell, J., Up on the farm: Could Vertical farming offer a solution to one of humanity’s most pressing problems?, Engineer, p 24-27, December 2012.
Search the Handbook:
- Introduction and Acknowledgements
- Senior Capstone Projects Summary for the 2021-22 Academic Year
- Senior Capstone Projects Summary for the 2020-21 Academic Year
- Senior Capstone Projects Summary for the 2019-20 Academic Year
- Senior Capstone Projects Summary for the 2018-19 Academic Year
- Senior Capstone Projects Summary for the 2017-18 Academic Year
- Senior Capstone Projects Summary for the 2016-17 Academic Year
- Senior Capstone Projects Summary for the 2015-16 Academic Year
- Senior Capstone Projects Summary for the 2014-15 Academic Year
- Senior Capstone Projects Summary for the 2013-14 Academic Year
- Senior Capstone Projects Summary for the 2012-13 Academic Year
- 1. Design Process
- 2. Management
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- 4. Communications And Life Skills
- 5. Tech Notes
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