3,534 research outputs found

    Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels

    Get PDF
    Existing deployments of wireless sensor networks (WSNs) are often conceived as stand-alone monitoring tools. In this paper, we report instead on a deployment where the WSN is a key component of a closed-loop control system for adaptive lighting in operational road tunnels. WSN nodes along the tunnel walls report light readings to a control station, which closes the loop by setting the intensity of lamps to match a legislated curve. The ability to match dynamically the lighting levels to the actual environmental conditions improves the tunnel safety and reduces its power consumption. The use of WSNs in a closed-loop system, combined with the real-world, harsh setting of operational road tunnels, induces tighter requirements on the quality and timeliness of sensed data, as well as on the reliability and lifetime of the network. In this work, we test to what extent mainstream WSN technology meets these challenges, using a dedicated design that however relies on wellestablished techniques. The paper describes the hw/sw architecture we devised by focusing on the WSN component, and analyzes its performance through experiments in a real, operational tunnel

    The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

    No full text
    The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholderโ€™s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

    ๊ฐœ๋ณ„ ์ด์˜จ ๋ฐ ์ž‘๋ฌผ ์ƒ์œก ์„ผ์‹ฑ ๊ธฐ๋ฐ˜์˜ ์ •๋ฐ€ ์ˆ˜๊ฒฝ์žฌ๋ฐฐ ์–‘์•ก ๊ด€๋ฆฌ ์‹œ์Šคํ…œ

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋ฐ”์ด์˜ค์‹œ์Šคํ…œยท์†Œ์žฌํ•™๋ถ€(๋ฐ”์ด์˜ค์‹œ์Šคํ…œ๊ณตํ•™), 2020. 8. ๊น€ํ•™์ง„.In current closed hydroponics, the nutrient solution monitoring and replenishment are conducted based on the electrical conductivity (EC) and pH, and the fertigation is carried out with the constant time without considering the plant status. However, the EC-based management is unable to detect the dynamic changes in the individual nutrient ion concentrations so the ion imbalance occurs during the iterative replenishment, thereby leading to the frequent discard of the nutrient solution. The constant time-based fertigation inevitably induces over- or under-supply of the nutrient solution for the growing plants. The approaches are two of the main causes of decreasing water and nutrient use efficiencies in closed hydroponics. Regarding the issues, the precision nutrient solution management that variably controls the fertigation volume and corrects the deficient nutrient ions individually would allow both improved efficiencies of fertilizer and water use and increased lifespan of the nutrient solution. The objectives of this study were to establish the precision nutrient solution management system that can automatically and variably control the fertigation volume based on the plant-growth information and supply the individual nutrient fertilizers in appropriate amounts to reach the optimal compositions as nutrient solutions for growing plants. To achieve the goal, the sensing technologies for the varying requirements of water and nutrients were investigated and validated. Firstly, an on-the-go monitoring system was constructed to monitor the lettuces grown under the closed hydroponics based on the nutrient film technique for the entire bed. The region of the lettuces was segmented by the excess green (ExG) and Otsu method to obtain the canopy cover (CC). The feasibility of the image processing for assessing the canopy (CC) was validated by comparing the computed CC values with the manually analyzed CC values. From the validation, it was confirmed the image monitoring and processing for the CC measurements were feasible for the lettuces before harvest. Then, a transpiration rate model using the modified Penman-Monteith equation was fitted based on the obtained CC, radiation, air temperature, and relative humidity to estimate the water need of the growing lettuces. Regarding the individual ion concentration measurements, two-point normalization, artificial neural network, and a hybrid signal processing consisting of the two-point normalization and artificial neural network were compared to select an effective method for the ion-selective electrodes (ISEs) application in continuous and autonomous monitoring of ions in hydroponic solutions. The hybrid signal processing showed the most accuracy in sample measurements, but the vulnerability to the sensor malfunction made the two-point normalization method with the most precision would be appropriate for the long-term monitoring of the nutrient solution. In order to determine the optimal injection amounts of the fertilizer salts and water for the given target individual ion concentrations, a decision tree-based dosing algorithm was designed. The feasibility of the dosing algorithm was validated with the stepwise and varying target focusing replenishments. From the results, the ion-specific replenishments formulated the compositions of the nutrient solution successfully according to the given target values. Finally, the proposed sensing and control techniques were integrated to implement the precision nutrient solution management, and the performance was verified by a closed lettuce cultivation test. From the application test, the fertigation volume was reduced by 57.4% and the growth of the lettuces was promoted in comparison with the constant timer-based fertigation strategy. Furthermore, the system successfully maintained the nutrient balance in the recycled solution during the cultivation with the coefficients of variance of 4.9%, 1.4%, 3.2%, 5.2%, and 14.9%, which were generally less than the EC-based replenishment with the CVs of 6.9%, 4.9%, 23.7%, 8.6%, and 8.3% for the NO3, K, Ca, Mg, and P concentrations, respectively. These results implied the developed precision nutrient solution management system could provide more efficient supply and management of water and nutrients than the conventional methods, thereby allowing more improved water and nutrient use efficiencies and crop productivity.ํ˜„์žฌ์˜ ์ˆœํ™˜์‹ ์ˆ˜๊ฒฝ์žฌ๋ฐฐ ์‹œ์Šคํ…œ์—์„œ ์–‘์•ก์˜ ๋ถ„์„๊ณผ ๋ณด์ถฉ์€ ์ „๊ธฐ์ „๋„๋„ (EC, electrical conductivity) ๋ฐ pH๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ์ˆ˜ํ–‰๋˜๊ณ  ์žˆ์œผ๋ฉฐ, ์–‘์•ก์˜ ๊ณต๊ธ‰์€ ์ž‘๋ฌผ์˜ ์ƒ์œก ์ƒํƒœ์— ๋Œ€ํ•œ ๊ณ ๋ ค ์—†์ด ํ•ญ์ƒ ์ผ์ •ํ•œ ์‹œ๊ฐ„ ๋™์•ˆ ํŽŒํ”„๊ฐ€ ๋™์ž‘ํ•˜์—ฌ ๊ณต๊ธ‰๋˜๋Š” ํ˜•ํƒœ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ EC ๊ธฐ๋ฐ˜์˜ ์–‘์•ก ๊ด€๋ฆฌ๋Š” ๊ฐœ๋ณ„ ์ด์˜จ ๋†๋„์˜ ๋™์ ์ธ ๋ณ€ํ™”๋ฅผ ๊ฐ์ง€ํ•  ์ˆ˜ ์—†์–ด ๋ฐ˜๋ณต๋˜๋Š” ๋ณด์ถฉ ์ค‘ ๋ถˆ๊ท ํ˜•์ด ๋ฐœ์ƒํ•˜๊ฒŒ ๋˜์–ด ์–‘์•ก์˜ ํ๊ธฐ๋ฅผ ์•ผ๊ธฐํ•˜๋ฉฐ, ๊ณ ์ •๋œ ์‹œ๊ฐ„ ๋™์•ˆ์˜ ์–‘์•ก ๊ณต๊ธ‰์€ ์ž‘๋ฌผ์— ๋Œ€ํ•ด ๊ณผ์ž‰ ๋˜๋Š” ๋ถˆ์ถฉ๋ถ„ํ•œ ๋ฌผ ๊ณต๊ธ‰์œผ๋กœ ์ด์–ด์ ธ ๋ฌผ ์‚ฌ์šฉ ํšจ์œจ์˜ ์ €ํ•˜๋ฅผ ์ผ์œผํ‚จ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋“ค์— ๋Œ€ํ•ด, ๊ฐœ๋ณ„ ์ด์˜จ ๋†๋„์— ๋Œ€ํ•ด ๋ถ€์กฑํ•œ ์„ฑ๋ถ„๋งŒ์„ ์„ ํƒ์ ์œผ๋กœ ๋ณด์ถฉํ•˜๊ณ , ์ž‘๋ฌผ์˜ ์ƒ์œก ์ •๋„์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ํ•„์š”ํ•œ ์ˆ˜์ค€์— ๋งž๊ฒŒ ์–‘์•ก์„ ๊ณต๊ธ‰ํ•˜๋Š” ์ •๋ฐ€ ๋†์—…์— ๊ธฐ๋ฐ˜ํ•œ ์–‘์•ก ๊ด€๋ฆฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๋ฉด ๋ฌผ๊ณผ ๋น„๋ฃŒ ์‚ฌ์šฉ ํšจ์œจ์˜ ํ–ฅ์ƒ๊ณผ ์–‘์•ก์˜ ์žฌ์‚ฌ์šฉ ๊ธฐ๊ฐ„ ์ฆ์ง„์„ ๊ธฐ๋Œ€ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ์ž๋™์œผ๋กœ, ๊ทธ๋ฆฌ๊ณ  ๊ฐ€๋ณ€์ ์œผ๋กœ ์ž‘๋ฌผ ์ƒ์œก ์ •๋ณด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์–‘์•ก ๊ณต๊ธ‰๋Ÿ‰์„ ์ œ์–ดํ•˜๊ณ , ์ž‘๋ฌผ ์ƒ์žฅ์— ์ ํ•ฉํ•œ ์กฐ์„ฑ์— ๋งž๊ฒŒ ํ˜„์žฌ ์–‘์•ก์˜ ์ด์˜จ ๋†๋„ ์„ผ์‹ฑ์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์ ์ ˆํ•œ ์ˆ˜์ค€๋งŒํผ์˜ ๋ฌผ๊ณผ ๊ฐœ๋ณ„ ์–‘๋ถ„ ๋น„๋ฃŒ๋ฅผ ๋ณด์ถฉํ•  ์ˆ˜ ์žˆ๋Š” ์ •๋ฐ€ ์ˆ˜๊ฒฝ์žฌ๋ฐฐ ์–‘์•ก ๊ด€๋ฆฌ ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ํ•ด๋‹น ๋ชฉํ‘œ๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•ด, ๋ณ€์ดํ•˜๋Š” ๋ฌผ๊ณผ ์–‘๋ถ„ ์š”๊ตฌ๋Ÿ‰์„ ์ธก์ •ํ•  ์ˆ˜ ์žˆ๋Š” ๋ชจ๋‹ˆํ„ฐ๋ง ๊ธฐ์ˆ ๋“ค์„ ๋ถ„์„ํ•˜๊ณ  ๊ฐ ๋ชจ๋‹ˆํ„ฐ๋ง ๊ธฐ์ˆ ๋“ค์— ๋Œ€ํ•œ ๊ฒ€์ฆ์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋จผ์ €, ์ž‘๋ฌผ์˜ ๋ฌผ ์š”๊ตฌ๋Ÿ‰์„ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ด€์ธกํ•  ์ˆ˜ ์žˆ๋Š” ์˜์ƒ ๊ธฐ๋ฐ˜ ์ธก์ • ๊ธฐ์ˆ ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์˜์ƒ ๊ธฐ๋ฐ˜ ๋ถ„์„ ํ™œ์šฉ์„ ์œ„ํ•ด ๋ฐ•๋ง‰๊ฒฝ ๊ธฐ๋ฐ˜์˜ ์ˆœํ™˜์‹ ์ˆ˜๊ฒฝ์žฌ๋ฐฐ ํ™˜๊ฒฝ์—์„œ ์ž๋ผ๋Š” ์ƒ์ถ”์˜ ์ด๋ฏธ์ง€๋“ค์„ ์ „์ฒด ๋ฒ ๋“œ์— ๋Œ€ํ•ด ์ˆ˜์ง‘ํ•  ์ˆ˜ ์žˆ๋Š” ์˜์ƒ ๋ชจ๋‹ˆํ„ฐ๋ง ์‹œ์Šคํ…œ์„ ๊ตฌ์„ฑํ•˜์˜€๊ณ , ์ˆ˜์ง‘ํ•œ ์˜์ƒ ์ค‘ ์ƒ์ถ” ๋ถ€๋ถ„๋งŒ์„ excess green (ExG)๊ณผ Otsu ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ๋ถ„๋ฆฌํ•˜์—ฌ ํˆฌ์˜์ž‘๋ฌผ๋ฉด์  (CC, canopy cover)์„ ํš๋“ํ•˜์˜€๋‹ค. ์˜์ƒ ์ฒ˜๋ฆฌ ๊ธฐ์ˆ ์˜ ์ ์šฉ์„ฑ ํ‰๊ฐ€๋ฅผ ์œ„ํ•ด ์ง์ ‘ ๋ถ„์„ํ•œ ํˆฌ์˜์ž‘๋ฌผ๋ฉด์  ๊ฐ’๊ณผ ์ด๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋น„๊ต ๊ฒ€์ฆ ๊ฒฐ๊ณผ์—์„œ ํˆฌ์˜์ž‘๋ฌผ๋ฉด์  ์ธก์ •์„ ์œ„ํ•œ ์˜์ƒ ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„์ด ์ˆ˜ํ™• ์ „๊นŒ์ง€์˜ ์ƒ์ถ”์— ๋Œ€ํ•ด ์ ์šฉ ๊ฐ€๋Šฅํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ดํ›„ ์ˆ˜์ง‘ํ•œ ํˆฌ์˜์ž‘๋ฌผ๋ฉด์ ๊ณผ ๊ธฐ์˜จ, ์ƒ๋Œ€์Šต๋„, ์ผ์‚ฌ๋Ÿ‰์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ƒ์œก ์ค‘์ธ ์ƒ์ถ”๋“ค์ด ์š”๊ตฌํ•˜๋Š” ๋ฌผ์˜ ์–‘์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด Penman-Monteith ๋ฐฉ์ •์‹ ๊ธฐ๋ฐ˜์˜ ์ฆ์‚ฐ๋Ÿ‰ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ตฌ์„ฑํ•˜์˜€์œผ๋ฉฐ ์‹ค์ œ ์ฆ์‚ฐ๋Ÿ‰๊ณผ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ๋†’์€ ์ผ์น˜๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ฐœ๋ณ„ ์ด์˜จ ๋†๋„ ์ธก์ •๊ณผ ๊ด€๋ จํ•˜์—ฌ์„œ๋Š”, ์ด์˜จ์„ ํƒ์„ฑ์ „๊ทน (ISE, ion-selective electrode)๋ฅผ ์ด์šฉํ•œ ์ˆ˜๊ฒฝ์žฌ๋ฐฐ ์–‘์•ก ๋‚ด ์ด์˜จ์˜ ์—ฐ์†์ ์ด๊ณ  ์ž์œจ์ ์ธ ๋ชจ๋‹ˆํ„ฐ๋ง ์ˆ˜ํ–‰์„ ์œ„ํ•ด 2์  ์ •๊ทœํ™”, ์ธ๊ณต์‹ ๊ฒฝ๋ง, ๊ทธ๋ฆฌ๊ณ  ์ด ๋‘˜์„ ๋ณตํ•ฉ์ ์œผ๋กœ ๊ตฌ์„ฑํ•œ ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ ๊ธฐ๋ฒ•์˜ ์„ฑ๋Šฅ์„ ๋น„๊ตํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ํ•˜์ด๋ธŒ๋ฆฌ๋“œ ์‹ ํ˜ธ ์ฒ˜๋ฆฌ ๋ฐฉ์‹์ด ๊ฐ€์žฅ ๋†’์€ ์ •ํ™•์„ฑ์„ ๋ณด์˜€์œผ๋‚˜, ์„ผ์„œ ๊ณ ์žฅ์— ์ทจ์•ฝํ•œ ์‹ ๊ฒฝ๋ง ๊ตฌ์กฐ๋กœ ์ธํ•ด ์žฅ๊ธฐ๊ฐ„ ๋ชจ๋‹ˆํ„ฐ๋ง ์•ˆ์ •์„ฑ์— ์žˆ์–ด์„œ๋Š” ๊ฐ€์žฅ ๋†’์€ ์ •๋ฐ€๋„๋ฅผ ๊ฐ€์ง„ 2์  ์ •๊ทœํ™” ๋ฐฉ์‹์„ ์„ผ์„œ ์–ด๋ ˆ์ด์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ ํ•ฉํ•  ๊ฒƒ์œผ๋กœ ํŒ๋‹จํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ฃผ์–ด์ง„ ๊ฐœ๋ณ„ ์ด์˜จ ๋†๋„ ๋ชฉํ‘œ๊ฐ’์— ๋งž๋Š” ๋น„๋ฃŒ ์—ผ ๋ฐ ๋ฌผ์˜ ์ตœ์  ์ฃผ์ž…๋Ÿ‰์„ ๊ฒฐ์ •ํ•˜๊ธฐ ์œ„ํ•ด ์˜์‚ฌ๊ฒฐ์ •ํŠธ๋ฆฌ ๊ตฌ์กฐ์˜ ๋น„๋ฃŒ ํˆฌ์ž… ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ œ์‹œํ•œ ๋น„๋ฃŒ ํˆฌ์ž… ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ํšจ๊ณผ์— ๋Œ€ํ•ด์„œ๋Š” ์ˆœ์ฐจ์ ์ธ ๋ชฉํ‘œ์— ๋Œ€ํ•œ ๋ณด์ถฉ ๋ฐ ํŠน์ • ์„ฑ๋ถ„์— ๋Œ€ํ•ด ์ง‘์ค‘์ ์ธ ๋ณ€ํ™”๋ฅผ ๋ถ€์—ฌํ•œ ๋ณด์ถฉ ์ˆ˜ํ–‰ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆํ•˜์˜€์œผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ œ์‹œํ•œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ์ฃผ์–ด์ง„ ๋ชฉํ‘œ๊ฐ’๋“ค์— ๋”ฐ๋ผ ์„ฑ๊ณต์ ์œผ๋กœ ์–‘์•ก์„ ์กฐ์„ฑํ•˜์˜€์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ œ์‹œ๋˜์—ˆ๋˜ ์„ผ์‹ฑ ๋ฐ ์ œ์–ด ๊ธฐ์ˆ ๋“ค์„ ํ†ตํ•ฉํ•˜์—ฌ NFT ๊ธฐ๋ฐ˜์˜ ์ˆœํ™˜์‹ ์ˆ˜๊ฒฝ์žฌ๋ฐฐ ๋ฐฐ๋“œ์— ์ƒ์ถ” ์žฌ๋ฐฐ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ์‹ค์ฆํ•˜์˜€๋‹ค. ์‹ค์ฆ ์‹คํ—˜์—์„œ, ์ข…๋ž˜์˜ ๊ณ ์ • ์‹œ๊ฐ„ ์–‘์•ก ๊ณต๊ธ‰ ๋Œ€๋น„ 57.4%์˜ ์–‘์•ก ๊ณต๊ธ‰๋Ÿ‰ ๊ฐ์†Œ์™€ ์ƒ์ถ” ์ƒ์œก์˜ ์ด‰์ง„์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋™์‹œ์—, ๊ฐœ๋ฐœ ์‹œ์Šคํ…œ์€ NO3, K, Ca, Mg, ๊ทธ๋ฆฌ๊ณ  P์— ๋Œ€ํ•ด ๊ฐ๊ฐ 4.9%, 1.4%, 3.2%, 5.2%, ๊ทธ๋ฆฌ๊ณ  14.9% ์ˆ˜์ค€์˜ ๋ณ€๋™๊ณ„์ˆ˜ ์ˆ˜์ค€์„ ๋ณด์—ฌ EC๊ธฐ๋ฐ˜ ๋ณด์ถฉ ๋ฐฉ์‹์—์„œ ๋‚˜ํƒ€๋‚œ ๋ณ€๋™๊ณ„์ˆ˜ 6.9%, 4.9%, 23.7%, 8.6%, ๊ทธ๋ฆฌ๊ณ  8.3%๋ณด๋‹ค ๋Œ€์ฒด์ ์œผ๋กœ ์šฐ์ˆ˜ํ•œ ์ด์˜จ ๊ท ํ˜• ์œ ์ง€ ์„ฑ๋Šฅ์„ ๋ณด์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ๊ฐœ๋ฐœ ์ •๋ฐ€ ๊ด€๋น„ ์‹œ์Šคํ…œ์ด ๊ธฐ์กด๋ณด๋‹ค ํšจ์œจ์ ์ธ ์–‘์•ก์˜ ๊ณต๊ธ‰๊ณผ ๊ด€๋ฆฌ๋ฅผ ํ†ตํ•ด ์–‘์•ก ์ด์šฉ ํšจ์œจ์„ฑ๊ณผ ์ƒ์‚ฐ์„ฑ์˜ ์ฆ์ง„์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋˜์—ˆ๋‹ค.CHAPTER 1. INTRODUCTION 1 BACKGROUND 1 Nutrient Imbalance 2 Fertigation Scheduling 3 OBJECTIVES 7 ORGANIZATION OF THE DISSERTATION 8 CHAPTER 2. LITERATURE REVIEW 10 VARIABILITY OF NUTRIENT SOLUTIONS IN HYDROPONICS 10 LIMITATIONS OF CURRENT NUTRIENT SOLUTION MANAGEMENT IN CLOSED HYDROPONIC SYSTEM 11 ION-SPECIFIC NUTRIENT MONITORING AND MANAGEMENT IN CLOSED HYDROPONICS 13 REMOTE SENSING TECHNIQUES FOR PLANT MONITORING 17 FERTIGATION CONTROL METHODS BASED ON REMOTE SENSING 19 CHAPTER 3. ON-THE-GO CROP MONITORING SYSTEM FOR ESTIMATION OF THE CROP WATER NEED 21 ABSTRACT 21 INTRODUCTION 21 MATERIALS AND METHODS 23 Hydroponic Growth Chamber 23 Construction of an On-the-go Crop Monitoring System 25 Image Processing for Canopy Cover Estimation 29 Evaluation of the CC Calculation Performance 32 Estimation Model for Transpiration Rate 32 Determination of the Parameters of the Transpiration Rate Model 33 RESULTS AND DISCUSSION 35 Performance of the CC Measurement by the Image Monitoring System 35 Plant Growth Monitoring in Closed Hydroponics 39 Evaluation of the Crop Water Need Estimation 42 CONCLUSIONS 46 CHAPTER 4. HYBRID SIGNAL-PROCESSING METHOD BASED ON NEURAL NETWORK FOR PREDICTION OF NO3, K, CA, AND MG IONS IN HYDROPONIC SOLUTIONS USING AN ARRAY OF ION-SELECTIVE ELECTRODES 48 ABSTRACT 48 INTRODUCTION 49 MATERIALS AND METHODS 52 Preparation of the Sensor Array 52 Construction and Evaluation of Data-Processing Methods 53 Preparation of Samples 57 Procedure of Sample Measurements 59 RESULTS AND DISCUSSION 63 Determination of the Artificial Neural Network (ANN) Structure 63 Evaluation of the Processing Methods in Training Samples 64 Application of the Processing Methods in Real Hydroponic Samples 67 CONCLUSIONS 72 CHAPTER 5. DECISION TREE-BASED ION-SPECIFIC NUTRIENT MANAGEMENT ALGORITHM FOR CLOSED HYDROPONICS 74 ABSTRACT 74 INTRODUCTION 75 MATERIALS AND METHODS 77 Decision Tree-based Dosing Algorithm 77 Development of an Ion-Specific Nutrient Management System 82 Implementation of Ion-Specific Nutrient Management with Closed-Loop Control 87 System Validation Tests 89 RESULTS AND DISCUSSION 91 Five-stepwise Replenishment Test 91 Replenishment Test Focused on The Ca 97 CONCLUSIONS 99 CHAPTER 6. ION-SPECIFIC AND CROP GROWTH SENSING BASED NUTRIENT SOLUTION MANAGEMENT SYSTEM FOR CLOSED HYDROPONICS 101 ABSTRACT 101 INTRODUCTION 102 MATERIALS AND METHODS 103 System Integration 103 Implementation of the Precision Nutrient Solution Management System 106 Application of the Precision Nutrient Solution Management System to Closed Lettuce Soilless Cultivation 112 RESULTS AND DISCUSSION 113 Evaluation of the Plant Growth-based Fertigation in the Closed Lettuce Cultivation 113 Evaluation of the Ion-Specific Management in the Closed Lettuce Cultivation 118 CONCLUSIONS 128 CHAPTER 7. CONCLUSIONS 130 CONCLUSIONS OF THE STUDY 130 SUGGESTIONS FOR FUTURE STUDY 134 LIST OF REFERENCES 136 APPENDIX 146 A1. Python Code for Controlling the Image Monitoring and CC Calculation 146 A2. Ion Concentrations of the Solutions used in Chapter 4 (Unit: mgโˆ™Lโˆ’1) 149 A3. Block Diagrams of the LabVIEW Program used in Chapter 4 150 A4. Ion Concentrations of the Solutions used in Chapters 5 and 6 (Unit: mgโˆ™Lโˆ’1) 154 A5. Block Diagrams of the LabVIEW Program used in the Chapters 5 and 6 155 ABSTRACT IN KOREAN 160Docto

    Wireless Sensor/Actuator Networks in Precision Agriculture: Recent Trends and Future Directions

    Get PDF
    Agricultural production and water has critical importance for socio-economic development of the societies. Unfortunately, the underground water level is slowly falling down and forests are being cut which reduces the rainfall as well. Technological advances on sensor technology and wireless communication are leading to the appearance of wireless sensor/actuator networks (WSANs) in a variety of commercial, industrial and military applications. There is no doubt merging wireless sensor technology into agricultural facilities will make farming activities much easier. In this paper, we look at the role of WSANs in agricultural production. We also investigate the communication architecture of WSAN based large scale irrigation management system and explain the key issues that are faced in the system design. Thanks to the easy installation and maintenance of WSANs, lots of water can be saved by giving timely feedback from field to improve the agricultural irrigation efficiency. This kind of solution can greatly help farmers to monitor the amount of water applied to a fiel

    Human-in-the-Loop Model Predictive Control of an Irrigation Canal

    Get PDF
    Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].Junta de Andalucรญa P11-TEP-812

    Intelligent control of agricultural irrigation system based on wireless sensor and actuator networks

    Get PDF
    Optimizing water usage is the primary objective of intelligent and eco-friendly agricultural irrigation systems. In irrigation systems, the flow and pressure of water is usually regulated by controlling the position of the valve. The proportioning electronic actuator accepts a signal from the control system and moves the valve to allow the valve to partially open or close. Varying speed of pump motor can also control the usage of water. The integration of wireless sensor and actuator networks (WSANs) into irrigation management promises to overcome the excessive watering problem while providing additional functionality. This paper presents a case study on the use of WSAN for irrigation activities and investigates the application of fuzzy logic based valve aperture control. The results show that the proposed strategy can be effective in water flow control

    Advances in irrigation management in greenhouse cultivation

    Get PDF
    The advantages of greenhouse include the ability to secure better conditions than outdoor environment for crop growth and development, increased off-season production and autonomy from external weather conditions. This chapter provides an up-to-date critical overview of scientific advances in irrigation management for greenhouse vegetables and ornamentals. The chapter presents a technical design of a typical greenhouse irrigation system, before covering water balance and crop evapotranspiration techniques as well as the use of high-tech moisture sensors for irrigation scheduling. In the context of enhancing the water use efficiency of greenhouse crops, the chapter also discusses innovative management practices such as biostimulants and grafting. Finally, the chapter concludes by looking ahead to future prospects and research breakthroughs

    Review of intelligent sprinkler irrigation technologies for remote autonomous system

    Get PDF
    Changing of environmental conditions and shortage of water demands a system that can manage irrigation efficiently. Autonomous irrigation systems are developed to optimize water use for agricultural crops. In dry areas or in case of inadequate rainfall, irrigation becomes difficult. So, it needs to be automated for proper yield and handled remotely for farmer safety. The aim of this study is to review the needs of soil moisture sensors in irrigation, sensor technology and their applications in irrigation scheduling and, discussing prospects. The review further discusses the literature of sensors remotely communicating with self-propelled sprinkler irrigation systems, distributed wireless sensor networks, sensors and integrated data management schemes and autonomous sprinkler control options. On board and field-distributed sensors can collect data necessary for real-time irrigation management decisions and transmit the information directly or through wireless networks to the main control panel or base computer. Communication systems such as cell phones, satellite radios, and internet-based systems are also available allowing the operator to query the main control panel or base computer from any location at any time. Selection of the communication system for remote access depends on local and regional topography and cost. Traditional irrigation systems may provide unnecessary irrigation to one part of a field while leading to a lack of irrigation in other parts. New sensors or remotely sensing capabilities are required to collect real time data for crop growth status and other parameters pertaining to weather, crop and soil to support intelligent and efficient irrigation management systems for agricultural processes. Further development of wireless sensor applications in agriculture is also necessary for increasing efficiency, productivity and profitability of farming operations

    The Seedling Sanctuary: Automated Cold Frame for Gardner Elementary

    Get PDF
    The purpose of this report is to provide the details of the Seedling Sanctuary, a mechanical engineering senior design project. The project in question is an automated cold frame designed specifically for Gardner Academy, a local elementary school in San Jose. A cold frame is a miniature greenhouse that opens like a chest and is made from clear plastic. Automated ventilation and watering systems create a microclimate within this greenhouse structure to create the ideal growing conditions for seeds. The main purposes of the cold frame are to lengthen the growing season, be maintenance free, and enhance garden education. From testing, the project goals were verified to have been achieved through several performance metrics. First, the systemโ€™s ability to lengthen the growing season is dependent on germinating seedlings that can be planted earlier in the season. The automated system maintained the seedlings at the proper soil moisture levels to grow. The system also implemented passive temperature control systems to maintain the plants in ideal conditions. With the ventilation and thermal mass, the system is able to be cooler at the hottest times of day and warmer at night than unprotected plants. The system has also successfully automated the care of the seedlings, achieving our goal of being maintenance free. Finally, the enhancement of garden education was incorporated through community engagement with the design and building of the cold frame, as well as the Bluetooth application which will be used in the school curriculum
    • โ€ฆ
    corecore