A novel 3D imaging system for strawberry phenotyping

Accurate and quantitative phenotypic data in plant breeding programmes is vital in breeding to assess the performance of genotypes and to make selections. Traditional strawberry phenotyping relies on the human eye to assess most external fruit quality attributes, which is time-consuming and subjective. 3D imaging is a promising high-throughput technique that allows multiple external fruit quality attributes to be measured simultaneously. A low cost multi-view stereo (MVS) imaging system was developed, which captured data from 360° around a target strawberry fruit. A 3D point cloud of the sample was derived and analysed with custom-developed software to estimate berry height, length, width, volume, calyx size, colour and achene number. Analysis of these traits in 100 fruits showed good concordance with manual assessment methods. This study demonstrates the feasibility of an MVS based 3D imaging system for the rapid and quantitative phenotyping of seven agronomically important external strawberry traits. With further improvement, this method could be applied in strawberry breeding programmes as a cost effective phenotyping technique

Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data

3차원 스캔 모델과 광학 시뮬레이션을 이용한 파프리카의 생육 단계 및 차광 조건별 수광과 광합성 속도 예측

학위논문 (석사)-- 서울대학교 대학원 : 농업생명과학대학 식물생산과학부(원예과학전공), 2019. 2. 손정익.In greenhouses, many crops are cultivated at high planting densities because this type of cultivation is highly productive and presents economic advantages. A high planting density causes strong mutual shading effects among adjacent crops, decreasing canopy light interception, photosynthesis, and consequently, crop yield and quality. The objective of this study was to analyze the light interception and photosynthetic rates of the paprika canopy using 3D-scanned plant models and optical simulation according to the growth stage and shading condition. Here, 3D plant models of paprika plants grown in greenhouses were constructed at 7, 35, 63, 91, and 112 days after transplanting by using a portable 3D scanner. To investigate the shading effects, the 3D-scanned plant models were arranged as isotropic forms of 1 × 1, 3 × 3, 5 × 5, 7 × 7, and 9 × 9 plants with a distance of 60 cm between plants, and the light interception of the center plant in the arrangement was obtained with the growth stage by simulation. The canopy photosynthetic rates were calculated using the Farquhar, von Caemmerer, and Berry (FvCB) model. The total canopy light interception and light interception per unit leaf area of the center plant decreased due to the self- and mutual shading effects caused by the growth of each plant and the increase in the number of surrounding plants. The canopy photosynthetic rates showed similar patterns to those of the total light interception, but their decreasing rate was less than that of the total light interception because the leaf photosynthetic rate was saturated at the top of the canopy. In this study, the spatial distributions of the canopy light interception and photosynthetic rates could be analyzed by using 3D-scanned models of paprika and optical simulation. This method can be an effective tool for designing crop cultivation systems as well as estimating canopy light interception and photosynthesis in greenhouses.온실에서는 높은 단위 생산성을 위해 높은 재식 밀도로 작물을 재배한다. 그러나 높은 재식 밀도 조건에서는 인접한 개체에 의한 상호 차광이 발생하여 캐노피 수광과 광합성을 감소시키고, 결과적으로 작물 수량과 품질의 저하를 초래한다. 이에 따라 최적 재식 밀도를 찾기 위한 연구가 진행되었으나, 이를 간접적인 단위로 표현하여 작물 간의 상호 작용에 대한 분석이 어렵다. 따라서 본 연구의 목적은 3차원 스캔 모델과 광학 시뮬레이션을 이용하여 파프리카의 생육 단계와 차광 조건에 따른 수광과 광합성 속도를 분석하는 것이다. 3차원 스캐닝을 통해 정식 후 7, 35, 63, 91, 112일의 작물 모델을 구축하였다. 작물의 차광 효과를 분석하기 위해 작물 모델을 60cm 간격으로 1 × 1, 3 × 3, 5 × 5, 7 × 7, 9 × 9로 정방형 배치하여 생육 단계별 시뮬레이션을 진행하였고, 중심에 위치한 개체의 수광량을 계산하였다. 캐노피 광합성 속도는 FvCB 모델을 이용하여 계산하였다. 시뮬레이션 결과 모든 생육 단계에서 주변 개체 수가 증가할수록 작물 수광량은 감소하였다. 특히, 단위 엽면적당 수광량은 작물의 생육이 진행됨에 따라 감소하였다. 이는 각 작물의 생육에 따른 자기 광 차단과 주변 작물의 증가에 따른 상호 광 차단 효과 때문으로 생각된다. 이산화탄소 소모량은 총 수광량과 비슷한 변화 양상을 보였으나, 작물 상단부에서 광합성 포화가 나타났기 때문에 수광량 변화에 감소율은 작았다. 본 실험에서는 파프리카의 3차원 스캔 모델과 광학 시뮬레이션을 이용하여 생육 단계와 주변 개체의 증감에 따른 작물의 수광과 광합성 변화를 분석할 수 있었다. 이는 추후 온실 내에서 작물 재배 시스템을 디자인하고, 수광과 광합성을 예측하는 데에 효과적인 방법이 될 수 있을 것이다.ABSTRACT i CONTENTS iii LIST OF TABLES iv LIST OF FIGURES v INTRODUCTION 1 LITERATURE REVIEW 3 MATERIALS AND METHODS 6 DISCUSSION 12 CONCLUSION 24 LITERATURE CITED 29 APPENDICES 37 ABSTRACT IN KOREAN 40Maste

온실 내 군락 내부 LED보광에 의한 파프리카의 광 이용 효율 및 물 이용 효율 평가

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 농림생물자원학부, 2021.8. 권성민.In greenhouses, the higher plant density, the less sunlight inside the canopy due to mutual shadings by adjacent plants. As a countermeasure, inter-lighting has been introduced to compensate for the lack of light in the middle and bottom parts of the canopy. However, most research have focused on growth and yield, not light use efficiency (LUE) and water use efficiency (WUE). The objective of this study was to evaluate the LUE and WUE of sweet peppers subjected to inter-lighting in greenhouses. Two lighting treatments, natural light (control) and supplemental inter-lighting of red and blue LEDs, were applied. The inter-lighting started at 34 days after transplanting (DAT). The ratio of red and blue light in photosynthetic photon flux density (PPFD) was 8:2, and the total PPFD was adjusted to 71 μmol·m2·s1 at 20 cm distance. To quantify the transpiration from the plants, the amount of daily transpiration was measured by subtracting the drainage from the supplied nutrient solution, and hydroponic system weight change. The photosynthetic rate was obtained by measuring light response curves at light intensities of 0, 50, 100, 200, 400, 600, 900, 1200, 1500, and 2000 μmol·m2·s1 in PPFD. The LUEs were calculated based on the light interception obtained by 3D-scanned plant models and ray-tracing simulation. The WUEs were calculated using dry weight per accumulated water consumption. The calculated results showed the increase in LUE at the canopy level, which is likely due to the improvement of canopy light distribution by inter-lighting. The WUE for biomass and fruit yield were higher in inter-lighting than those in the control. These results were due to the increases in plant dry weight and fruit yield, which is greater than the increase in water consumption by inter-lighting. In this study, the improvement of LUE and WUE by inter-lighting could be quantified by optical simulation and the water consumption during the whole growth period.온실에서는 재식밀도가 높아질수록 인접한 식물간의 간섭 현상으로 캐노피 내부에 빛이 부족하게 된다. 이에 대한 대책으로 군락 중, 하단부의 빛 부족을 보완하기 위해 군락 내 보광을 도입하고 있다. 현재 대부분의 연구는 광이용 효율(LUE), 물이용 효율(WUE)이 아닌 생장과 수확량에 초점을 맞추어 왔다. 따라서 이 연구의 목적은 온실에서 군락 내 측면 보광(inter-lighting) 하에서 자란 파프리카의 LUE와 WUE를 평가하는 것이다. 자연광(대조구)과 자연광에 적색 및 청색 LED에 의한 보광 처리구가 적용되었다. 보광 처리는 정식 후 34일 (DAT)에 시작하였고, 적색 및 청색 LED 비율이 8:2인 광원은 20cm 거리에서 71mol · m-2 · s-1로 설정하였다. 식물의 증산량을 정량화 하기 위해 수경재배 무게측정 시스템으로 급액량에서 배액량을 제하여 하루 증산량을 측정했다. 광합성속도는 0, 50, 100, 200, 400, 600, 900, 1200, 1500, 2000 μmol · m-2 · s-1에서 광반응 곡선을 측정하여 얻었다. LUE는 40, 60, 80, 100 및 120 DAT에서 3D 식물 모델 및 광학 시뮬레이션으로 측정한 수광 태세를 기반으로 계산하였다. WUE는 40, 60, 80, 100 및 120 DAT에서 누적된 물 소비량 대비 건조중으로 계산하였다. 시뮬레이션 결과에서 캐노피 수준의 LUE가 증가하였는데, 이는 보광 이 캐노피 내부의 수광 개선에 기인한 것으로 보인다. 건물중 및 과일 생산량에 대한 WUE는 대조군보다 보광 처리구에서 더 높았다. 이러한 결과는 보광에 의한 물 소비보다 식물의 건조 중량과 과일 수확량의 증가폭이 더 높았기 때문이다. 본 연구에서는 광학 시뮬레이션과 전체 생장 기간 동안의 물 이용량을 분석하여 군락 내 측면 보광으로 인한 LUE 및 WUE 증가를 정량화 할 수 있게 되었다.INTRODUCTION 1 LITERATURE REVIEW 3 MATERIALS AND METHODS 5 RESULTS 15 DISCUSSION 25 CONCLUSION 28 LITERATURE CITED 29 ABSTRACT IN KOREAN 39석

Application of UAV based high-resolution remote sensing for crop monitoring

Advances in technologies could offer enormous potential for crop monitoring applications, allowing the real-time acquisition of various environmental data. Technology such as high spatio-temporal imagery of unmanned aerial vehicles (UAV’s) can be widely used in crop monitoring applications. These technologies are expected to revolutionize the global agriculture practices, by enabling decision-making during the crop cycle days. Such results allow the effective practice of agricultural inputs, aiding precision agriculture pillars, i.e., applying the right practice in the right place, with the right amount and time. However, the actual exploitation of UAV’s has not been much strong in smart farming, mainly due to the challenges faced during selecting and deploying relevant technologies, including data acquisition and processing methods. The major problem is that there is still no consistent workflow for the use of UAV’s in such areas, as this mechanization is relatively new. In this article, the latest applications of UAV’s for crop monitoring are reviewed. It covers the most common applications, the types of UAV’s used and then we focused on data acquisition methods and technologies, employing the benefit and drawbacks of each. It also indicates the most popular image processing methods and summarizes the potential application in agricultural operations. 

Automatic leaf segmentation and overlapping leaf separation using stereo vision

Farm management and crop quality assessment is becoming increasingly automated to keep up with demand. The physical examination of the plant leaves, stems and fruit can provide valuable information about a plant’s health. Automating the visual inspection through machine vision spawns challenges such as occlusions, irregular lightning and varying environmental conditions. In this paper, a plant leaf extraction algorithm utilising depth from a stereo vision sensor is presented. The algorithm tackles multiple leaf segmentation and overlapping leaf separation through synergising features such as colour, shape and depth. Depth is particularly used to measure discontinuities along its gradient in the disparity maps. The algorithm has a segmentation rate of 78% for individual plant leaves, over a range of complex backgrounds and changing plant canopies. The proposed algorithm was evaluated using 272 cotton and hibiscus plant images with results demonstrating that depth properties were effective in separating occluded and overlapping leaves, with a high separation rate of 84%. Leaf occlusion could be detected automatically without adding any artificial tags on the leaf boundaries. Furthermore, the results show a nearly identical performance for both types of plants (cotton and hibiscus) under various lighting and environmental conditions. The developed algorithm could be potentially applied to other types of plants that have similar structures to cotton and hibiscus

개별 이온 및 작물 생육 센싱 기반의 정밀 수경재배 양액 관리 시스템

학위논문 (박사) -- 서울대학교 대학원 : 농업생명과학대학 바이오시스템·소재학부(바이오시스템공학), 2020. 8. 김학진.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.현재의 순환식 수경재배 시스템에서 양액의 분석과 보충은 전기전도도 (EC, electrical conductivity) 및 pH를 기반으로 수행되고 있으며, 양액의 공급은 작물의 생육 상태에 대한 고려 없이 항상 일정한 시간 동안 펌프가 동작하여 공급되는 형태이다. 그러나 EC 기반의 양액 관리는 개별 이온 농도의 동적인 변화를 감지할 수 없어 반복되는 보충 중 불균형이 발생하게 되어 양액의 폐기를 야기하며, 고정된 시간 동안의 양액 공급은 작물에 대해 과잉 또는 불충분한 물 공급으로 이어져 물 사용 효율의 저하를 일으킨다. 이러한 문제들에 대해, 개별 이온 농도에 대해 부족한 성분만을 선택적으로 보충하고, 작물의 생육 정도에 기반하여 필요한 수준에 맞게 양액을 공급하는 정밀 농업에 기반한 양액 관리를 수행하면 물과 비료 사용 효율의 향상과 양액의 재사용 기간 증진을 기대할 수 있다. 본 연구의 목적은 자동으로, 그리고 가변적으로 작물 생육 정보에 기반하여 양액 공급량을 제어하고, 작물 생장에 적합한 조성에 맞게 현재 양액의 이온 농도 센싱에 기반하여 적절한 수준만큼의 물과 개별 양분 비료를 보충할 수 있는 정밀 수경재배 양액 관리 시스템을 개발하는 것이다. 해당 목표를 달성하기 위해, 변이하는 물과 양분 요구량을 측정할 수 있는 모니터링 기술들을 분석하고 각 모니터링 기술들에 대한 검증을 수행하였다. 먼저, 작물의 물 요구량을 실시간으로 관측할 수 있는 영상 기반 측정 기술을 조사하였다. 영상 기반 분석 활용을 위해 박막경 기반의 순환식 수경재배 환경에서 자라는 상추의 이미지들을 전체 베드에 대해 수집할 수 있는 영상 모니터링 시스템을 구성하였고, 수집한 영상 중 상추 부분만을 excess green (ExG)과 Otsu 방법을 통해 분리하여 투영작물면적 (CC, canopy cover)을 획득하였다. 영상 처리 기술의 적용성 평가를 위해 직접 분석한 투영작물면적 값과 이를 비교하였다. 비교 검증 결과에서 투영작물면적 측정을 위한 영상 수집 및 분석이 수확 전까지의 상추에 대해 적용 가능함을 확인하였다. 이후 수집한 투영작물면적과 기온, 상대습도, 일사량을 기반으로 생육 중인 상추들이 요구하는 물의 양을 예측하기 위해 Penman-Monteith 방정식 기반의 증산량 예측 모델을 구성하였으며 실제 증산량과 비교하였을 때 높은 일치도를 확인하였다. 개별 이온 농도 측정과 관련하여서는, 이온선택성전극 (ISE, ion-selective electrode)를 이용한 수경재배 양액 내 이온의 연속적이고 자율적인 모니터링 수행을 위해 2점 정규화, 인공신경망, 그리고 이 둘을 복합적으로 구성한 하이브리드 신호 처리 기법의 성능을 비교하여 분석하였다. 분석 결과, 하이브리드 신호 처리 방식이 가장 높은 정확성을 보였으나, 센서 고장에 취약한 신경망 구조로 인해 장기간 모니터링 안정성에 있어서는 가장 높은 정밀도를 가진 2점 정규화 방식을 센서 어레이에 적용하는 것이 적합할 것으로 판단하였다. 또한, 주어진 개별 이온 농도 목표값에 맞는 비료 염 및 물의 최적 주입량을 결정하기 위해 의사결정트리 구조의 비료 투입 알고리즘을 제시하였다. 제시한 비료 투입 알고리즘의 효과에 대해서는 순차적인 목표에 대한 보충 및 특정 성분에 대해 집중적인 변화를 부여한 보충 수행 실험을 통해 검증하였으며, 그 결과 제시한 알고리즘은 주어진 목표값들에 따라 성공적으로 양액을 조성하였음을 확인하였다. 마지막으로, 제시되었던 센싱 및 제어 기술들을 통합하여 NFT 기반의 순환식 수경재배 배드에 상추 재배를 수행하여 실증하였다. 실증 실험에서, 종래의 고정 시간 양액 공급 대비 57.4%의 양액 공급량 감소와 상추 생육의 촉진을 확인하였다. 동시에, 개발 시스템은 NO3, K, Ca, Mg, 그리고 P에 대해 각각 4.9%, 1.4%, 3.2%, 5.2%, 그리고 14.9% 수준의 변동계수 수준을 보여 EC기반 보충 방식에서 나타난 변동계수 6.9%, 4.9%, 23.7%, 8.6%, 그리고 8.3%보다 대체적으로 우수한 이온 균형 유지 성능을 보였다. 이러한 결과들을 통해 개발 정밀 관비 시스템이 기존보다 효율적인 양액의 공급과 관리를 통해 양액 이용 효율성과 생산성의 증진에 기여할 수 있을 것으로 판단되었다.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: mg∙L−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: mg∙L−1) 154 A5. Block Diagrams of the LabVIEW Program used in the Chapters 5 and 6 155 ABSTRACT IN KOREAN 160Docto

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