Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Soil structure exploration and measurement of its macroscopic behavior for a better understanding of the soil hydropedodynamic functionalities

Air permeability and water conductivity are fundamental physical properties when it comes to the soil functions across the environment. The water conductivity and the air permeability as functions of the soil’s degree of saturation (K(θ) and ka(ɛ), respectively) are only discretely measurable, and the use of models is necessary to obtain continuous expressions of these functions. Most models however consider the soil pore network structure as a fitting parameter although it is public knowledge that K(θ) and ka(ɛ) depend mostly on the soil microstructure, which is, none the less, unique between samples with homogeneous texture. New ways of studying K(θ) and ka(ɛ) are needed. The direct soil pore space visualization is a promising avenue to lead us to objectifying soil physics. The X-ray microtomographic technique (X-ray µCT) is now widely used by soil scientists and delivers 3D grayscale images of objects composed by materials of different densities. When dealing with a porous medium such as the natural soil, the X-ray µCT images need to be cautiously and expertly processed to obtain realistic feature quantification. A parallel, but however perquisite, objective of this dissertation is to statistically compare the effects of various image processing on the final X-ray µCT image features quantification. We simulated grayscale images to be processed to conclude about the image processing methodology we applied in our research. The overall objective of this dissertation is to explore the relationships between one microscopic soil structure (the volume of the smallest visible pore is 0.0004 mm³) and its macroscopic functionalities, such as its water conductivity and air permeability. More specifically, we confirmed that the use of 3D X-ray µCT data enables a better estimation of the soil water retention curve near saturation through the identification of the largest soil pores. These are indeed often by-passed with pressure plate’s laboratory measurements because of various artefacts. We also identified microscopic pore space morphological parameters that explained the soil saturated hydraulic conductivity, and microscopic porosity distribution measures that explained the soil air permeability. The final X-ray µCT image features quantification depends on the applied image processing, as stated, but also, clearly, on the image resolution. We concluded that working with a higher resolution would not necessarily lead to a higher degree of knowledge because resolution is sample-size dependent, and one pore size distribution could moreover be sufficiently visible at low resolution. We however observed that the pore network morphological and topological connectivity increases with resolution. Finally, we highlighted the imperfections of the capillary theory applied to soil through scanning the same soil samples at various water contents. As hypothesized, the pore network connectivity seems to play an important role in the pore accessibility to draining. After having studied the effects of the soil pore network structure on the soil hydrodynamic properties, we turned the question around and evaluated the effects of the chemical soil composition (organic carbon and free forms of iron) on the very same soil pore network structure. This dissertation therefore discusses the advantages and limitations of the use of X-ray microtomography to study soils for a more realistic understanding of the soil hydropedodynamic processes

Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

Microstructural changes in thermochemical heat storage material over cycles:Insights from micro-X-ray computed tomography

This paper studies the effect of successive (de)hydration cycles on the structure of potassium carbonate K2CO3·1.5H2O grains for low-temperature heat storage applications. Such structural changes are caused by exposure of the salt to water vapor or removal of water from it, accompanied by successive swelling and shrinkage. Understanding the material's internal structure is key to predicting its behaviour and optimizing its design. However, due to the simultaneous and persistent occurrence of structural changes and transport mechanisms throughout the process, gaining a complete understanding of the phenomenon can be challenging. Unlike conventional experimental approaches and two-dimensional imaging techniques used for porosity assessment, our study showcases the qualitative and quantitative alterations in the porosity and microstructure of potassium carbonate. This analysis is achieved by using Micro-X-ray computed tomography (Micro-CT). The study focuses on the impact of cycling on grain microstructure, investigating pore volume distribution, radial variation of pore sizes, and density of individual grains. It was noted that the porosity increased from 6.4 % to 19.7 % after seven cycles. Initially, we observed a greater number of pores in the core of the uncycled salt grain. However, after cycling, we noticed a more even distribution, with a higher number of pores in the outer region of the grain, which caused a radial change in porosity. Lastly, this research provides the intrinsic and apparent densities of both non-cycled and cycled potassium carbonate specimens. Micro-CT is a good tool for a better understanding of changes in thermochemical material at a structural level. Calculation of porosity provided a pathway to calculate apparent and intrinsic density. The demonstrated method can be used for a wide range of salt hydrates, enhancing the scope and applicability of this study in the field of low-temperature heat storage applications. Additionally, it gives the measuring parameter needed to calculate energy density and change in volume during the reaction.</p

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots. In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually. A literature review on automated drusen detection showed that the works already published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques. This methodology is based on a detection and modelling algorithm to automatically quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the image, which is used to compute the affected areas. To validate the methodology, two software applications, one for semi-automated (MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have taken place. These compared the automated method to the specialists and evaluated its repeatability. The studies were analysed regarding several indicators, which were based on the total affected area and on a pixel-to-pixel analysis. Due to the high variability among the graders involved in the first study, a new evaluation method, the Weighed Matching Analysis, was developed to improve the pixel-to-pixel analysis by using the statistical significance of the observations to differentiate positive and negative pixels. From the results of these studies it was concluded that the methodology proposed is capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image segmentation,image modelling and images comparison, which are also applicable to other image processing fields

Inter-comparison of quantitative imaging of lutetium-177 (177Lu) in European hospitals

Background: This inter-comparison exercise was performed to demonstrate the variability of quantitative SPECT/CT imaging for lutetium-177 (177Lu) in current clinical practice. Our aim was to assess the feasibility of using international inter-comparison exercises as a means to ensure consistency between clinical sites whilst enabling the sites to use their own choice of quantitative imaging protocols, specific to their systems. Dual-compartment concentric spherical sources of accurately known activity concentrations were prepared and sent to seven European clinical sites. The site staff were not aware of the true volumes or activity within the sources—they performed SPECT/CT imaging of the source, positioned within a water-filled phantom, using their own choice of parameters and reported their estimate of the activities within the source. Results: The volumes reported by the participants for the inner section of the source were all within 29% of the true value and within 60% of the true value for the outer section. The activities reported by the participants for the inner section of the source were all within 20% of the true value, whilst those reported for the outer section were up to 83% different to the true value. Conclusions: A variety of calibration and segmentation methods were used by the participants for this exercise which demonstrated the variability of quantitative imaging across clinical sites. This paper presents a method to assess consistency between sites using different calibration and segmentation methods

Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification. Document type: Articl