• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.151-155
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• 2024

This project is designed with the aim to facilitate the farmer or gardener to engage in green house systems and to improve agricultural technology. In order to reduce continuous monitoring of the soil parameters, excess time consumption for the farmers and excessive usage of water, "Smart irrigation and temperature control for a greenhouse system" has been developed. There are two different ways to irrigate the land namely traditional irrigation methods and modern irrigation methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.198-198
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• 2017

Climate change causes unpredictable and erratic climatic patterns which affects crop production in agriculture and threatens public health. To cope with the challenges of climate change, sustainable and sound growth environment for crop production should be secured. Recent attention has been given to the development of smart irrigation system using sensors and wireless network as a solution to achieve water conservation as well as improvement in crop yield and quality with less water and labor. This study developed the smart irrigation technique for farmlands by monitoring the soil moisture contents and real-time climate condition for decision-making support. Central to this design is micro-controller which monitors the farm condition and controls the distribution of water on the farm. In addition, a series of laboratory studies were conducted to determine the optimal irrigation pattern, one time versus plug time. This smart technique allows farmers to reduce water use, improve the efficiency of irrigation systems, produce more yields and better quality of crops, reduce fertilizer and pesticide application, improve crop uniformity, and prevent soil erosion which eventually reduce the nonpoint source pollution discharge into aquatic-environment.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.25-37
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• 2024

As the agricultural land use shifts from paddy to upland, ensuring reservoir water supply stability for upland crop irrigation becomes essential. The objectives of this study were to estimate the irrigation water requirements considering the upland irrigation scenario and to evaluate the reliability of the water supply from the agricultural reservoir using resilience indexes. Two study sites, Sinheung and Hwajeong, were selected, and soybean and red peppers, the most water-intensive crops, were selected as study crops, respectively. For the irrigation scenario, two irrigation methods of traditional scheduling (which irrigates all sites at once) and rotational scheduling (which distributes irrigation by districts), along with the upland conversion rate, were considered. The net irrigation requirement was estimated through a water balance analysis. The stability of the reservoir was evaluated using resilience indexes based on the simulated 10-years reservoir water levels and drought criterion. Overall, the water supply of the reservoir was evaluated as stable during the simulated 10 years, except for the one year. Compared to the two irrigation methods, rotational scheduling resulted in lower irrigation water usage in both sites, with reductions of 1.6%, and 0.3%, respectively. As the upland conversion rate increases, the water deficit could be intensified in Hwajeong with a conversion rate exceeding 50%, showing the number of deficit(ND) over the one and a rapid increase in the deficit ratio(DR). It was confirmed that the reservoir operation criteria can be enhanced by incorporating resilience indicators along with crop growth information, thus, this will be a further study.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.227-234
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• 2018

The smart irrigation system using ICT technology is crucial for stable production of upland crops. The objective of this study was to develop a smart irrigation system that can control soil water, depending on irrigation methods, in order to improve crop production. In surface irrigation, three irrigation methods (sprinkler irrigation (SI), surface drip irrigation (SDI), and fountain irrigation (FI)) were installed on a crop field. The soil water contents were measured at 10, 20, 30, and 40 cm depth, and an automatic irrigation system controls a valve to maintain the soil water content at 10 cm to be 30%. In subsurface drip irrigation (SSDI), the drip lines were installed at a depth of 20 cm. Controlled drainage system (CDS) was managed with two ground water level (30 cm and 60 cm). The seasonal irrigation amounts were 96.4 ton/10a (SDI), 119.5 ton/10a (FI), and 113 ton/10a (SI), respectively. Since SDI system supplied water near the root zone of plants, the water was saved by 23.9% and 17.3%, compared with FI and SI, respectively. In SSDI, the mean soil water content was 38.8%, which was 10.8% higher than the value at the control treatment. In CDS, the water contents were greatly affected by the ground water level; the water contents at the surface zone with 30 cm ground water level was 9.4% higher than the values with 60 cm ground water level. In conclusion, this smart irrigation system can reduce production costs of upland crops.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.146-150
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• 2023

In areas where water is scarce, water management is critical. This has an impact on agriculture, as a significant amount of water is used for that purpose. Electronic measurement equipment are essential for regulating and storing soil data. As a result, research has been conducted to manage water usage in the irrigation process. Many equipment for managing soil fertility systems are extremely expensive, making this type of system unaffordable for small farmers. These soil fertility control systems are simple to implement because to recent improvements in IoT technology. The goal of this project is to develop a new methodology for smart irrigation systems. The parameters required to maintain water amount and quality, soil properties, and weather conditions are determined by this IoT-based Smart irrigation System. The system also assists in sending warning signals to the consumer when an error occurs in determining the percentage of moisture in the soil specified for the crop, as well as an alert message when the fertility of the soil changes, since many workers, particularly in big projects, find it extremely difficult to check the soil on a daily basis and operate agricultural devices such as sprinkler and soil fertilizing devices.

• 한국관개배수회지
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• no.54
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• pp.52-57
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• 2014

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.883-902
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• 2023

Water is critical to the health and productivity of fruit trees. Efficient monitoring of water stress is essential for optimizing irrigation practices and ensuring sustainable fruit production. Short-range sensing can be reliable, rapid, inexpensive, and used for applications based on well-developed and validated algorithms. This paper reviews the recent advancement in fruit tree water stress detection via short-range sensing, which can be used for irrigation scheduling in orchards. Thermal imagery, near-infrared, and shortwave infrared methods are widely used for crop water stress detection. This review also presents research demonstrating the efficacy of short-range sensing in detecting water stress indicators in different fruit tree species. These indicators include changes in leaf temperature, stomatal conductance, chlorophyll content, and canopy reflectance. Short-range sensing enables precision irrigation strategies by utilizing real-time data to customize water applications for individual fruit trees or specific orchard areas. This approach leads to benefits, such as water conservation, optimized resource utilization, and improved fruit quality and yield. Short-range sensing shows great promise for potentially changing water stress monitoring in fruit trees. It could become a useful tool for effective fruit tree water stress management through continued research and development.

• Journal of Appropriate Technology
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• v.6 no.2
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• pp.136-145
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• 2020

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 directly influences crop production. The fluctuating amount of rainfall per year has led to the adoption of irrigation systems in most farms. The absence of smart sensors, monitoring methods and control, has led to low harvests and draining water sources. In this research paper, we introduce a 433 MHz Radio Frequency and 2G based Smart Irrigation Meter System and a water prepayment system for rural areas of Tanzania with no reliable internet coverage. 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, a solenoid valve, and a prepayment system. To achieve high precision in linear and nonlinear regression and to improve classification and prediction, this work cascades a Dynamic Regression Algorithm and Naïve Bayes algorithm.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.