Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies

In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts. In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes. Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset. To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods

Fuzzy-Logic Based Detection and Characterization of Junctions and Terminations in Fluorescence Microscopy Images of Neurons

Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process

Out-of-plane graphene materials for enhanced cell-chip coupling

Bioelectronic devices interact directly with biological systems to monitor cellular electrical activity and promote cell reaction to electrical stimulation. The capabilities of such devices, in terms of recording and stimulation, are affected by the effective cell-platform coupling. Therefore, during the last years, the development of engineered 2.5-3D micro and nanostructures has improved the effectiveness of biosensors using protruding structures to achieve a more intimate contact between cells and substrates. The vertical structures, due to their surface curvature, can actively modulate the cell-material interaction and the coupling conditions by regulating peculiar cellular processes at the interface such as membrane bending, ruffling, which ultimately reduce the distance between the electroactive materials and the biological components. In parallel, the rising of carbon-based materials (i.e., graphene) for bioelectronics has gained attention during the last years because of their outstanding chemical properties which allow improved cell-device interfacing. Given this scenario, 3D out-of-the-plane graphene structures has been designed and grown on planar platforms, exploiting the electrical, mechanical and optical features of this promising material. 3D fuzzy graphene (3DFG) and two nanowire-templated arrangements (NT-3DFG collapsed and non-collapsed) were realized to ultimately increase the dimensionality at the interface with cells through nanoscale features and wire-based architectures. Here we report a comprehensive study of the electrogenic cells-material interface by using fluorescence and electron microscopy for characterizing cell-graphene materials interactions at micro and nanoscale. First, we investigated the biocompatibility and the adhesion effect (cell stretching and outgrowth) of the diverse graphene-based pseudo-3D surfaces coupled to cardiomyocytes-like cells and primary cortical neuronal cells. Then, we examined the membrane deformation and the actual cell-device coupling via scanning electron microscopy/focused ion beam sectioning. We found out an enhanced cells adhesion on the substrates, suggesting that out-of-the-plane platform could improve the coupling between cells and sensors not only for electrophysiology application but also to modulate cellular functionalities and outgrowth

Nano Cost Nano Patterned Template for Surface Enhanced Raman Scattering (SERS) for IN-VITRO and IN-VIVO Applications

Raman scattering is a well-known technique for detecting and identifying complex molecular level samples. The weak Raman signals are enormously enhanced in the presence of a nano-patterned metallic surface next to the specimens. This dissertation describes a technique to fabricate a novel, low cost, high sensitive, disposable, and reproducible metallic nanostructure on a transparent substrate for Surface Enhanced Raman Scattering (SERS). Raman signals can be obtained from the specimen surface of opaque specimens. Most importantly, the metallic nanostructure can be bonded on the end of a probe / a needle, and the other end is coupled to a distant spectrometer. This opens up the Raman spectroscopy for a use in a clinical environment with the patient simply sitting or lying near a spectrometer. This SERS system, one of molecular level early diagnosis technologies, can be divided into four parts: SERS nanostructure substrates, reflection Raman signal (in vitro), transmission (in vivo) Raman signal, and a probe / a needle with a gradient-index (GRIN) lens in an articulated arm system. In this work, the aluminum metal was employed as not only a base substrate for a sputtered Au nanostructure (conventional view) but also a sacrificial layer for the Au nanostructure on a transparent substrate (transmission view). The enhanced Raman Signal from reflection and transparent SERS substrates depended on aluminum etching methods, Au deposition angles, and Au deposition thicknesses. Rhodamine 6G solutions on both sides of the SERS substrates were used to analyze and characterize. Moreover, preliminary Raman Spectra from R6G and chicken specimen were obtained through a remote SERS probe head and an articulated arm system. The diameter of the invasive probe head was shrunk to 0.5 mm. The implication is that this system can be applied in medical applications

Bioaerosol detection through simultaneous measurement of particle intrinsic fluorescence and spatial light scattering

Interest in the role and detection of airborne biological micro-organisms has increased dramatically in recent years, in part through heightened fears of bioterrorism. Traditional bio-detection methods have generally slow response times and require the use of reagents. Conversely, techniques based on light scattering phenomena are reagent-free and are able to operate in real-time. Previous research has established that classification of certain types of airborne particles on the basis of shape and size may be achieved through the analysis of the spatial light scattering patterns produced by individual particles. Similarly, other research has shown that the intrinsic fluorescence of particles excited by radiation of an appropriate wavelength can be used to establish the presence of biological particles, provided background particles with similar fluorescence properties are not present. This is often not the case. This thesis, therefore, describes the design, development, and testing of a new type of bioaerosol detection instrument in which the advantages of both particle spatial light scattering analysis and intrinsic fluorescence are exploited. The instrument, referred to as the Mult- Parameter Aerosol Monitor (MPAM), is unique in simultaneously recording data relating to the size, shape, and fluorescence properties of individual airborne particles at rates up to several thousand particles per second. The MPAM uses a continuous-wave frequency quadrupled Nd: YVO4 laser to produce both spatial scattering and fluorescence data from particles carried in single-file through the laser beam. This use of a CW laser leads to opto-mechanical simplicity and reduces fluorescence bleaching effects. A custom-designed multi-pixel Hybrid Photodiode (HPD) detector is used to record the spatial scattering data in forward scattering plane whilst particle fluorescence is recorded via a large solid-angle ellipsoidal reflector and single photomultiplier detector. Calibration tests and experimental trials involving a range of both biological and nonbiological aerosols have shown that the MPAM, when supported by appropriate data analysis algorithms, is capable of achieving enhanced levels of discrimination between biological and non-biological particles down to the submicrometre sizes and, in some cases, enhanced discrimination between classes of biological particle

Morphological and chemical changes in in vitro bone mineral and the effect of strontium on in vitro mineralisation

The steps involved in early bone mineralisation have been extensively studied. Studies have suggested various means by which bone mineralisation occurs: (1) a cell-independent (protein-assisted) process where non-collagenous proteins mediate soluble ions to form mineral on the collagen fibril; (2) a cell-controlled mechanism where mineral seeds formed within intracellular vesicles released from the plasma membrane would subsequently rupture and disperse their contents on the extracellular matrix; and (3) an acellular route in which amorphous calcium phosphate mineral precursors are produced and deposited in collagen fibrils where they transform into more crystalline apatite platelets. Despite extensive studies, there are still unanswered questions about how bone becomes bone. The first series of experiments in this thesis are aimed at studying the mineral characteristics of early in vitro osteoblast mineralisation at the extracellular matrix. These experiments seek to determine the sequence of possible mineralisation events that take place during mineral nucleation and growth on collagen. Transmission electron microscopy was used to provide high spatial resolution, which was compounded with chemical analysis of energy dispersive x-ray and electron energy-loss spectroscopies. We identified carbonate-rich calcium phosphate dense granules in the extracellular matrix that may act as seeds for growth into larger, submicron-sized, globular aggregates of apatite mineral with a different stoichiometry. These globules appear to mineralise the collagen fibrils forming crystalline textured crystals with higher calcium-to-phosphate ratio and lower carbonate content as the mineral phase of bone. We provide new evidence that the use of a carbonate rich, amorphous calcium phosphate spherical bioseed could be a process by which a soluble calcium phosphate phase is stabilised and delivered to the collagen for subsequent maturation and collagen mineralisation. We also examined the effect of strontium ion supplementation to bone mineralisation as a translational study. Previous studies showed the positive effects of in vivo strontium supplementation as an anti-osteoporotic drug. Strontium is able to: (1) stimulate bone formation; (2) increase osteoid surface, osteoblast surface, and bone forming surfaces; (3) decrease bone resorbing cells; (4) increase bone strength and mass; and (5) reduce the risk of fractures. In the in vitro system, studies have focused on how strontium ions increase bone mineral’s a- and c-axis lattice parameters. The effect of strontium ions on the matrix component of bone is the aim for the second part of this thesis. Using Raman spectroscopy, TEM imaging, and biochemical quantification, we studied the effect of strontium ion supplementation on in vitro MC3T3 osteoblasts, with close focus on the osteoblast matrix. We observed that cultures treated with high strontium supplementation had impaired mineralisation, where nodules were formed but failed to mineralise. Periodic collagen banding was seen on TEM micrograph from all treatments. Collagen organisation was quantified using image analysis of TEM micrograph, and strontium supplementation seemed to affect fibril organisation. A slight addition of 0.1 mM strontium seemed to result in the less random organisation of collagen fibril, while 3 mM supplementation seemed to increase the random organisation. Only cultures treated with the highest amount of strontium supplementation showed an abundance of matrix vesicles around the collagen fibrils. Raman spectroscopy showed an increase in lipid detection on strontium supplemented groups, which may be due to the increased presence of matrix vesicles. Taken together, high strontium supplementation may decrease the rate of degradation of matrix vesicles and lead to altered mineralisation whereby nodules form but fail to mineralise. The relative amounts of collagen were also explored by Raman spectroscopy and hydroxyproline analysis; however, our findings were not statistically significant. Further experiments are needed to more completely elucidate the molecular mechanisms at play in strontium’s effect on bone mineralisation.Open Acces