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κ°λ³ μ΄μ¨ λ° μλ¬Ό μμ‘ μΌμ± κΈ°λ°μ μ λ° μκ²½μ¬λ°° μμ‘ κ΄λ¦¬ μμ€ν
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곡ν), 2020. 8. κΉνμ§.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.νμ¬μ μνμ μκ²½μ¬λ°° μμ€ν
μμ μμ‘μ λΆμκ³Ό 보좩μ μ κΈ°μ λλ (EC, electrical conductivity) λ° pHλ₯Ό κΈ°λ°μΌλ‘ μνλκ³ μμΌλ©°, μμ‘μ 곡κΈμ μλ¬Όμ μμ‘ μνμ λν κ³ λ € μμ΄ νμ μΌμ ν μκ° λμ ννκ° λμνμ¬ κ³΅κΈλλ ννμ΄λ€. κ·Έλ¬λ EC κΈ°λ°μ μμ‘ κ΄λ¦¬λ κ°λ³ μ΄μ¨ λλμ λμ μΈ λ³νλ₯Ό κ°μ§ν μ μμ΄ λ°λ³΅λλ 보좩 μ€ λΆκ· νμ΄ λ°μνκ² λμ΄ μμ‘μ νκΈ°λ₯Ό μΌκΈ°νλ©°, κ³ μ λ μκ° λμμ μμ‘ κ³΅κΈμ μλ¬Όμ λν΄ κ³Όμ λλ λΆμΆ©λΆν λ¬Ό 곡κΈμΌλ‘ μ΄μ΄μ Έ λ¬Ό μ¬μ© ν¨μ¨μ μ νλ₯Ό μΌμΌν¨λ€. μ΄λ¬ν λ¬Έμ λ€μ λν΄, κ°λ³ μ΄μ¨ λλμ λν΄ λΆμ‘±ν μ±λΆλ§μ μ νμ μΌλ‘ 보좩νκ³ , μλ¬Όμ μμ‘ μ λμ κΈ°λ°νμ¬ νμν μμ€μ λ§κ² μμ‘μ 곡κΈνλ μ λ° λμ
μ κΈ°λ°ν μμ‘ κ΄λ¦¬λ₯Ό μννλ©΄ λ¬Όκ³Ό λΉλ£ μ¬μ© ν¨μ¨μ ν₯μκ³Ό μμ‘μ μ¬μ¬μ© κΈ°κ° μ¦μ§μ κΈ°λν μ μλ€. λ³Έ μ°κ΅¬μ λͺ©μ μ μλμΌλ‘, κ·Έλ¦¬κ³ κ°λ³μ μΌλ‘ μλ¬Ό μμ‘ μ 보μ κΈ°λ°νμ¬ μμ‘ κ³΅κΈλμ μ μ΄νκ³ , μλ¬Ό μμ₯μ μ ν©ν μ‘°μ±μ λ§κ² νμ¬ μμ‘μ μ΄μ¨ λλ μΌμ±μ κΈ°λ°νμ¬ μ μ ν μμ€λ§νΌμ λ¬Όκ³Ό κ°λ³ μλΆ λΉλ£λ₯Ό 보좩ν μ μλ μ λ° μκ²½μ¬λ°° μμ‘ κ΄λ¦¬ μμ€ν
μ κ°λ°νλ κ²μ΄λ€. ν΄λΉ λͺ©νλ₯Ό λ¬μ±νκΈ° μν΄, λ³μ΄νλ λ¬Όκ³Ό μλΆ μꡬλμ μΈ‘μ ν μ μλ λͺ¨λν°λ§ κΈ°μ λ€μ λΆμνκ³ κ° λͺ¨λν°λ§ κΈ°μ λ€μ λν κ²μ¦μ μννμλ€. λ¨Όμ , μλ¬Όμ λ¬Ό μꡬλμ μ€μκ°μΌλ‘ κ΄μΈ‘ν μ μλ μμ κΈ°λ° μΈ‘μ κΈ°μ μ μ‘°μ¬νμλ€. μμ κΈ°λ° λΆμ νμ©μ μν΄ λ°λ§κ²½ κΈ°λ°μ μνμ μκ²½μ¬λ°° νκ²½μμ μλΌλ μμΆμ μ΄λ―Έμ§λ€μ μ 체 λ² λμ λν΄ μμ§ν μ μλ μμ λͺ¨λν°λ§ μμ€ν
μ ꡬμ±νμκ³ , μμ§ν μμ μ€ μμΆ λΆλΆλ§μ excess green (ExG)κ³Ό Otsu λ°©λ²μ ν΅ν΄ λΆλ¦¬νμ¬ ν¬μμλ¬Όλ©΄μ (CC, canopy cover)μ νλνμλ€. μμ μ²λ¦¬ κΈ°μ μ μ μ©μ± νκ°λ₯Ό μν΄ μ§μ λΆμν ν¬μμλ¬Όλ©΄μ κ°κ³Ό μ΄λ₯Ό λΉκ΅νμλ€. λΉκ΅ κ²μ¦ κ²°κ³Όμμ ν¬μμλ¬Όλ©΄μ μΈ‘μ μ μν μμ μμ§ λ° λΆμμ΄ μν μ κΉμ§μ μμΆμ λν΄ μ μ© κ°λ₯ν¨μ νμΈνμλ€. μ΄ν μμ§ν ν¬μμλ¬Όλ©΄μ κ³Ό κΈ°μ¨, μλμ΅λ, μΌμ¬λμ κΈ°λ°μΌλ‘ μμ‘ μ€μΈ μμΆλ€μ΄ μꡬνλ λ¬Όμ μμ μμΈ‘νκΈ° μν΄ Penman-Monteith λ°©μ μ κΈ°λ°μ μ¦μ°λ μμΈ‘ λͺ¨λΈμ ꡬμ±νμμΌλ©° μ€μ μ¦μ°λκ³Ό λΉκ΅νμμ λ λμ μΌμΉλλ₯Ό νμΈνμλ€. κ°λ³ μ΄μ¨ λλ μΈ‘μ κ³Ό κ΄λ ¨νμ¬μλ, μ΄μ¨μ νμ±μ κ·Ή (ISE, ion-selective electrode)λ₯Ό μ΄μ©ν μκ²½μ¬λ°° μμ‘ λ΄ μ΄μ¨μ μ°μμ μ΄κ³ μμ¨μ μΈ λͺ¨λν°λ§ μνμ μν΄ 2μ μ κ·ν, μΈκ³΅μ κ²½λ§, κ·Έλ¦¬κ³ μ΄ λμ 볡ν©μ μΌλ‘ ꡬμ±ν νμ΄λΈλ¦¬λ μ νΈ μ²λ¦¬ κΈ°λ²μ μ±λ₯μ λΉκ΅νμ¬ λΆμνμλ€. λΆμ κ²°κ³Ό, νμ΄λΈλ¦¬λ μ νΈ μ²λ¦¬ λ°©μμ΄ κ°μ₯ λμ μ νμ±μ 보μμΌλ, μΌμ κ³ μ₯μ μ·¨μ½ν μ κ²½λ§ κ΅¬μ‘°λ‘ μΈν΄ μ₯κΈ°κ° λͺ¨λν°λ§ μμ μ±μ μμ΄μλ κ°μ₯ λμ μ λ°λλ₯Ό κ°μ§ 2μ μ κ·ν λ°©μμ μΌμ μ΄λ μ΄μ μ μ©νλ κ²μ΄ μ ν©ν κ²μΌλ‘ νλ¨νμλ€. λν, μ£Όμ΄μ§ κ°λ³ μ΄μ¨ λλ λͺ©νκ°μ λ§λ λΉλ£ μΌ λ° λ¬Όμ μ΅μ μ£Όμ
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μκ³ λ¦¬μ¦μ ν¨κ³Όμ λν΄μλ μμ°¨μ μΈ λͺ©νμ λν 보좩 λ° νΉμ μ±λΆμ λν΄ μ§μ€μ μΈ λ³νλ₯Ό λΆμ¬ν 보좩 μν μ€νμ ν΅ν΄ κ²μ¦νμμΌλ©°, κ·Έ κ²°κ³Ό μ μν μκ³ λ¦¬μ¦μ μ£Όμ΄μ§ λͺ©νκ°λ€μ λ°λΌ μ±κ³΅μ μΌλ‘ μμ‘μ μ‘°μ±νμμμ νμΈνμλ€. λ§μ§λ§μΌλ‘, μ μλμλ μΌμ± λ° μ μ΄ κΈ°μ λ€μ ν΅ν©νμ¬ NFT κΈ°λ°μ μνμ μκ²½μ¬λ°° λ°°λμ μμΆ μ¬λ°°λ₯Ό μννμ¬ μ€μ¦νμλ€. μ€μ¦ μ€νμμ, μ’
λμ κ³ μ μκ° μμ‘ κ³΅κΈ λλΉ 57.4%μ μμ‘ κ³΅κΈλ κ°μμ μμΆ μμ‘μ μ΄μ§μ νμΈνμλ€. λμμ, κ°λ° μμ€ν
μ NO3, K, Ca, Mg, κ·Έλ¦¬κ³ Pμ λν΄ κ°κ° 4.9%, 1.4%, 3.2%, 5.2%, κ·Έλ¦¬κ³ 14.9% μμ€μ λ³λκ³μ μμ€μ λ³΄μ¬ ECκΈ°λ° λ³΄μΆ© λ°©μμμ λνλ λ³λκ³μ 6.9%, 4.9%, 23.7%, 8.6%, κ·Έλ¦¬κ³ 8.3%λ³΄λ€ λ체μ μΌλ‘ μ°μν μ΄μ¨ κ· ν μ μ§ μ±λ₯μ 보μλ€. μ΄λ¬ν κ²°κ³Όλ€μ ν΅ν΄ κ°λ° μ λ° κ΄λΉ μμ€ν
μ΄ κΈ°μ‘΄λ³΄λ€ ν¨μ¨μ μΈ μμ‘μ 곡κΈκ³Ό κ΄λ¦¬λ₯Ό ν΅ν΄ μμ‘ μ΄μ© ν¨μ¨μ±κ³Ό μμ°μ±μ μ¦μ§μ κΈ°μ¬ν μ μμ κ²μΌλ‘ νλ¨λμλ€.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: mgβLβ1) 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: mgβLβ1) 154
A5. Block Diagrams of the LabVIEW Program used in the Chapters 5 and 6 155
ABSTRACT IN KOREAN 160Docto