Electrical impedance and image analysis methods in detecting and measuring Scots pine heartwood from a log end during tree harvesting

Scots pine (Pinus sylvestris L.) heartwood is naturally durable wood material which has not been fully utilized in the wood industry. Currently, there are no practical measurement methods for detecting and measuring heartwood in a tree harvesting. The objective of this study was to evaluate the applicability of an electrical impedance spectroscopy and an image analysis of a log end face for pine heartwood measurements from the harvesting perspective. Both methods were tested with a fresh wood material which was collected during the harvesting operations. The results indicate that both methods have potential to measure the heartwood from processed stems with an average heartwood diameter error being less than two centimeters for each method. However, the image analysis of the log end face is only appropriate when visible contrast between the heartwood and a sapwood exists. Our findings indicate that the studied heartwood detection methods show great potential in measuring the heartwood of the stem in the harvesting phase which would ideally benefit later links in wood value chains.Peer reviewe

Development of a sensor-based harrowing system using digital image analysis to achieve a uniform weed control selectivity in cereals

Using intelligent sensor technology for site-specific weed control can increase the efficacy of traditional weed control implements. Several scientific studies successfully used intelligent sensors for automatic harrow control by taking many different parameters into account such as weed density, soil resistance factor, and plant growth. However, none of the systems was practically feasible because these factors made the control system too complex and unattractive for farmers. Defining only one parameter (crop soil cover) instead of many provides a new and simple approach which was investigated in this work. The first scientific publication focuses on the development, practical implementation and testing of the automatic harrow control system. Two RGB-cameras were mounted before and after the harrow and constantly monitored crop cover. The CSC was then computed out of these resulting images. The image analysis, decision support system and automatic control of harrowing intensity by hydraulic adjustment of the tine angle were installed on a controller which was mounted on the harrow. Eight field experiments were carried out in spring cereals. Mode of harrowing intensity was changed in four experiments by speed, number of passes and tine angle. Each mode was varied in five intensities. In four experiments, only the intensity of harrowing was changed. Modes of intensity were not significantly different among each other. However, intensity had significant effects on WCE and CSC. Cereal plants recovered well from 10% CSC, and selectivity was in the constant range at 10% CSC. Therefore, 10% CSC was the threshold for the decision algorithm. If the actual CSC was below 10% CSC, intensity was increased. If the actual CSC was higher than 10%, intensity was decreased. The new system was tested in an additional field study. Threshold values for CSC were set at 10%, 30% and 60%. Automatic tine angle adjustment precisely realised the three different CSC values with variations of 1.5% to 3%. The next publication discussed and assessed the site-specific field adaptation of the development in cereals. In 2020, three field experiments were conducted in winter wheat and spring oats to investigate the response of the weed control efficacy and the crop to different harrowing intensities, in southwest Germany. In all experiments, six levels of CSC were tested. Each experiment contained an untreated control and an herbicide treatment as a comparison to the harrowing treatments. The results showed an increase in the WCE with an increasing CSC threshold. Difficult-to-control weed species such as Cirsium arvense (L.) and Galium aparine (L.) were best controlled with a CSC threshold of 70%. With a CSC threshold of 20% it was possible to control up to 98% of Thlaspi arvense (L.) The highest crop biomass, grain yield, and selectivity were achieved with an CSC threshold of 2025% at all trial locations. With this harrowing intensity, grain yields were higher than in the herbicide control plots and a WCE of 6898% was achieved. The last scientific article compares pairwise a conventional harrow intensity with automatic sensor-based harrowing intensity. Five field experiments in cereals were conducted at three locations in southwestern Germany in 2019 and 2020 to investigate if camera-based harrowing resulted in a more homogenous CSC and higher WCE, biomass, and crop grain yield than a conventional harrow with a constant intensity across the whole plot. For this purpose, pairwise comparisons of three fixed harrowing intensities (10 °, 40 °, and 70 ° tine angle) and three predefined CSC thresholds (CSC of 10%, 20%, and 60%) were realized in randomized complete block designs. Camera-based adjustment of the intensity resulted in 6-16% less standard deviation variation of CSC compared to fixed settings of tine angle. Crop density, WCE, crop biomass and grain yield were significantly higher for camera-based harrowing than for conventional harrowing. WCE and yields of all automatic adjusted harrowing treatments were equal to the herbicide control plots. In this PhD-thesis, a sensor-based harrow was developed and successfully investigated as an alternative to conventional herbicide application in cereals. A permanent, equal replacement of chemical weed control in arable farming systems can only be achieved using modern, sensor-based mechanical weed control approaches. Therefore, the efficacy of the mechanical weed control method can be improved and increased continuously. It has been shown that the precise adjustment of mechanical weed control methods to site-specific weed conditions allows similar WCE results as an herbicide application without causing yield losses. These findings contribute towards modern plant protection strategies to reduce the herbicide use and to establish the acceptance of technical progress in society.Durch den Einsatz von Sensortechnik bei der mechanischen Unkrautbekämpfung, kann die Effektivität und Auslastung der Maschinen gesteigert werden. Die automatische Anpassung der Striegelintensität durch Sensoren, wurde bereits in zahlreichen wissenschaftlichen Arbeiten untersucht, jedoch hat sich keines der Systeme auf dem Markt etablieren können. Bei diesen Striegel-Prototypen wurden verschiedenste Parameter wie beispielsweise die Unkrautdichte, der Bodenwiderstand oder das Pflanzenwachstum berücksichtigt. Allerdings konnten bisherige Systeme nur einzelne Einflussfaktoren berücksichtigen und sie eigneten sich daher nicht für den großflächigen praktischen Einsatz. Dieses Problem kann umgangen werden, wenn der Fokus auf die gezielte Verschüttung von Unkräutern und Kulturpflanze (CSC) gerichtet wird. Diese neue Vorgehensweise bei der sensorgesteuerten Striegeltechnik bietet einen einfachen und zugleich umfangreichen Ansatz, der alle Pflanzen-, Boden- und Unkrautrelevanten Parameter berücksichtigt und abdeckt. Die erste wissenschaftliche Veröffentlichung befasst sich mit der Entwicklung und einem praktischen Test der automatischen Striegelsteuerung. Hierzu wurden zwei RGB-Kameras vor und hinter dem Striegel angebracht, um den tatsächlichen CSC-Wert zu messen, berechnen und mit einem vorgegebenen CSC-Schwellenwert abgleichen zu können. Die Ansteuerung erfolgte im Anschluss hydraulisch, durch elektrische Signale an das Magnetventil. Wurde der vorgegebene CSC-Schwellenwert unterschritten, wurde die Striegelintensität erhöht. War der tatsächliche, gemessene CSC-Wert über dem vorgegebenen Schwellenwert, wurde die Intensität verringert. In einem ersten Feldexperiment wurde das neue System auf Genauigkeit getestet und mit einem manuell eingestellten Zinkenwinkel verglichen. Hierbei wurden für das automatische System CSC-Schwellenwerte von 10%, 30% und 60% und für die manuelle Ansteuerung drei äquivalente Zinkenwinkel, festgelegt. Es konnten die voreingestellten, automatischen CSC-Schwellenwerte mit einer Präzision bzw. Standardabweichung von 1,5-3% realisiert werden, während die manuell eingestellten Zinkenwinkel eine Standardabweichung zwischen 18-20% aufwiesen. Die nächste Veröffentlichung befasst sich mit der standortspezifischen Feldanpassung des sensorbasierten Striegels im Getreide. Drei Feldexperimente wurden durchgeführt, um die Auswirkungen der Feldanpassung sowohl im Sommer-, als auch Wintergetreide zu untersuchen. In allen Experimenten wurden sechs verschiedene CSC-Schwellenwerte (CSC von 5, 15, 20, 25, 45 und 70%) getestet. Jedes Experiment enthielt eine unbehandelte Kontrolle und eine Herbizidbehandlung als Vergleich. Die Ergebnisse zeigten einen Anstieg des WCE mit einem steigenden CSC-Schwellenwert. Schwer zu bekämpfende Unkrautarten wie Cirsium arvense (L.) und Galium aparine (L.) wurden am besten mit einem CSC-Schwellenwert von 70% kontrolliert. Mit einem CSC-Schwellenwert von 20% war es möglich, bis zu 98% von Thlaspi arvense (L.) zu bekämpfen. Die höchste Kulturpflanzenbiomasse und Kornertrag konnte mit einem CSC-Schwellenwert zwischen 20-25% erzielt werden. Kornertrag war sogar höher als in der Herbizidparzelle. Der letzte wissenschaftliche Artikel vergleicht paarweise eine konventionelle Striegelintensität mit einer automatischen, sensorbasierten Striegelsteuerung. In den Jahren 2019 und 2020 wurden dafür fünf Feldversuche in Getreide an drei Standorten in Südwestdeutschland durchgeführt. Zu diesem Zweck wurden paarweise Vergleiche von drei festen Striegelintensitäten (10 °, 40 ° und 70 ° Zinkenwinkel) und drei vordefinierten CSC-Schwellenwerten (CSC von 10%, 20% und 60%) durchgeführt. Die automatische Anpassung der Intensität führte zu einer 6-16% geringeren Standardabweichung des CSC im Vergleich zu den festen Einstellungen des Zinkenwinkels. Pflanzendichte, WCE, Pflanzenbiomasse und Kornertrag waren beim kamerabasierten Striegeln signifikant höher als beim konventionellen Striegeln. WCE und Erträge aller automatisch eingestellten Striegelbehandlungen waren vergleichbar mit den Herbizid-Kontrollparzellen. In dieser Dissertation wurde ein sensorbasiertes Striegelsystem entwickelt und als erfolgreiche Alternative zur konventionellen Herbizidanwendung und zur Effektivitätssteigerung herkömmlicher Striegel, im Getreide aufgezeigt. Durch eine präzise Anpassung an standortspezifische Unkraut- und Bodensituationen waren nicht nur die Bekämpfungserfolge vergleichbar mit denen einer Herbizidanwendung, sondern es entstanden auch nur geringfügige Pflanzen- bzw. Ertragsverluste. Die Ergebnisse dieser Arbeit haben das Potential Einfluss auf zukünftige Pflanzenschutzstrategien zu nehmen, da sie die Möglichkeiten der mechanischen Unkrautbekämpfung erweitern, den Herbizideinsatz reduzieren und die Akzeptanz des technischen Fortschritts in der Gesellschaft fördern

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