Sensors and biosensors for pathogen and pest detection in agricultural systems : recent trends and oportunities

Pathogen and pest-linked diseases across agriculture and ecosystems are a major issue towards enhancing current thresholds in terms of farming yields and food security. Recent developments in nanotechnology allowed the designing of new generation sensors and biosensors in order to detect and mitigate these biological hazards. However, there are still important challenges concerning its respective applications in agricultural systems, typically related to point-of-care testing, cost reduction and real-time analysis. Thus, an important question arises: what are the current state-of-the-art trends and relationships among sensors and biosensors for pathogen and pest detection in agricultural systems? Targeted to meet this gap, a comparative study is performed by a literature review of the past decade and further data mining analysis. With the majority of the results coming from recent studies, leading trends towards new technologies were reviewed and identified, along with its respective agricultural application and target pathogens, such as bacteria, viruses, fungi, as well as pests like insects and parasites. Results have indicated lateral flow assay, lab-on-a-chip technologies and infrared thermography (both fixed and aerial) as the most promising categories related to sensors and biosensors driven to the detection of several different pathogenic varieties. The main existing interrelations between the results are especially associated to cereals, fruits and nuts, meat and dairy along with vegetables and legumes, mostly caused by bacterial and fungal infections. Additional results also presented and discussed, providing a fertile groundwork for decision-making and further developments in modern smart farming and IoT-based agriculture

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Vision-based navigation with reality-based 3D maps

This research is focused on developing vision-based navigation system for positioning and navigation in GPS degraded environments. The main research contributions are summarized as follows: a. A new concept of 3D map, which mainly consists of geo-referenced images, has been introduced. In this research, it provides the map-matching function for vision-based positioning. b. A method of vision-based positioning with use of photogrammetric methodologies has been proposed. It mainly obtains geometric information of the navigation environment from the 3D map through SIFT based image matching and uses photogrammetric space resection to solve the position in 6 degrees of freedom. The algorithms have been tested in an indoor environment. The accuracy has reached around 10 cm. c. A multi-level outlier detection scheme for the vision-based navigation system has been developed. It mainly combines RANSAC with data snooping. The former one deals with high percentage of mismatches, while data snooping removes outliers from different sources in the least squares adjustment for both 3D mapping and positioning solution. d. The deficiency of using RANSAC for outlier detection in image matching and homography estimation has been identified. In this research, a novel method which combines cross correlation with feature based image matching has been proposed. It is able to evaluate the RANSAC homography estimation and improve the image matching performance. The method has been successfully applied to the vision-based navigation solution to find corresponding view from the database and improve the final positioning accuracy. e. The positioning performance of the system has been evaluated through the analysis of mathematical model and experiments. The focus has been on various image matching conditions/methods and their impact on the system performance. The strength and weaknesses of the system have been revealed and investigated. f. The vision-based navigation system has been extended from indoor to outdoor with corresponding changes. Besides camera, it also takes advantage of multiple built-in sensors, including GPS receiver and a digital compass to assist visual methods in outdoor environments. Experiments demonstrate that such system can largely improve the position accuracy in areas where stand-alone GPS is affected and can be easily adopted on mobile devic

Deep Learning for Time-Series Analysis of Optical Satellite Imagery

In this cumulative thesis, I cover four papers on time-series analysis of optical satellite imagery. The contribution is split into two parts. The first one introduces DENETHOR and DynamicEarthNet, two landmark datasets with high-quality ground truth data for agricultural monitoring and change detection. Second, I introduce SiROC and SemiSiROC, two methodological contributions to label-efficient change detection

Contributions to discrete-time methods for room acoustic simulation

The sound field distribution in a room is the consequence of the acoustic properties of radiating sources and the position, geometry and absorbing characteristics of the surrounding boundaries in an enclosure (boundary conditions). Despite there existing a consolidated acoustic wave theory, it is very difficult, nearly impossible, to find an analytical expression of the sound variables distribution in a real room, as a function of time and position. This scenario represents as an inhomogeneous boundary value problem, where the complexity of source properties and boundary conditions make that problem extremely hard to solve. Room acoustic simulation, as treated in this thesis, comprises the algebraical approach to solve the wave equation, and the way to define the boundary conditions and source modeling of the scenario under analysis. Numerical methods provide accurate algorithms for this purpose and among the different possibilities, the use of discrete-time methods arises as a suitable solution for solving those partial differential equations, particularized by some specific constrains. Together with the constant growth of computer power, those methods are increasing their suitability for room acoustic simulation. However, there exists an important lack of accuracy in the definition of some of these conditions so far: current frequency-dependent boundary conditions do not comply with any physical model, and directive sources in discrete-time methods have been hardly treated. This thesis discusses about the current state-of-the-art of the boundary conditions and source modeling in discrete-time methods for room acoustic simulation, and it contributes some algorithms to enhance boundary condition formulation, in a locally reacting impedance sense, and source modelling in terms of directive sources under a defined radiation pattern. These algorithms have been particularized to some discrete-time methods such as the Finite Difference Time Domain and the Digital Waveguide Mesh.Escolano Carrasco, J. (2008). Contributions to discrete-time methods for room acoustic simulation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8309Palanci

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Intraoperative Assessment of Breast Cancer Margins ex vivo using Aqueous Quantum Dot-Functionalized Molecular Probes

Breast cancer is increasingly diagnosed at an early stage, allowing the diseased breast to be removed only partially or breast conserving surgery (BCS). Current BCS procedures have no rapid methods during surgery to assess if the surgical margin is clear of cancer, often resulting in re-excision. The current breast cancer re-excision rate is estimated to be 15% to as high as 60%. It would be desirable if there is a rapid and reliable breast cancer margin assessment tool in the operating room to help assess if the surgical margin is clean to minimize unnecessary re-excisions. In this research, we seek to develop an intraoperative, molecular probe-based breast cancer surgical margin assessment tool using aqueous quantum dots (AQDs) coupled with cancer specific biomarkers. Quantum dots (QDs) are photoluminescent semiconductor nanoparticles that do not photobleach and are brighter than organic fluorescent dyes. Aqueous quantum dots (AQDs) such as CdSe and near infrared (NIR) CdPbS developed in Shihs lab emit light longer than 600 nm. We have examined conjugating AQDs with antibodies to cancer specific biomarkers such as Tn antigen, a cancer-associated glycan antigen for epithelial cancers. We showed that AQDs could achieve ~80% antibody conjugation efficiency, i.e., 100 times less antibodies than required by commercial, making such AQD molecular probe surgical margin evaluation economically feasible. By conjugating AQDs with anti-Tn-antigen antibody, the AQDs molecular probe exhibited 94% sensitivity and 92% specificity in identifying breast cancer against normal breast tissues as well as benign breast tumors in 480 tissue blocks from 126 patients. Furthermore, mice model and clinical human studies indicated that AQDs imaging did not interfere with the following pathological staining. More interestingly, we showed that it it possible to directly conjugate one antibody to multiple AQDs, further reduces the required amount of antibodies needed, a feat that could not be accomplished by commercial QDs. To date, using a home-built imaging system consisted of 4 LEDs and a NIR CCD camera we have successfully imaged several human breast surgical margins with high sensitivity in less than 30 min.Ph.D., Biomedical Engineering -- Drexel University, 201