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    ๋ฌด์„  ํ†ต์‹  ๊ธฐ๋ฐ˜์˜ ์Šค๋งˆํŠธ ๊ด€๊ฐœ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„๊ณตํ•™๋ถ€, 2020. 8. ์•ˆ์„ฑํ›ˆ.๋†์—…์€ ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ๋“ค์˜ ๊ฒฝ์ œ์  ์ค‘์ถ”์ž„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ๋Œ€๋ถ€๋ถ„์˜ ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์—์„œ๋Š” ์ž๋™ํ™”๋œ ์žฅ๋น„๋‚˜ ๋ฐ์ดํ„ฐ ๋ชจ๋‹ˆํ„ฐ๋ง ๋“ฑ์˜ ์ง€๋Šฅํ˜• ์‹œ์Šคํ…œ์ด ๊ฑฐ์˜ ์ ์šฉ๋˜์ง€ ๋ชปํ•œ ์ƒํƒœ์—์„œ ์ธ๋ ฅ์— ์˜ํ•ด ๋†์—…์˜ ๋ชจ๋“  ๊ณผ์ •์„ ์ˆ˜ํ–‰ํ•˜๊ณ  ์žˆ๋‹ค. ๊ด€๊ฐœ๋Š” ๋†์ž‘๋ฌผ์˜ ์ƒ์‚ฐ์„ฑ์— ๊ฒฐ์ •์  ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ํ•„์ˆ˜์ ์ธ ๋†์—… ๊ณต์ •์ค‘ ํ•˜๋‚˜๋กœ์„œ, ์—ฐ์ค‘ ๊ฐ•์šฐ๋Ÿ‰์˜ ๋ณ€๋™์— ๋Œ€ํ•œ ๋Œ€์‘์„ ์œ„ํ•˜์—ฌ ๋Œ€๋ถ€๋ถ„์˜ ๋†์ดŒ์ง€์—ญ์—๋Š” ๋†์—…์šฉ์ˆ˜ ๊ด€๊ฐœ ์‹œ์Šคํ…œ์˜ ๊ตฌ์ถ•์„ ์œ„ํ•ด ๋…ธ๋ ฅํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ, ์ด๋Ÿฌํ•œ ์ธ๋ ฅ์— ์˜ํ•œ ๋†์—… ๋ฐฉ๋ฒ•์—์„œ์˜ ๊ด€๊ฐœ ์‹œ์Šคํ…œ์€ ์Šค๋งˆํŠธ ์„ผ์„œ๋ฅผ ์ด์šฉํ•œ ๋ชจ๋‹ˆํ„ฐ๋ง ๋ฐ ์ œ์–ด ๋“ฑ์˜ ๊ธฐ์ˆ ์  ์š”์†Œ๊ฐ€ ์ ์šฉ๋˜์ง€ ๋ชปํ•˜์—ฌ ํšจ์œจ์ ์ธ ์ˆ˜์ž์›์˜ ํ™œ์šฉ์ด ์ œํ•œ๋˜๊ณ  ์ด๋กœ ์ธํ•ด ๋†์ž‘๋ฌผ์˜ ์ƒ์‚ฐ์„ฑ ๋˜ํ•œ ๋‚ฎ์€ ์‹ค์ •์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์˜ ๋†์ดŒ ์ง€์—ญ์—์„œ ์ ์šฉ ๊ฐ€๋Šฅํ•œ ๋ฌด์„ ํ†ต์‹ (RF: Radio Frequency) ๊ธฐ๋ฐ˜์˜ ์Šค๋งˆํŠธ ๊ด€๊ฐœ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ ๋ฐ ์š”๊ธˆ ์„ ๋ถˆ ์‹œ์Šคํ…œ์„ ์ œ์•ˆํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํƒ„์ž๋‹ˆ์•„ ์•„๋ฃจ์ƒค(Arusha) ์ง€์—ญ์˜ ์‘๊ตฌ๋ฃจ๋„ํ† (Ngurudoto) ๋งˆ์„์„ ๋Œ€์ƒ์œผ๋กœ ์ˆ˜ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ ์ œ์•ˆํ•˜๋Š” ์‹œ์Šคํ…œ์€ ๊ธฐ์ƒ ๋ฐ์ดํ„ฐ์™€ ํ† ์–‘ ์ˆ˜๋ถ„ ๋ฐ์ดํ„ฐ๋ฅผ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ๋กœ ๋ถ„์„ํ•˜์—ฌ ๋†์—… ์šฉ์ˆ˜์˜ ์†Œ์š”๋ฅผ ๋ชจ๋‹ˆํ„ฐ๋งํ•œ๋‹ค. ํ•˜๋“œ์›จ์–ด ์‹œ์Šคํ…œ์€ ๊ธฐ์ƒ ์ธก์ • ์ปจํŠธ๋กค๋Ÿฌ, ํ† ์–‘ ์ˆ˜๋ถ„ ์„ผ์„œ, ์ˆ˜๋ฅ˜ ์„ผ์„œ, ์†”๋ ˆ๋…ธ์ด๋“œ ๋ฐธ๋ธŒ ๋ฐ ์š”๊ธˆ ์„ ๋ถˆ ์‹œ์Šคํ…œ ๋“ฑ์œผ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์‹œ์Šคํ…œ์˜ ๊ฐ ์„ผ์„œ๋Š” ๋ฌด์„  ํ†ต์‹ ์„ ํ†ตํ•ด ์„œ๋ฒ„๋กœ ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ๋ฅผ ์ „์†กํ•˜๋„๋ก ๊ตฌ์ถ•๋˜์—ˆ๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ๋ฌด์„  ํ†ต์‹  ์‹œ์Šคํ…œ ์•„ํ‚คํ…์ฒ˜๋Š” ์ธํ„ฐ๋„ท์˜ ์šด์šฉ์ด ์ œํ•œ๋˜๋Š” ๋„คํŠธ์›Œํฌ ์˜ค์ง€ ์ง€์—ญ์— ์ ํ•ฉํ•˜๋„๋ก ์„ค๊ณ„๋˜์—ˆ๋‹ค. ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ๋ถ„์„ ๋ฐ ์˜ˆ์ธก์€ ๋ฐ์ดํ„ฐ ๋ถ„์„ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํ†ตํ•ด ์ˆ˜ํ–‰๋˜๋Š”๋ฐ, ์ด๋ฅผ ํ†ตํ•˜์—ฌ ๋†์žฅ์— ์šฉ์ˆ˜๋ฅผ ๊ณต๊ธ‰ํ•  ์‹œ๊ธฐ ๋ฐ ์ˆ˜๋Ÿ‰๊ณผ ํ•จ๊ป˜ ์š”๊ตฌ๋˜๋Š” ์ „๋ ฅ๋Ÿ‰์ด ์ž๋™์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค. ํ•œํŽธ, ์„ ๋ถˆ์‹œ์Šคํ…œ์€ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ฒฐ๊ณผ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์šฉ์ˆ˜ ์‚ฌ์šฉ์ž๊ฐ€ ์šฉ์ˆ˜๋ฅผ ๊ณต๊ธ‰๋ฐ›๊ธฐ ์ „์— ๋น„์šฉ์„ ์šฐ์„  ์ง€๋ถˆํ•˜๋„๋ก ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ๋ณธ ์‹œ์Šคํ…œ์˜ ๋ชจ๋“  ์„ผ์„œ์—์„œ ์ˆ˜์ง‘๋œ ์ •๋ณด๋Š” ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋ง๋˜๋„๋ก ๊ทธ๋ž˜ํ”ฝ ๊ธฐ๋ฐ˜์˜ ์‚ฌ์šฉ์ž ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ๊ฐœ๋ฐœ๋œ ๋ฌด์„  ํ†ต์‹  ๊ธฐ๋ฐ˜ ์Šค๋งˆํŠธ ๊ด€๊ฐœ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์€ ์‚ฌ์šฉ์ž ์ค‘์‹ฌ์˜ ํŽธ์˜์„ฑ๊ณผ ๊ฒฝ์ œ์ ์ธ ๊ด€๊ฐœ ๋ฐ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์„ ์ œ๊ณตํ•˜์—ฌ ๊ฐœ๋ฐœ ๋„์ƒ๊ตญ์˜ ๊ฒฝ์ œ์  ๊ธฐ๋ฐ˜์ธ ๋†์—… ๋ถ„์•ผ์˜ ๋ฐœ์ „์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that influences crop production. The fluctuating amount of rainfall per year has led to the adaption of irrigation systems in most farms. This manual type of farming has proved to yield fair results, however, due to the absence of smart sensors monitoring methods and control, it has failed to be a better type of farming and thus leading to low harvests and draining water sources. In this paper, we introduce an RF (Radio Frequency) based Smart Irrigation Meter System and a water prepayment system in rural areas of Tanzania. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, solenoid valve, and a prepayment system. These sensors send data to the server through wireless RF based communication architecture, which is suitable for areas where the internet is not reliable and, it is interpreted and decisions and predictions are made on the data by our data analysis algorithm. The decisions made are, when to automatically irrigate a farm and the amount of water and the power needed. Then, the user has to pay first before being supplied with water. All these sensors and water usage are monitored in real time and displaying the information on a custom built graphical user interface. The RF-based smart irrigation monitoring system has both economical and social impact on the developing countries' societies by introducing a convenient and affordable means of Irrigation system and autonomous monitoring.Chapter 1. Introduction 1 Chapter 2 Background of the study and Literature review 3 1.1.Purpose of Research 17 Chapter 3. Requirements and System Design 21 3.1. Key Components 21 3.1.1. System Architecture 21 3.1.2. The Smart Irrigation Meter 22 3.1.2. Parts of Smart Irrigation Meter 23 3.1.3. The pre-paid system and the monitoring device 26 3.2. The Monitoring Application and Cloud Server. 27 Chapter 4. Experiment Setup 30 4.1. Testing Location 30 4.2. Hardware & Software Setup 31 Chapter 5 Results and Analysis 36 5.1 Optimization and anomaly detection algorithm 36 5.1.1 Dynamic Regression Model 36 5.1.2 Nave classifier algorithm for anomaly detection. 38 Chapter 6. Conclusion 44 References 46 ์ดˆ ๋ก 49Maste

    IWRM challenges in developing countries: lessons from India and elsewhere

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    Water resource management / Institutional development / Tube wells / Economic aspects / Policy / India

    Survey of Impact of Technology on Effective Implementation of Precision Farming in India

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    The advancements in technology have made its impact on almost every field. India being an agricultural country, proper use of technology can greatly help in improving the standard of living of the farmers. With varying weather conditions, illiteracy of farmers and non-availability of timely assistance, the farmers of this country could not get the best out of their efforts. Precision farming focuses mainly on the aspects that can improve the efficiency based on the data collected from various sources viz. meteorology, sensors, GIS, GPS, etc. The information pertaining to farmland (e.g., soil moisture, soil pH, soil nitrogen) and agro-meteorology (e.g., temperature & humidity, solar radiation, wind speed, atmospheric CO2 concentration, rainfall, climate change and global warming) are used as input parameters to decide the varying requirements of the crop cultivation. Historical farm land data are used as a means to decide on the kind of actions to be taken under a specific scenario. This paper surveys the existing methods of precision farming and highlights the impact of technology in farming. An overview of different technologies used in precision farming around the world and their implications on the yield are discussed. The methods adopted towards managing different types of crops, the varying environmental conditions and the use of realtime data being collected through sensors are also analyzed. Also, the need for dynamic approaches to assist the farmers in taking context specific decisions has been highlighted

    A review of gender and sustainable land management: Implications for research and development

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    Climate Neutral and Resilient Farming Systems

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    This book presents evidence-based research on climate-neutral and resilient farming systems and further to provide innovative and practical solutions for reducing greenhouse gas emissions and mitigating the impact of climate change. Intensive farming systems are a significant source of greenhouse gas emissions, thereby contributing to global warming and the acceleration of climate change. As paddy rice farming is one of the largest contributors, and most environmentally damaging farming systems, this will be a particular focus of the book. The mitigation of greenhouse gas emissions needs to be urgently addressed to achieve the 2 degrees Celsius target adopted by COP21 and the 2015 Paris Agreement, but this is not possible if local and national level innovations are not accompanied by international level cooperation, mutual learning and sharing of knowledge and technologies. This book, therefore, brings together international collaborative research on climate-neutral and resilient farming systems compiled by leading scientists and experts from Europe, Asia and Africa. The chapters present evidence-based research and innovative solutions that can be applied or upscaled in different farming systems and regions across the world. Chapters present models and technologies that can be used for practical implementation at the systemic level and advance state of the art knowledge on carbon neutral farming. Combining theory and practice, this interdisciplinary book provides guidance which can inform and increase cooperation between researchers from various countries on climate-neutral and resilient farming systems. Most importantly, the volume provides recommendations which can be put into practice by those working in the agricultural industry, especially in developing countries, where they are attempting to promote climate-neutral and resilient farming systems. The book will be of great interest to students and academics of sustainable agriculture, food security, climate mitigation and sustainable development, in addition to policymakers and practitioners working in these areas

    The Digitalisation of African Agriculture Report 2018-2019

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    An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvementsย for Africaโ€™s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth andย create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT โ€˜agripreneursโ€™. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

    Enabling sustainable, productive smallholder farming systems through improved land and water management

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    Farmer-led investments in agricultural land and water management (ALWM) are transforming livelihoods and food security across South Asia and sub-Saharan Africa. Potential exists for even greater benefits, for even more beneficiaries. Understanding what factors influence adoption and impact of ALWM interventions can help ensure sustainable, positive effects of future investments. WLE has designed a suite of tools and investment models to support policy makers and development agents to leverage and extend the investments farmers are already making

    CCAFS Rwanda Deep Dive Assessment of Climate-Smart Agriculture (CSA) in the USAID Feed the Future Portfolio in Rwanda

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    As part of a global effort that will inform how Feed the Future tracks CSA across the 19 focus countries (plus aligned) the CCAFS and USAID/BFS team selected five to carry out a deeper analysis of their portfolio. A visit in June 2015 by CCAFS to the Rwanda Mission highlighted the importance of addressing the effects of climate change in the agricultural sector and the current and potential benefits of CSA in Feed the Future. The five-day visit included a number of meetings with USAID Mission staff, Feed the Future implementing partners, Government of Rwanda partners, and other stakeholders, as well as a field trip to one Feed the Future project in the Southern Region. The process also included a review of documentation on the five current projects in the Feed the Future portfolio, shared in advance of the visit by USAID Rwanda staff. This report outlines the key findings of the visit and highlights some ways in which CSA can be further incorporated into the Missionโ€™s future programmin

    Development of Smart Farming Technology on Ginger Plants in Padamulya Ciamis Village, West Java, Indonesia

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    In this paper, we present a comprehensive study aimed at enhancing the cultivation of ginger plants through the integration of smart farming technology. Ginger (Zingiber officinale) is an essential crop in the agriculture-based economy of Indonesia, providing numerous health benefits and culinary applications. However, traditional farming methods often face challenges such as inefficiencies, resource wastage, and unpredictable yields. The research conducted in Padamulya Ciamis Village seeks to address these issues by harnessing the potential of smart farming technology. The study involves the implementation of cutting-edge agricultural tools, including Internet of Things (IoT) devices, sensor networks, and data analytics. By utilizing these advancements, the project aims to optimize the cultivation process, ensure sustainable resource management, and enhance overall productivity. The methodology of the research encompasses a mix of experimental trials, data collection, and analysis. Smart sensors are deployed to monitor critical variables such as soil moisture, temperature, humidity, and light intensity, enabling farmers to gain real-time insights into their ginger fields. The collected data is processed using machine learning algorithms, providing predictive models and personalized recommendations for cultivation practices. The results of this study demonstrate promising advancements in ginger farming practices. By implementing smart farming technology, farmers in Padamulya Ciamis Village experience optimized irrigation schedules, precise nutrient delivery, and timely pest control measures, leading to increased crop yields and improved quality. Furthermore, resource utilization efficiency is enhanced, minimizing water and fertilizer wastage, contributing to the sustainable and eco-friendly management of ginger plantations. Beyond its local implications, this research showcases the potential of smart farming technology as a transformative force in agriculture. The findings serve as a foundation for scaling up similar projects in other regions of Indonesia and beyond, contributing to the nation's agricultural modernization and food security. Finally, the development of smart farming technology on ginger plants in Padamulya Ciamis Village presents a promising pathway towards sustainable and efficient agricultural practices. By combining traditional farming knowledge with cutting-edge technology, this study exemplifies how smart farming can elevate crop cultivation, empower farmers, and foster rural development in Indonesia
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