Design and development of an unmanned aerial and ground vehicles for precision pesticide spraying

Günümüzde, bitki hastalıkları tarımsal üretimi etkileyen önemli sorunlardan birisi olarak karşımıza çıkmaktadır. Bitkileri hastalıklardan ve zararlı otların etkilerinden korumak hem tarımda üretimi artırmak hem de tarımın kalitesini yükseltmek için büyük önem taşımaktadır. Tarımsal ürünler, ülkemizde ve dünyada çeşitli ilaçlama yöntemleri kullanılarak korunabilmektedir. Bu yöntemlerin başında gelen ilaçlama yolu ile bitki koruma yöntemi üretimin kalitesini geliştirmek ve rekolteyi artırmak amacıyla yaygın olarak kullanılmaktadır. Ancak bitkilerin korunmasında uygulanan geleneksel ilaçlama yöntemlerinin bitkilere ve toprağa büyük ölçüde zarar verdiği gözlenmektedir. Son yıllarda gelişmiş ülkelerdeki tarımsal uygulamalarda robotların kullanımı hızla artmakta, tarımsal alanlarda özellikle uzaktan algılama ve hassas tarım çalışmalarında bu robotların kullanıldığı görülmektedir. Dahası, tarımsal üretimde yararlanılan fayda-maliyet oranı da dikkate alındığında, günümüzde hassas tarım uygulamalarında robotların kullanılmasının kaçınılmaz hale geldiği anlaşılmaktadır. Günümüz gereksinimleri ve gelişen teknoloji göz önüne alınarak planlanmış olan bu çalışmada, ülkemizde yaygın olarak kullanılan tarımsal mücadele yöntemlerinin maliyetlerini, tarımsal üretimin miktarını ve kalitesini önemli ölçüde etkileyecek geleneksel ilaçlama yöntemlerine alternatif olabilecek bir tarımsal mücadele sistemi geliştirilmiştir. Çalışmada, yakın mesafeden doğrudan hedeflenen bitki üzerine ilaçlama yapılması, ilaçlama sırasında toprağa ve bitkilere verilen zararın en aza indirgenmesi hedeflenmiştir. Bu doğrultuda, özgün tasarım multispektral kamera, ilaçlama ünitesi, Yer Kontrol İstasyonu (YKİ) ve eşgüdümlü çalışabilen İnsansız Hava Aracı (İHA) ile İnsansız Yer Aracından (İYA) oluşan tarımsal mücadele mekanizması tasarlanmış ve geliştirilmiştir. Bu mekanizma, tarımsal ilaçlama uygulamaları için geleneksel yöntemlere kıyasla daha ileri düzey bir alternatif yöntem olarak ortaya çıkmaktadır.TABLE OF CONTENTS ÖZET ................................................................................................................ vii ABSTRACT ....................................................................................................... ix ACKNOWLEDGEMENTS ................................................................................ xi 1 . INTRODUCTION .......................................................................................... 1 2. LITERATURE REVIEW ............................................................................. 6 2.1 Robotics ..................................................................................................... 9 2.2 Unmanned Ground Vehicles ..................................................................... 11 2.3 Unmanned Aerial Vehicles ....................................................................... 11 2.4 Remote Sensing Technology .................................................................... 17 2.4.1 Remote Sensing Platforms ................................................................. 19 2.4.2 Plant Disease Detection ..................................................................... 22 2.4.3 Normalized Difference Vegetation Index ........................................... 27 3 . MATERIAL AND METHOD ....................................................................... 29 3.1 Ground Control Station ............................................................................ 32 3.2 Unmanned Ground Vehicle ...................................................................... 37 3.2.1 Specifications of the UGV ................................................................. 38 3.2.2 The Chassis and Sensor Holder .......................................................... 40 3.2.3 FEM Analysis .................................................................................... 43 3.3 Multispectral Camera for Plant Disease Detection .................................... 44 3.3.1 Spectral Imaging ................................................................................ 46 3.3.2 Multispectral Camera – Spektra TSL128RN ...................................... 47 3.3.3 The hardware of the Device ............................................................... 49 3.3.4 Calibrating Steps of the Device .......................................................... 52 3.3.5 Software for the Device ..................................................................... 56 3.3.6 Measurements using NDVI Devices .................................................. 58 3.4 Unmanned Aerial Vehicle ........................................................................ 62 3.4.1 The Chassis and Arm ......................................................................... 66 3.4.2 FEM Analysis ................................................................................... 69 3.4.3 Modal Analysis ................................................................................. 70 3.4.4 Performance of the Propellers ............................................................ 73 3.4.5 Flight Duration and Maximum Conditions ......................................... 82 3.4.6 Strain Measurement ........................................................................... 84 3.4.7 Other Parts ........................................................................................ 92 3.4.8 Specifications of the UAV ................................................................. 95 3.4.9 Flight Tests ....................................................................................... 96 3.5 Spraying Unit –Sprayer and Tank ............................................................. 99 4 . RESULTS AND DISCUSSION .................................................................. 103 4.1 The UGV ............................................................................................... 103 4.2 The Multispectral Camera ...................................................................... 105 4.3 The UAV ............................................................................................... 115 4.4 The Sprayer............................................................................................ 135 xv 4.5 UGV and Multispectral Camera .............................................................. 138 4.6 Aerial Spraying UAV ............................................................................. 145 5 . CONCLUSIONS......................................................................................... 154 REFERENCES ................................................................................................ 156 RESUME......................................................................................................... 16

Measurement of micro burr and slot widths through image processing: Comparison of manual and automated measurements in micro‐milling

In this study, the burr and slot widths formed after the micro‐milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user‐defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open‐sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user‐friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation

A Battery-Powered Fluid Manipulation System Actuated by Mechanical Vibrations

Miniaturized fluid manipulation systems are an important component of lab-on-a-chip platforms implemented in resourced-limited environments and point-of-care applications. This work aims to design, fabricate, and test a low-cost and battery-operated microfluidic diffuser/nozzle type pump to enable an alternative fluid manipulation solution for field applications. For this, CNC laser cutting and 3D printing are used to fabricate the fluidic unit and casing of the driving module of the system, respectively. This system only required 3.5-V input power and can generate flow rates up to 58 µL/min for water. In addition, this portable pump can manipulate higher viscosity fluids with kinematic viscosities up to 24 mPa·s resembling biological fluids such as sputum and saliva. The demonstrated system is a low-cost, battery-powered, and highly versatile fluid pump that can be adopted in various lab-on-a-chip applications for field deployment and remote applications

A Multi-Flow Production Line for Sorting of Eggs Using Image Processing

In egg production facilities, the classification of eggs is carried out either manually or by using sophisticated systems such as load cells. However, there is a need for the classification of eggs to be carried out with faster and cheaper methods. In the agri-food industry, the use of image processing technology is continuously increasing due to the data processing speed and cost-effective solutions. In this study, an image processing approach was used to classify chicken eggs on an industrial roller conveyor line in real-time. A color camera was used to acquire images in an illumination cabinet on a motorized roller conveyor while eggs are moving on the movement halls. The system successfully operated for the grading of eggs in the industrial multi-flow production line in real-time. There were significant correlations among measured weights of the eggs after image processing. The coefficient of linear correlation (R2) between measured and actual weights was 0.95

Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation

Measurement of Micro Burr and Slot Widths through Image Processing: Comparison of Manual and Automated Measurements in Micro-Milling

In this study, the burr and slot widths formed after the micro-milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user-defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open-sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user-friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation

Image Processing of Mg-Al-Sn Alloy Microstructures for Determining Phase Ratios and Grain Size and Correction with Manual Measurement

The study of microstructures for the accurate control of material properties is of industrial relevance. Identification and characterization of microstructural properties by manual measurement are often slow, labour intensive, and have a lack of repeatability. In the present work, the intermetallic phase ratio and grain size in the microstructure of known Mg-Sn-Al alloys were measured by computer vision (CV) technology. New Mg (Magnesium) alloys with different alloying element contents were selected as the work materials. Mg alloys (Mg-Al-Sn) were produced using the hot-pressing powder metallurgy technique. The alloys were sintered at 620 °C under 50 MPa pressure in an argon gas atmosphere. Scanning electron microscopy (SEM) images were taken for all the fabricated alloys (three alloys: Mg-7Al-5Sn, Mg-8Al-5Sn, Mg-9Al-5Sn). From the SEM images, the grain size was counted manually and automatically with the application of CV technology. The obtained results were evaluated by correcting automated grain counting procedures with manual measurements. The accuracy of the automated counting technique for determining the grain count exceeded 92% compared to the manual counting procedure. In addition, ASTM (American Society for Testing and Materials) grain sizes were accurately calculated (approximately 99% accuracy) according to the determined grain counts in the SEM images. Hence, a successful approach was proposed by calculating the ASTM grain sizes of each alloy with respect to manual and automated counting methods. The intermetallic phases (Mg17Al12 and Mg2Sn) were also detected by theoretical calculations and automated measurements. The accuracy of automated measurements for Mg17Al12 and Mg2Sn intermetallic phases were over 95% and 97%, respectively. The proposed automatic image processing technique can be used as a tool to track and analyse the grain and intermetallic phases of the microstructure of other alloys such as AZ31 and AZ91 magnesium alloys, aluminium, titanium, and Co alloys

Demographics of patients with heart failure who were over 80 years old and were admitted to the cardiology clinics in Turkey

WOS: 000468584300005PubMed ID: 30930455Objective: Heart failure (HF) has a high prevalence and mortality rate in elderly patients; however, there are few studies that have focused on patients older than 80 years. The aim of this study is to describe and compare the age-specific demographics and clinical features of Turkish elderly patients with HF who were admitted to cardiology clinics. Methods: The Epidemiology of Cardiovascular Disease in Elderly Turkish population (ELDER-TURK) study was conducted in 73 centers in Turkey, and it recruited a total of 5694 patients aged 65 years or older. In this study, the clinical profile of the patients who were aged 80 years or older and those between 65 and 79 years with HF were described and compared based on the ejection fraction (EF)-related classification: HFrEF and HFpEF (is considered as EF: >= 50%). Results: A total of 1098 patients (male, 47.5%; mean age, 83.5 +/- 3.1 years) aged 80 years and 4596 patients (male, 50.2 %; mean age, 71.1 +/- 4.31 years) aged 65-79 years were enrolled in this study. The prevalence of HF was 39.8% for patients who were >= 80 years and 27.1% for patients 65-79 years old. For patients aged >= 80 years with HF, the prevalence rate was 67% for hypertension (HT), 25.6% for diabetes mellitus (DM), 54.3% for coronary artery disease (CAD), and 42.3% for atrial fibrilation. Female proportion was lower in the HFrEF group (p=0.019). The prevalence of HT and DM was higher in the HFpEF group (p= 80 years with HFrEF (p<0.01). Conclusion: HF is common in elderly Turkish population, and its frequency increases significantly with age. Females, diabetics, and hypertensives are more likely to have HFpEF, whereas CAD patients are more likely to have HFrEF.Turkish Society of CardiologyThis study was supported by Turkish Society of Cardiology

Demographics of patients with heart failure who were over 80 years old and were admitted to the cardiology clinics in Turkey

Objective: Heart failure (HF) has a high prevalence and mortality rate in elderly patients; however, there are few studies that have focused on patients older than 80 years. The aim of this study is to describe and compare the age-specific demographics and clinical features of Turkish elderly patients with HF who were admitted to cardiology clinics. Methods: The Epidemiology of Cardiovascular Disease in Elderly Turkish population (ELDER-TURK) study was conducted in 73 centers in Turkey, and it recruited a total of 5694 patients aged 65 years or older. In this study, the clinical profile of the patients who were aged 80 years or older and those between 65 and 79 years with HF were described and compared based on the ejection fraction (EF)-related classification: HFrEF and HFpEF (is considered as EF: >= 50\%). Results: A total of 1098 patients (male, 47.5\%; mean age, 83.5 +/- 3.1 years) aged 80 years and 4596 patients (male, 50.2 \%; mean age, 71.1 +/- 4.31 years) aged 65-79 years were enrolled in this study. The prevalence of HF was 39.8\% for patients who were >= 80 years and 27.1\% for patients 65-79 years old. For patients aged >= 80 years with HF, the prevalence rate was 67\% for hypertension (HT), 25.6\% for diabetes mellitus (DM), 54.3\% for coronary artery disease (CAD), and 42.3\% for atrial fibrilation. Female proportion was lower in the HFrEF group (p=0.019). The prevalence of HT and DM was higher in the HFpEF group (p<0.01), whereas CAD had a higher prevalence in the HFrEF group (p=0.02). Among patients aged 65-79 years, 43.9\% (548) had HFpEF, and 56.1\% (700) had HFrEF. In this group of patients aged 65-79 years with HFrEF, the prevalence of DM was significantly higher than in patients aged >= 80 years with HFrEF (p<0.01). Conclusion: HF is common in elderly Turkish population, and its frequency increases significantly with age. Females, diabetics, and hypertensives are more likely to have HFpEF, whereas CAD patients are more likely to have HFrEF