2,109 research outputs found

    Definition and Application of a Computational Parameter for the Quantitative Production of Hydroponic Tomatoes Based on Artificial Neural Networks and Digital Image Processing.

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    This work presents an alternative method, referred to as Productivity Index or PI, to quan tify the production of hydroponic tomatoes using computer vision and neural networks, in contrast to other well-known metrics, such as weight and count. This new method also allows the automation of processes, such as tracking of tomato growth and quality control. To compute the PI, a series of computational processes are conducted to calculate the total pixel area of the displayed tomatoes and obtain a quantitative indicator of hydroponic crop production. Using the PI, it was possible to identify objects belonging to hydroponic tomatoes with an error rate of 1.07%. After the neural networks were trained, the PI was applied to a full crop season of hydroponic tomatoes to show the potential of the PI to monitor the growth and maturation of tomatoes using different dosages of nutrients. With the help of the PI, it was observed that a nutrient dosage diluted with 50% water shows no difference in yield when compared with the use of the same nutrient with no dilution.CONACYT - Consejo Nacional de Ciencia y TecnologíaPROCIENCI

    Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

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    Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy

    A Generic ROS-Based Control Architecture for Pest Inspection and Treatment in Greenhouses Using a Mobile Manipulator

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    To meet the demands of a rising population greenhouses must face the challenge of producing more in a more efficient and sustainable way. Innovative mobile robotic solutions with flexible navigation and manipulation strategies can help monitor the field in real-time. Guided by Integrated Pest Management strategies, robots can perform early pest detection and selective treatment tasks autonomously. However, combining the different robotic skills is an error prone work that requires experience in many robotic fields, usually deriving on ad-hoc solutions that are not reusable in other contexts. This work presents Robotframework, a generic ROS-based architecture which can easily integrate different navigation, manipulation, perception, and high-decision modules leading to a faster and simplified development of new robotic applications. The architecture includes generic real-time data collection tools, diagnosis and error handling modules, and user-friendly interfaces. To demonstrate the benefits of combining and easily integrating different robotic skills using the architecture, two flexible manipulation strategies have been developed to enhance the pest detection in its early state and to perform targeted spraying in simulated and field commercial greenhouses. Besides, an additional use-case has been included to demonstrate the applicability of the architecture in other industrial contexts.This work was supported in part by the GreenPatrol European Project through the European GNSS Agency by the European Union's (EU) Horizon 2020 Research and Innovation Program under Grant 776324 [11]. Documen

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

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    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

    An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

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    A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and plant segmentation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.Comment: 35 pages, 8 figures, Preprint submitted to PLoS On

    Precision Agriculture Technology for Crop Farming

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    This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

    Systemwide Review of Plant Breeding Methodologies in the CGIAR

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    Report of the systemwide review of plant breeding methodologies in the CGIAR conducted in 2000 by a panel chaired by Donald N. Duvick. The document includes an excerpt from the summary of CGIAR International Centers Week 2000, a transmittal letter from TAC Chair Emil Javier, TAC's commentary, and a transmittal letter from the panel chair.The study was based on sub-reports for nine centers, which were made available through the CGIAR website. There were six main findings:1. centers are using traditional techniques effectively and efficiently;2. new tools are used effectively, but will not replace traditional methods in the short term;3. biotechnology will increase efficiency and effectiveness but cost more;4. centers are outsourcing biotechnology effectively but should do it more;5. more financial support is needed for germplasm research and mechanisms that hinder intercenter collaboration should be changed;6. better intercenter collaboration, consolidation, and even centralization could increase effectiveness.The Group endorsed these recommendations.There are nine annexes covering among other things: breeding methods for CGIAR commodities, biotechnology methods used at centers, resource commitments, and CGIAR-NARS interactions in plant breeding and biotechnology

    Machine learning based anomaly detection for industry 4.0 systems.

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    223 p.This thesis studies anomaly detection in industrial systems using technologies from the Fourth Industrial Revolution (4IR), such as the Internet of Things, Artificial Intelligence, 3D Printing, and Augmented Reality. The goal is to provide tools that can be used in real-world scenarios to detect system anomalies, intending to improve production and maintenance processes. The thesis investigates the applicability and implementation of 4IR technology architectures, AI-driven machine learning systems, and advanced visualization tools to support decision-making based on the detection of anomalies. The work covers a range of topics, including the conception of a 4IR system based on a generic architecture, the design of a data acquisition system for analysis and modelling, the creation of ensemble supervised and semi-supervised models for anomaly detection, the detection of anomalies through frequency analysis, and the visualization of associated data using Visual Analytics. The results show that the proposed methodology for integrating anomaly detection systems in new or existing industries is valid and that combining 4IR architectures, ensemble machine learning models, and Visual Analytics tools significantly enhances theanomaly detection processes for industrial systems. Furthermore, the thesis presents a guiding framework for data engineers and end-users

    First External Programme and Management Review of ILRI

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    First External Program and Management Review (EPMR) of ILRI, carried out between September 1998 and March 1999 by a panel chaired by Samuel Jutzi. The document also contains an excerpt from the report of the CGIAR 1999 Mid Term Meeting, a transmittal from the TAC Chair and CGIAR Executive Secretary, TAC's commentary, ILRI's response, and a transmittal from the review panel chair. The report was favorably reviewed by an ad hoc committee, whose report was adopted by the CGIAR.There was unanimous praise for the manner in which ILRAD and ILCA had been combined in 1995 into ILRI, with some aspects cited as models in case of future mergers. TAC suggested that the social science research at ILRI was not covered thoroughly, but the report was otherwise complete. It found ILRI's formal report incomplete. The CGIAR asked ILRI to make a report at ICW 1999 on progress in implementing the fourteen recommendations of the review and those of the ad hoc committee. The committee asked ILRI to communicate with CGIAR members and others engaged in livestock research to sharpen its answers to strategic issues concerning ILRI research. This corresponded with a panel recommendation that IFPRI sharpen its priorities, and focus on fewer areas to ensure scientific quality and increased impact. A Deputy Director General to oversee the research program was recommended, as part of a realignment of ILRI's research structure.The panel recommended a more proactive role for the board in the strategic planning area, and a sharper delineation of the line between board and management responsibilities. It also suggested that networks should emphasize collaborative research with NARS as opposed to capacity building.The report was an agenda document at TAC 76, and the CGIAR Mid-term meeting in May 1999
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