Segmentation and size estimation of tomatoes from sequences of paired images

International audienceIn this paper, we present a complete system to monitor the growth of tomatoes from images acquired in open fields. This is a challenging task because of the severe occlusion and poor contrast in the images. We approximate the tomatoes by spheres in the 3D space, hence by ellipses in the image space. The tomatoes are first identified in the images using a segmentation procedure. Then, the size of the tomatoes is measured from the obtained segmentation and camera parameters. The shape information combined with temporal information, given the limited evolution from an image to the next one, is used throughout the system to increase the robustness with respect to occlusion and poor contrast. The segmentation procedure presented in this paper is an extension of our previous work based on active contours. Here, we present a method to update the position of the tomato by comparing the SIFT descriptors computed at predetermined points in two consecutive images. This leads to a very accurate estimation of the tomato position, from which the entire segmentation procedure benefits. The average error between the automatic and manual segmentations is around 4 % (expressed as the percentage of tomato size) with a good robustness with respect to occlusion (up to 50 %). The size estimation procedure was evaluated by calculating the size of tomatoes under a controlled environment. In this case, the mean percentage error between the actual radius and the estimated size is around 2.35 % with a standard deviation of 1.83 % and is less than 5 % in most (91 %) cases. The complete system was also applied to estimate the size of tomatoes cultivated in open fields

Review of Methods for Smartphone Application Foot Size Estimation From Images

The task of obtaining real-world coordinates of an object is quite challenging. In this paper proposed brief explanation of algorithms dedicated to overcome this challenge. They were analyzed including their advantages and disadvantages in application to possible implementation in smartphone app

How to make a pizza: Learning a compositional layer-based GAN model

A food recipe is an ordered set of instructions for preparing a particular dish. From a visual perspective, every instruction step can be seen as a way to change the visual appearance of the dish by adding extra objects (e.g., adding an ingredient) or changing the appearance of the existing ones (e.g., cooking the dish). In this paper, we aim to teach a machine how to make a pizza by building a generative model that mirrors this step-by-step procedure. To do so, we learn composable module operations which are able to either add or remove a particular ingredient. Each operator is designed as a Generative Adversarial Network (GAN). Given only weak image-level supervision, the operators are trained to generate a visual layer that needs to be added to or removed from the existing image. The proposed model is able to decompose an image into an ordered sequence of layers by applying sequentially in the right order the corresponding removing modules. Experimental results on synthetic and real pizza images demonstrate that our proposed model is able to: (1) segment pizza toppings in a weaklysupervised fashion, (2) remove them by revealing what is occluded underneath them (i.e., inpainting), and (3) infer the ordering of the toppings without any depth ordering supervision. Code, data, and models are available online.Comment: CVPR 201

Computer vision beyond the visible : image understanding through language

In the past decade, deep neural networks have revolutionized computer vision. High performing deep neural architectures trained for visual recognition tasks have pushed the field towards methods relying on learned image representations instead of hand-crafted ones, in the seek of designing end-to-end learning methods to solve challenging tasks, ranging from long-lasting ones such as image classification to newly emerging tasks like image captioning. As this thesis is framed in the context of the rapid evolution of computer vision, we present contributions that are aligned with three major changes in paradigm that the field has recently experienced, namely 1) the power of re-utilizing deep features from pre-trained neural networks for different tasks, 2) the advantage of formulating problems with end-to-end solutions given enough training data, and 3) the growing interest of describing visual data with natural language rather than pre-defined categorical label spaces, which can in turn enable visual understanding beyond scene recognition. The first part of the thesis is dedicated to the problem of visual instance search, where we particularly focus on obtaining meaningful and discriminative image representations which allow efficient and effective retrieval of similar images given a visual query. Contributions in this part of the thesis involve the construction of sparse Bag-of-Words image representations from convolutional features from a pre-trained image classification neural network, and an analysis of the advantages of fine-tuning a pre-trained object detection network using query images as training data. The second part of the thesis presents contributions to the problem of image-to-set prediction, understood as the task of predicting a variable-sized collection of unordered elements for an input image. We conduct a thorough analysis of current methods for multi-label image classification, which are able to solve the task in an end-to-end manner by simultaneously estimating both the label distribution and the set cardinality. Further, we extend the analysis of set prediction methods to semantic instance segmentation, and present an end-to-end recurrent model that is able to predict sets of objects (binary masks and categorical labels) in a sequential manner. Finally, the third part of the dissertation takes insights learned in the previous two parts in order to present deep learning solutions to connect images with natural language in the context of cooking recipes and food images. First, we propose a retrieval-based solution in which the written recipe and the image are encoded into compact representations that allow the retrieval of one given the other. Second, as an alternative to the retrieval approach, we propose a generative model to predict recipes directly from food images, which first predicts ingredients as sets and subsequently generates the rest of the recipe one word at a time by conditioning both on the image and the predicted ingredients.En l'última dècada, les xarxes neuronals profundes han revolucionat el camp de la visió per computador. Els resultats favorables obtinguts amb arquitectures neuronals profundes entrenades per resoldre tasques de reconeixement visual han causat un canvi de paradigma cap al disseny de mètodes basats en representacions d'imatges apreses de manera automàtica, deixant enrere les tècniques tradicionals basades en l'enginyeria de representacions. Aquest canvi ha permès l'aparició de tècniques basades en l'aprenentatge d'extrem a extrem (end-to-end), capaces de resoldre de manera efectiva molts dels problemes tradicionals de la visió per computador (e.g. classificació d'imatges o detecció d'objectes), així com nous problemes emergents com la descripció textual d'imatges (image captioning). Donat el context de la ràpida evolució de la visió per computador en el qual aquesta tesi s'emmarca, presentem contribucions alineades amb tres dels canvis més importants que la visió per computador ha experimentat recentment: 1) la reutilització de representacions extretes de models neuronals pre-entrenades per a tasques auxiliars, 2) els avantatges de formular els problemes amb solucions end-to-end entrenades amb grans bases de dades, i 3) el creixent interès en utilitzar llenguatge natural en lloc de conjunts d'etiquetes categòriques pre-definits per descriure el contingut visual de les imatges, facilitant així l'extracció d'informació visual més enllà del reconeixement de l'escena i els elements que la composen La primera part de la tesi està dedicada al problema de la cerca d'imatges (image retrieval), centrada especialment en l'obtenció de representacions visuals significatives i discriminatòries que permetin la recuperació eficient i efectiva d'imatges donada una consulta formulada amb una imatge d'exemple. Les contribucions en aquesta part de la tesi inclouen la construcció de representacions Bag-of-Words a partir de descriptors locals obtinguts d'una xarxa neuronal entrenada per classificació, així com un estudi dels avantatges d'utilitzar xarxes neuronals per a detecció d'objectes entrenades utilitzant les imatges d'exemple, amb l'objectiu de millorar les capacitats discriminatòries de les representacions obtingudes. La segona part de la tesi presenta contribucions al problema de predicció de conjunts a partir d'imatges (image to set prediction), entès com la tasca de predir una col·lecció no ordenada d'elements de longitud variable donada una imatge d'entrada. En aquest context, presentem una anàlisi exhaustiva dels mètodes actuals per a la classificació multi-etiqueta d'imatges, que són capaços de resoldre la tasca de manera integral calculant simultàniament la distribució probabilística sobre etiquetes i la cardinalitat del conjunt. Seguidament, estenem l'anàlisi dels mètodes de predicció de conjunts a la segmentació d'instàncies semàntiques, presentant un model recurrent capaç de predir conjunts d'objectes (representats per màscares binàries i etiquetes categòriques) de manera seqüencial. Finalment, la tercera part de la tesi estén els coneixements apresos en les dues parts anteriors per presentar solucions d'aprenentatge profund per connectar imatges amb llenguatge natural en el context de receptes de cuina i imatges de plats cuinats. En primer lloc, proposem una solució basada en algoritmes de cerca, on la recepta escrita i la imatge es codifiquen amb representacions compactes que permeten la recuperació d'una donada l'altra. En segon lloc, com a alternativa a la solució basada en algoritmes de cerca, proposem un model generatiu capaç de predir receptes (compostes pels seus ingredients, predits com a conjunts, i instruccions) directament a partir d'imatges de menjar.Postprint (published version

An Outlook into the Future of Egocentric Vision

What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.Comment: We invite comments, suggestions and corrections here: https://openreview.net/forum?id=V3974SUk1

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Fruit Detection and Tree Segmentation for Yield Mapping in Orchards

Accurate information gathering and processing is critical for precision horticulture, as growers aim to optimise their farm management practices. An accurate inventory of the crop that details its spatial distribution along with health and maturity, can help farmers efficiently target processes such as chemical and fertiliser spraying, crop thinning, harvest management, labour planning and marketing. Growers have traditionally obtained this information by using manual sampling techniques, which tend to be labour intensive, spatially sparse, expensive, inaccurate and prone to subjective biases. Recent advances in sensing and automation for field robotics allow for key measurements to be made for individual plants throughout an orchard in a timely and accurate manner. Farmer operated machines or unmanned robotic platforms can be equipped with a range of sensors to capture a detailed representation over large areas. Robust and accurate data processing techniques are therefore required to extract high level information needed by the grower to support precision farming. This thesis focuses on yield mapping in orchards using image and light detection and ranging (LiDAR) data captured using an unmanned ground vehicle (UGV). The contribution is the framework and algorithmic components for orchard mapping and yield estimation that is applicable to different fruit types and orchard configurations. The framework includes detection of fruits in individual images and tracking them over subsequent frames. The fruit counts are then associated to individual trees, which are segmented from image and LiDAR data, resulting in a structured spatial representation of yield. The first contribution of this thesis is the development of a generic and robust fruit detection algorithm. Images captured in the outdoor environment are susceptible to highly variable external factors that lead to significant appearance variations. Specifically in orchards, variability is caused by changes in illumination, target pose, tree types, etc. The proposed techniques address these issues by using state-of-the-art feature learning approaches for image classification, while investigating the utility of orchard domain knowledge for fruit detection. Detection is performed using both pixel-wise classification of images followed instance segmentation, and bounding-box regression approaches. The experimental results illustrate the versatility of complex deep learning approaches over a multitude of fruit types. The second contribution of this thesis is a tree segmentation approach to detect the individual trees that serve as a standard unit for structured orchard information systems. The work focuses on trellised trees, which present unique challenges for segmentation algorithms due to their intertwined nature. LiDAR data are used to segment the trellis face, and to generate proposals for individual trees trunks. Additional trunk proposals are provided using pixel-wise classification of the image data. The multi-modal observations are fine-tuned by modelling trunk locations using a hidden semi-Markov model (HSMM), within which prior knowledge of tree spacing is incorporated. The final component of this thesis addresses the visual occlusion of fruit within geometrically complex canopies by using a multi-view detection and tracking approach. Single image fruit detections are tracked over a sequence of images, and associated to individual trees or farm rows, with the spatial distribution of the fruit counting forming a yield map over the farm. The results show the advantage of using multi-view imagery (instead of single view analysis) for fruit counting and yield mapping. This thesis includes extensive experimentation in almond, apple and mango orchards, with data captured by a UGV spanning a total of 5 hectares of farm area, over 30 km of vehicle traversal and more than 7,000 trees. The validation of the different processes is performed using manual annotations, which includes fruit and tree locations in image and LiDAR data respectively. Additional evaluation of yield mapping is performed by comparison against fruit counts on trees at the farm and counts made by the growers post-harvest. The framework developed in this thesis is demonstrated to be accurate compared to ground truth at all scales of the pipeline, including fruit detection and tree mapping, leading to accurate yield estimation, per tree and per row, for the different crops. Through the multitude of field experiments conducted over multiple seasons and years, the thesis presents key practical insights necessary for commercial development of an information gathering system in orchards

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

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