Making sense of light: the use of optical spectroscopy techniques in plant sciences and agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.info:eu-repo/semantics/publishedVersio

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

STRUCTURED LIGHT FOR DATA-DRIVEN QUANTITATIVE TISSUE IMAGING

Compared to the other major medical imaging modalities (CT, MRI, PET, ultrasound, and microscopy), conventional wide-field optical imaging is particularly qualitative. For endoscopic and laparoscopic optical imaging, the spatial scale of the image is typically unknown, heterogeneous, and rapidly changing frame-to-frame. Moreover, the fundamental optical properties of the imaged tissue are conflated in the acquired images. This leads to several clinical limitations. The absorption coefficient, which can indicate important biomarkers such as tissue oxygenation, cannot be unambiguously determined. Moreover, for machine learning analysis of endoscopic images, qualitative image inputs lead to large training datasets requirements and limit generalizability compared to algorithms trained with quantitative images. Quantitative optical imaging may be key to enabling reliable artificial intelligence algorithms for tasks such as tissue classification or tumor margin assessment. Spatial Frequency Domain Imaging (SFDI) is a relatively new optical imaging technique that is capable of wide-field quantification and mapping of tissue optical properties. Using structured illumination at different phases and spatial frequencies, it is capable of unambiguously decoupling optical absorption and scattering. In recent years, SFDI has seen growing use in many applications, such as wound monitoring and diabetic ulcer staging. Unfortunately, conventional SFDI approaches suffer from the trade-off between imaging speed and accuracy. SFDI also requires a projection system, which limits the potential for clinical adoption in space-constrained applications, such as endoscopic imaging. In this work, we present a technique that leverages the power of data-driven methods, such as convolutional neural networks, to achieve real-time and accurate optical property and tissue oxygenation mapping from single-shot SFDI images. We also develop a signal processing model for projector-free SFDI using random laser speckle patterns as structured illumination. This approach is promising for rapid, low-cost, and compact endoscopic imaging of optical biomarkers. Overall, our work has the potential to facilitate clinical translation and adoption of quantitative tissue imaging techniques

XLIII Jornadas de Automática: libro de actas: 7, 8 y 9 de septiembre de 2022, Logroño (La Rioja)

[Resumen] Las Jornadas de Automática (JA) son el evento más importante del Comité Español de Automática (CEA), entidad científico-técnica con más de cincuenta años de vida y destinada a la difusión e implantación de la Automática en la sociedad. Este año se celebra la cuadragésima tercera edición de las JA, que constituyen el punto de encuentro de la comunidad de Automática de nuestro país. La presente edición permitirá dar visibilidad a los nuevos retos y resultados del ámbito, y su uso en un gran número de aplicaciones, entre otras, las energías renovables, la bioingeniería o la robótica asistencial. Además de la componente científica, que se ve reflejada en este libro de actas, las JA son un punto de encuentro de las diferentes generaciones de profesores, investigadores y profesionales, incluyendo la componente social que es de vital importancia. Esta edición 2022 de las JA se celebra en Logroño, capital de La Rioja, región mundialmente conocida por la calidad de sus vinos de Denominación de Origen y que ha asumido el desafío de poder ganar competitividad a través de la transformación verde y digital. Pero también por ser la cuna del castellano e impulsar el Valle de la Lengua con la ayuda de las nuevas tecnologías, entre ellas la Automática Inteligente. Los organizadores de estas JA, pertenecientes al Área de Ingeniería de Sistemas y Automática del Departamento de Ingeniería Eléctrica de la Universidad de La Rioja (UR), constituyen un pilar fundamental en el apoyo a la región para el estudio, implementación y difusión de estos retos. Esta edición, la primera en formato íntegramente presencial después de la pandemia de la covid-19, cuenta con más de 200 asistentes y se celebra a caballo entre el Edificio Politécnico de la Escuela Técnica Superior de Ingeniería Industrial y el Monasterio de Yuso situado en San Millán de la Cogolla, dos marcos excepcionales para la realización de las JA. Como parte del programa científico, dos sesiones plenarias harán hincapié, respectivamente, sobre soluciones de control para afrontar los nuevos retos energéticos, y sobre la calidad de los datos para una inteligencia artificial (IA) imparcial y confiable. También, dos mesas redondas debatirán aplicaciones de la IA y la implantación de la tecnología digital en la actividad profesional. Adicionalmente, destacaremos dos clases magistrales alineadas con tecnología de última generación que serán impartidas por profesionales de la empresa. Las JA también van a albergar dos competiciones: CEABOT, con robots humanoides, y el Concurso de Ingeniería de Control, enfocado a UAVs. A todas estas actividades hay que añadir las reuniones de los grupos temáticos de CEA, las exhibiciones de pósteres con las comunicaciones presentadas a las JA y los expositores de las empresas. Por último, durante el evento se va a proceder a la entrega del “Premio Nacional de Automática” (edición 2022) y del “Premio CEA al Talento Femenino en Automática”, patrocinado por el Gobierno de La Rioja (en su primera edición), además de diversos galardones enmarcados dentro de las actividades de los grupos temáticos de CEA. Las actas de las XLIII Jornadas de Automática están formadas por un total de 143 comunicaciones, organizadas en torno a los nueve Grupos Temáticos y a las dos Líneas Estratégicas de CEA. Los trabajos seleccionados han sido sometidos a un proceso de revisión por pares