1,138 research outputs found
Decision support for optimised irrigation scheduling
The system, developed under the FLOW-AID (an FP6 project), is a farm level water management system of special value in situations where the water availability and quality is limited. This market-ready precision irrigation management system features new models, hardware and software. The hardware platform delivers a maintenance-free low cost dielectric tensiometer and several low-end irrigation or fertigation controllers for serving different situations. The software includes a complete, web based, Decision Support System (DSS) that consists of an expert planner for farm zoning (MOPECO) and a universal irrigation scheduler, based on crop-water stress models (UNIPI) and water and nutrient uptake calculations. The system, designed also to service greenhouse fertigation and hydroponics, is scalable from one to many zones. It consists of 1) a data gathering tool which uploads agronomic data, from monitored crops around the world, to a central web Data Base (DB), and 2) a web based Decision Support System (DSS). The DSS processes intelligently the data of the crop using Crop Response Models, Nutrient Uptake Models and Water Uptake Models. The central system returns over Internet to the low-end controller a command file containing water scheduling and nutrient supply guideline
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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
HYDROPONIC DEVICES FOR GREEN FODDER PRODUCTION: A REVIEW
In traditional farming, plants require a lot of space (growing area), they consume a large amount of water, absorb a small percentage of nutrients in soil and are completely dependent on meteorological conditions. Therefore, growing crops in this way entails high costs and a high risk of invested funds. One of the measures to reduce these factors is the use of hydroponics.In the study six types of hydroponic systems (HS) plant constructions based on plant nutrient supply technology were reviewed: ebb and flow HS; nutrient film technique (NFT) HS; aeroponics; deep water culture HS; βwickβ HS and drip-irrigation HS. In addition, a review of the structural design of the hydroponic systems identified their advantages and disadvantages in green fodder production.The most promising technology for the cultivation of green fodder is the NFT HS. This cultivation technology is appreciated in feed production for its highly utilized growing room volume and closed-loop irrigation solution to plants, which allows it to be easily automated based on solution parameters. Seven farms already have this technology in place in Lithuania. In order to optimize hydroponic fodder cultivation technology, it is expedient to improve NFT equipment and process control systems
Automated System that Monitors and Controls the pH and Electrical Conductivity of a Closed-Hydroponic Setup
The Automated Closed Hydroponics System relates to a system that monitors the pH and electrical conductivity (EC) of the nutrient solution. It is constructed to modify and optimize the conventional hydroponic system as determined through its specific operational requirements to utilize the system safely and profitably. The modules are electronically integrated into the system that continuously monitors the pH level and the EC level of the nutrient solution and automatically adjusts its content to the proper range suitable for the plant used. The developed system lessens human involvement, and in turn, eliminates human error while keeping expenses at a minimum and ensuring yield at maximum. This article discusses the operation of the system which consists of the reservoirs for the dispensing of acid, base, nutrient solution, and water, with its necessary valves, motors, hose, and flow regulators; level sensors installed to the reservoirs to monitor the refilling requirement and operational conditions; pH monitoring and control; electrical conductivity (EC) monitoring and control; and, the nutrient uptake analysis to illustrate the plant nutritional status verifying that the plants were taking up the nutrient balance they require when the pH and EC are on their respective exact levels. The automation in the hydroponic system results in a more balanced culture, creating healthier and more homogeneous leaves
Wireless ICT monitoring for hydroponic agriculture
It is becoming increasingly evident that agriculture is playing a pivotal role in the socio-economic development of South Africa. The agricultural sector is important because it contributes approximately 2% to the gross domestic product of the country. However, many factors impact on the sustainability of traditional agriculture in South Africa. Unpredictable climatic conditions, land degradation and a lack of information and awareness of innovative farming solutions are among the factors plaguing the South African agricultural landscape. Various farming techniques have been looked at in order to mitigate these challenges. Among these interventions are the introduction of organic agriculture, greenhouse agriculture and hydroponic agriculture, which is the focus area of this study. Hydroponic agriculture is a method of precision agriculture where plants are grown in a mineral nutrient solution instead labour- intensive activity that requires an incessant monitoring of the farm environment in order to ensure a successful harvest. Hydroponic agriculture, however, presents a number of challenges that can be mitigated by leveraging the recent mobile Information and Communication Technologies (ICTs) breakthroughs. This dissertation reports on the development of a wireless ICT monitoring application for hydroponic agriculture: HydroWatcher mobile app. HydroWatcher is a complex system that is composed of several interlacing parts and this study will be focusing on the development of the mobile app, the front-end of the system. This focus is motivated by the fact that in such systems the front-end, being the part that the users interact with, is critical for the acceptance of the system. However, in order to design and develop any part of HydroWatcher, it is crucial to understand the context of hydroponic agriculture in South Africa. Therefore, complementary objectives of this study are to identify the critical factors that impact hydroponic agriculture as well as the challenges faced by hydroponic farmers in South Africa. Thus, it leads to the elicitation of the requirements for the design and development of HydroWatcher. This study followed a mixed methods approach, including interviews, observations, exploration of hydroponic farming, to collect the data, which will best enable the researcher to understand the activities relating to hydroponic agriculture. A qualitative content analysis was followed to analyse the data and to constitute the requirements for the system and later to assert their applicability to the mobile app. HydroWatcher proposes to couple recent advances in mobile technology development, like the Android platform, with the contemporary advances in electronics necessary for the creation of wireless sensor nodes, as well as Human Computer interaction guidelines tailored for developing countries, in order to boost the user experience
A lunar base reference mission for the phased implementation of bioregenerative life support system components
Previous design efforts of a cost effective and reliable regenerative life support system (RLSS) provided the foundation for the characterization of organisms or 'biological processors' in engineering terms and a methodology was developed for their integration into an engineered ecological LSS in order to minimize the mass flow imbalances between consumers and producers. These techniques for the design and the evaluation of bioregenerative LSS have now been integrated into a lunar base reference mission, emphasizing the phased implementation of components of such a BLSS. In parallel, a designers handbook was compiled from knowledge and experience gained during past design projects to aid in the design and planning of future space missions requiring advanced RLSS technologies. The lunar base reference mission addresses in particular the phased implementation and integration of BLS parts and includes the resulting infrastructure burdens and needs such as mass, power, volume, and structural requirements of the LSS. Also, operational aspects such as manpower requirements and the possible need and application of 'robotics' were addressed
Prospects in Agricultural Engineering in the Information Age - Technological Development for the Producer and the Consumer
Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is an Invited article from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 1 (1999): N. Sigrimis, Y. Hashimoto, A. Munack and J. De Baerdemaker. Prospects in Agricultural Engineering in the Information Age - Technological Development for the Producer and the Consumer
Decision-tree-based ion-specific dosing algorithm for enhancing closed hydroponic efficiency and reducing carbon emissions
The maintenance of ion balance in closed hydroponic solutions is essential to improve the crop quality and recycling efficiency of nutrient solutions. However, the absence of robust ion sensors for key ions such as P and Mg and the coupling of ions in fertilizer salts render it difficult to effectively manage ion-specific nutrient solutions. Although ion-specific dosing algorithms have been established, their effectiveness has been inadequately explored. In this study, a decision-tree-based dosing algorithm was developed to calculate the optimal volumes of individual nutrient stock solutions to be supplied for five major nutrient ions, i.e., NO3, K, Ca, P, and Mg, based on the concentrations of NO3, K, and Ca and remaining volume of the recycled nutrient solution. In the performance assessment based on five nutrient solution samples encompassing the typical concentration ranges for leafy vegetable cultivation, the ion-selective electrode array demonstrated feasible accuracies, with root mean square errors of 29.5, 10.1, and 6.1 mgΒ·L-1 for NO3, K, and Ca, respectively. In a five-step replenishment test involving varying target concentrations and nutrient solution volumes, the system formulated nutrient solutions according to the specified targets, exhibiting average relative errors of 10.6 Β± 8.0%, 7.9 Β± 2.1%, 8.0 Β± 11.0%, and 4.2 Β± 3.7% for the Ca, K, and NO3 concentrations and volume of the nutrient solution, respectively. Furthermore, the decision tree method helped reduce the total fertilizer injections and carbon emissions by 12.8% and 20.6% in the stepwise test, respectively. The findings demonstrate that the decision-tree-based dosing algorithm not only enables more efficient reuse of nutrient solution compared to the existing simplex method but also confirms the potential for reducing carbon emissions, indicating the possibility of sustainable agricultural development
Designing, fabrication and evaluation of a small-scale vertical hydroponic system to produce leafy vegetables
The agricultural productivity in Newfoundland and Labrador (NL) faces many challenges, including severe weather conditions, short growing seasons, and poor soil conditions. To address these challenges in NL, researchers should explore innovative methods like hydroponic farming to improve local food production. This study was conducted to design, fabricate, and evaluate a household hydroponic system capable of producing year-round leafy vegetables. A vertical hydroponic system was designed, fabricated and tested along with two other systems including 1) a vertical drip hydroponic system (G-DNA), 2) a vertical wick hydroponic system (C-Tree), and 3) a horizontal deep water culture (DWC) system as the control, under two growth conditions (a grow tent experiment and an experiment without a grow tent). The growth of spinach, water use efficiency (WUE) and nitrogen use efficiency (NUE) in three systems were tested. Results showed that the G-DNA system produced significantly higher spinach yield and outperformed the C-Tree hydroponic system. While G-DNA and C-Tree hydroponic systems had no significant effect on WUE, compared to the DWC system which demonstrated nearly twice the WUE. The G-DNA system exhibited the highest NUE in both environmental conditions, suggesting that spinach in the G-DNA system could absorb more nitrogen from the nutrient solution and yield more with the same amount of absorbed nitrogen compared to DWC and C-Tree systems. These findings indicate that the G-DNA system holds greater potential for improved NUE, and higher spinach yield compared to the C-Tree system. However, the G-DNA and C-Tree systems had no significant effect on WUE
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