3,534 research outputs found
Is There Light at the Ends of the Tunnel? Wireless Sensor Networks for Adaptive Lighting in Road Tunnels
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
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 This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions.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 stakeholderโs 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
๊ฐ๋ณ ์ด์จ ๋ฐ ์๋ฌผ ์์ก ์ผ์ฑ ๊ธฐ๋ฐ์ ์ ๋ฐ ์๊ฒฝ์ฌ๋ฐฐ ์์ก ๊ด๋ฆฌ ์์คํ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๋์
์๋ช
๊ณผํ๋ํ ๋ฐ์ด์ค์์คํ
ยท์์ฌํ๋ถ(๋ฐ์ด์ค์์คํ
๊ณตํ), 2020. 8. ๊นํ์ง.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.ํ์ฌ์ ์ํ์ ์๊ฒฝ์ฌ๋ฐฐ ์์คํ
์์ ์์ก์ ๋ถ์๊ณผ ๋ณด์ถฉ์ ์ ๊ธฐ์ ๋๋ (EC, electrical conductivity) ๋ฐ pH๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ํ๋๊ณ ์์ผ๋ฉฐ, ์์ก์ ๊ณต๊ธ์ ์๋ฌผ์ ์์ก ์ํ์ ๋ํ ๊ณ ๋ ค ์์ด ํญ์ ์ผ์ ํ ์๊ฐ ๋์ ํํ๊ฐ ๋์ํ์ฌ ๊ณต๊ธ๋๋ ํํ์ด๋ค. ๊ทธ๋ฌ๋ EC ๊ธฐ๋ฐ์ ์์ก ๊ด๋ฆฌ๋ ๊ฐ๋ณ ์ด์จ ๋๋์ ๋์ ์ธ ๋ณํ๋ฅผ ๊ฐ์งํ ์ ์์ด ๋ฐ๋ณต๋๋ ๋ณด์ถฉ ์ค ๋ถ๊ท ํ์ด ๋ฐ์ํ๊ฒ ๋์ด ์์ก์ ํ๊ธฐ๋ฅผ ์ผ๊ธฐํ๋ฉฐ, ๊ณ ์ ๋ ์๊ฐ ๋์์ ์์ก ๊ณต๊ธ์ ์๋ฌผ์ ๋ํด ๊ณผ์ ๋๋ ๋ถ์ถฉ๋ถํ ๋ฌผ ๊ณต๊ธ์ผ๋ก ์ด์ด์ ธ ๋ฌผ ์ฌ์ฉ ํจ์จ์ ์ ํ๋ฅผ ์ผ์ผํจ๋ค. ์ด๋ฌํ ๋ฌธ์ ๋ค์ ๋ํด, ๊ฐ๋ณ ์ด์จ ๋๋์ ๋ํด ๋ถ์กฑํ ์ฑ๋ถ๋ง์ ์ ํ์ ์ผ๋ก ๋ณด์ถฉํ๊ณ , ์๋ฌผ์ ์์ก ์ ๋์ ๊ธฐ๋ฐํ์ฌ ํ์ํ ์์ค์ ๋ง๊ฒ ์์ก์ ๊ณต๊ธํ๋ ์ ๋ฐ ๋์
์ ๊ธฐ๋ฐํ ์์ก ๊ด๋ฆฌ๋ฅผ ์ํํ๋ฉด ๋ฌผ๊ณผ ๋น๋ฃ ์ฌ์ฉ ํจ์จ์ ํฅ์๊ณผ ์์ก์ ์ฌ์ฌ์ฉ ๊ธฐ๊ฐ ์ฆ์ง์ ๊ธฐ๋ํ ์ ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ์๋์ผ๋ก, ๊ทธ๋ฆฌ๊ณ ๊ฐ๋ณ์ ์ผ๋ก ์๋ฌผ ์์ก ์ ๋ณด์ ๊ธฐ๋ฐํ์ฌ ์์ก ๊ณต๊ธ๋์ ์ ์ดํ๊ณ , ์๋ฌผ ์์ฅ์ ์ ํฉํ ์กฐ์ฑ์ ๋ง๊ฒ ํ์ฌ ์์ก์ ์ด์จ ๋๋ ์ผ์ฑ์ ๊ธฐ๋ฐํ์ฌ ์ ์ ํ ์์ค๋งํผ์ ๋ฌผ๊ณผ ๊ฐ๋ณ ์๋ถ ๋น๋ฃ๋ฅผ ๋ณด์ถฉํ ์ ์๋ ์ ๋ฐ ์๊ฒฝ์ฌ๋ฐฐ ์์ก ๊ด๋ฆฌ ์์คํ
์ ๊ฐ๋ฐํ๋ ๊ฒ์ด๋ค. ํด๋น ๋ชฉํ๋ฅผ ๋ฌ์ฑํ๊ธฐ ์ํด, ๋ณ์ดํ๋ ๋ฌผ๊ณผ ์๋ถ ์๊ตฌ๋์ ์ธก์ ํ ์ ์๋ ๋ชจ๋ํฐ๋ง ๊ธฐ์ ๋ค์ ๋ถ์ํ๊ณ ๊ฐ ๋ชจ๋ํฐ๋ง ๊ธฐ์ ๋ค์ ๋ํ ๊ฒ์ฆ์ ์ํํ์๋ค. ๋จผ์ , ์๋ฌผ์ ๋ฌผ ์๊ตฌ๋์ ์ค์๊ฐ์ผ๋ก ๊ด์ธกํ ์ ์๋ ์์ ๊ธฐ๋ฐ ์ธก์ ๊ธฐ์ ์ ์กฐ์ฌํ์๋ค. ์์ ๊ธฐ๋ฐ ๋ถ์ ํ์ฉ์ ์ํด ๋ฐ๋ง๊ฒฝ ๊ธฐ๋ฐ์ ์ํ์ ์๊ฒฝ์ฌ๋ฐฐ ํ๊ฒฝ์์ ์๋ผ๋ ์์ถ์ ์ด๋ฏธ์ง๋ค์ ์ ์ฒด ๋ฒ ๋์ ๋ํด ์์งํ ์ ์๋ ์์ ๋ชจ๋ํฐ๋ง ์์คํ
์ ๊ตฌ์ฑํ์๊ณ , ์์งํ ์์ ์ค ์์ถ ๋ถ๋ถ๋ง์ excess green (ExG)๊ณผ Otsu ๋ฐฉ๋ฒ์ ํตํด ๋ถ๋ฆฌํ์ฌ ํฌ์์๋ฌผ๋ฉด์ (CC, canopy cover)์ ํ๋ํ์๋ค. ์์ ์ฒ๋ฆฌ ๊ธฐ์ ์ ์ ์ฉ์ฑ ํ๊ฐ๋ฅผ ์ํด ์ง์ ๋ถ์ํ ํฌ์์๋ฌผ๋ฉด์ ๊ฐ๊ณผ ์ด๋ฅผ ๋น๊ตํ์๋ค. ๋น๊ต ๊ฒ์ฆ ๊ฒฐ๊ณผ์์ ํฌ์์๋ฌผ๋ฉด์ ์ธก์ ์ ์ํ ์์ ์์ง ๋ฐ ๋ถ์์ด ์ํ ์ ๊น์ง์ ์์ถ์ ๋ํด ์ ์ฉ ๊ฐ๋ฅํจ์ ํ์ธํ์๋ค. ์ดํ ์์งํ ํฌ์์๋ฌผ๋ฉด์ ๊ณผ ๊ธฐ์จ, ์๋์ต๋, ์ผ์ฌ๋์ ๊ธฐ๋ฐ์ผ๋ก ์์ก ์ค์ธ ์์ถ๋ค์ด ์๊ตฌํ๋ ๋ฌผ์ ์์ ์์ธกํ๊ธฐ ์ํด Penman-Monteith ๋ฐฉ์ ์ ๊ธฐ๋ฐ์ ์ฆ์ฐ๋ ์์ธก ๋ชจ๋ธ์ ๊ตฌ์ฑํ์์ผ๋ฉฐ ์ค์ ์ฆ์ฐ๋๊ณผ ๋น๊ตํ์์ ๋ ๋์ ์ผ์น๋๋ฅผ ํ์ธํ์๋ค. ๊ฐ๋ณ ์ด์จ ๋๋ ์ธก์ ๊ณผ ๊ด๋ จํ์ฌ์๋, ์ด์จ์ ํ์ฑ์ ๊ทน (ISE, ion-selective electrode)๋ฅผ ์ด์ฉํ ์๊ฒฝ์ฌ๋ฐฐ ์์ก ๋ด ์ด์จ์ ์ฐ์์ ์ด๊ณ ์์จ์ ์ธ ๋ชจ๋ํฐ๋ง ์ํ์ ์ํด 2์ ์ ๊ทํ, ์ธ๊ณต์ ๊ฒฝ๋ง, ๊ทธ๋ฆฌ๊ณ ์ด ๋์ ๋ณตํฉ์ ์ผ๋ก ๊ตฌ์ฑํ ํ์ด๋ธ๋ฆฌ๋ ์ ํธ ์ฒ๋ฆฌ ๊ธฐ๋ฒ์ ์ฑ๋ฅ์ ๋น๊ตํ์ฌ ๋ถ์ํ์๋ค. ๋ถ์ ๊ฒฐ๊ณผ, ํ์ด๋ธ๋ฆฌ๋ ์ ํธ ์ฒ๋ฆฌ ๋ฐฉ์์ด ๊ฐ์ฅ ๋์ ์ ํ์ฑ์ ๋ณด์์ผ๋, ์ผ์ ๊ณ ์ฅ์ ์ทจ์ฝํ ์ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ก ์ธํด ์ฅ๊ธฐ๊ฐ ๋ชจ๋ํฐ๋ง ์์ ์ฑ์ ์์ด์๋ ๊ฐ์ฅ ๋์ ์ ๋ฐ๋๋ฅผ ๊ฐ์ง 2์ ์ ๊ทํ ๋ฐฉ์์ ์ผ์ ์ด๋ ์ด์ ์ ์ฉํ๋ ๊ฒ์ด ์ ํฉํ ๊ฒ์ผ๋ก ํ๋จํ์๋ค. ๋ํ, ์ฃผ์ด์ง ๊ฐ๋ณ ์ด์จ ๋๋ ๋ชฉํ๊ฐ์ ๋ง๋ ๋น๋ฃ ์ผ ๋ฐ ๋ฌผ์ ์ต์ ์ฃผ์
๋์ ๊ฒฐ์ ํ๊ธฐ ์ํด ์์ฌ๊ฒฐ์ ํธ๋ฆฌ ๊ตฌ์กฐ์ ๋น๋ฃ ํฌ์
์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ์๋ค. ์ ์ํ ๋น๋ฃ ํฌ์
์๊ณ ๋ฆฌ์ฆ์ ํจ๊ณผ์ ๋ํด์๋ ์์ฐจ์ ์ธ ๋ชฉํ์ ๋ํ ๋ณด์ถฉ ๋ฐ ํน์ ์ฑ๋ถ์ ๋ํด ์ง์ค์ ์ธ ๋ณํ๋ฅผ ๋ถ์ฌํ ๋ณด์ถฉ ์ํ ์คํ์ ํตํด ๊ฒ์ฆํ์์ผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์ฃผ์ด์ง ๋ชฉํ๊ฐ๋ค์ ๋ฐ๋ผ ์ฑ๊ณต์ ์ผ๋ก ์์ก์ ์กฐ์ฑํ์์์ ํ์ธํ์๋ค. ๋ง์ง๋ง์ผ๋ก, ์ ์๋์๋ ์ผ์ฑ ๋ฐ ์ ์ด ๊ธฐ์ ๋ค์ ํตํฉํ์ฌ NFT ๊ธฐ๋ฐ์ ์ํ์ ์๊ฒฝ์ฌ๋ฐฐ ๋ฐฐ๋์ ์์ถ ์ฌ๋ฐฐ๋ฅผ ์ํํ์ฌ ์ค์ฆํ์๋ค. ์ค์ฆ ์คํ์์, ์ข
๋์ ๊ณ ์ ์๊ฐ ์์ก ๊ณต๊ธ ๋๋น 57.4%์ ์์ก ๊ณต๊ธ๋ ๊ฐ์์ ์์ถ ์์ก์ ์ด์ง์ ํ์ธํ์๋ค. ๋์์, ๊ฐ๋ฐ ์์คํ
์ NO3, K, Ca, Mg, ๊ทธ๋ฆฌ๊ณ P์ ๋ํด ๊ฐ๊ฐ 4.9%, 1.4%, 3.2%, 5.2%, ๊ทธ๋ฆฌ๊ณ 14.9% ์์ค์ ๋ณ๋๊ณ์ ์์ค์ ๋ณด์ฌ EC๊ธฐ๋ฐ ๋ณด์ถฉ ๋ฐฉ์์์ ๋ํ๋ ๋ณ๋๊ณ์ 6.9%, 4.9%, 23.7%, 8.6%, ๊ทธ๋ฆฌ๊ณ 8.3%๋ณด๋ค ๋์ฒด์ ์ผ๋ก ์ฐ์ํ ์ด์จ ๊ท ํ ์ ์ง ์ฑ๋ฅ์ ๋ณด์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ค์ ํตํด ๊ฐ๋ฐ ์ ๋ฐ ๊ด๋น ์์คํ
์ด ๊ธฐ์กด๋ณด๋ค ํจ์จ์ ์ธ ์์ก์ ๊ณต๊ธ๊ณผ ๊ด๋ฆฌ๋ฅผ ํตํด ์์ก ์ด์ฉ ํจ์จ์ฑ๊ณผ ์์ฐ์ฑ์ ์ฆ์ง์ ๊ธฐ์ฌํ ์ ์์ ๊ฒ์ผ๋ก ํ๋จ๋์๋ค.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: mgโLโ1) 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: mgโLโ1) 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
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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
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
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
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
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 systemโs 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
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