Recognizing that ICT today plays a pivotal role in ensuring sustainable, smart and inclusive growth of agriculture, the Research and Development Institute for Information Technologies in Biosystems, also known as the BioSense Institute, has been founded to focus multidisciplinary, game-changing and needs-driven research and disseminate it to a global ecosystem of forward-looking stakeholders. Multidisciplinary research is performed in the fields of micro and nanoelectronics, communications, signal processing, remote sensing, big data, robotics and biosystems, with a common goal to support the development of sustainable agriculture and create a positive impact to the lives of people. Bio-Sense advances and integrates all that ICT can offer today — nanomaterials, low-cost miniature sensors, satellite imaging, robotics, big data analytics — to provide as much information as possible to the agricultural sector. The final goal of BioSense is to incorporate all efforts and results of various research groups into a unique BioSense integrated system for agricultural monitoring. BioSense Institute coordinates or participates in a large number of international research projects, including Horizon, FP7 and Eureka.
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It dates back with the invention of photography and the first areal images from balloons back in mid- 19th century. It developed a lot over this period, so nowadays there are various techniques for collecting the data about an object without touching it multispectral cameras, RADAR, LiDAR, etc. All these techniques have in common that they use electromagnetic EM radiation as a mean of gathering the data.
Synthetic Aperture Radar SAR is a specific technique because it uses microwave electromagnetic radiation. Depending on the part of EM spectrum which is used for sensing, e. Yes, radar signal can penetrate the canopy depending on the wavelength of the signal! Figure 1. SAR working principles also make it able to penetrate clouds, which is very useful in cases when opticalimagery is obscured by clouds. In principle, SAR has its own source of illumination which sets it in a group of active sensors and enables it to work both during day and at night.
In this sense, SAR is actually very reliable source of information. In agriculture, SAR is used for classification, monitoring vegetation dynamics, collecting the information about soil and vegetation characteristics such as roughness and moisture , irrigation, and in many other applications.
When performing crop classification, SAR can be utilized independently or in combination with optical images. Thus, SAR brings added value to well established crop classification process using solely optical imagery. These results were better than the crop classification accuracy achieved using single source data.
Figure 2. Schematic diagram of different influences on backscatter signal van Emmerik, Tim. Water stress detection using radar. SAR image processing is more complex than of optical imagery because SAR images are not intuitive and receptive to human eye like optical ones. However, it is worth learning and understanding SAR technique because it provides unique information, highly valuable, that no other sensor is capable of. Figure 3. With recently available Sentinel-1 SAR data distributed free of charge by the European Space Agency,community interest in radars increased significantly because for the first time ever people are offeredwith high quality radar data at no cost for both scientific and commercial use.
Figure 3 shows historical overview of SAR satellite missions. As part of the DRAGON project, she works on connecting young researchers with the startup ecosystem, as well as on building their entrepreneurial and innovative thinking. He has been working in the fields of GIS and remote sensing data. Vojislav D. His work has been published in many international peer-reviewed journals and conference proceedings.
Lectures in area of precision farming over Europe as Italy, Malta, Slovakia etc. His specialty in area of precision farming is yield monitoring and crop scouting. Interested in machine learning and precision agricul-ture.
Eng, PhD in Electr. His main area of research is computer vision applications using deep learning. He coordinated several National and European projects. He is involved in the development of radiative transfer models at several scales soil, leaf, canopy and their use for the retrieval of vegetation biophysical variables.
He recently expanded his activity on high throughput phenotyping with the development of measurement systems as well as interpretation methods. This includes the application of IoTs sensors on fixed positions , phenomobiles fully automatic robot rover as well as the development of drone observations. SoilEssentials Ltd. He has been running the family farm at Hilton of Fern in Angus, Scotland since the early eighties. A typical Scottish arable farm, it has hugely variable land, both in terms of soil type and topography.
In he began collating data collected from a variety of sensors and input sources which helped quantify in field spatial variability in both map and economic terms. This information, when demonstrated to local farmers was the starting point for a long journey into the world of Precision Agriculture.
SoilEssentials was set up in with three business partners, two farmers and an agronomist, who were equally interested in creating useful, meaningful, practical and ultimately user-friendly hardware and software based on sound agronomy practices.
His passion for finding user-friendly, practical applications for Precision Agriculture Technology is what drives him. Maintaining an active role in the farm allows him to test first-hand any new developments.
Ben Scott-Robinson is an accomplished digital entrepreneur focused on geospatial and mobility technologies. Ben co-founded the Small Robot Company in which endeavours to replace tractors with accurate, smart, lightweight robots. With 20 years experience in digital innovation, including the digital transformation of Ordnance Survey, Ben is also an experienced technology entrepreneur having founded two agencies, two consultancies, an app start-up and a phone for the blind.
Dr Shamal Mohammed is a leading expert in digital agriculture with a strong passion for transforming the industry by developing novel and practical solutions, using smart technologies, to help farmers and producers optimise food production and minimise adverse environmental impacts.
Currently, being based in Cranfield, Shamal works with Agri-EPI partners to lead the journey to innovate technology in the agriculture sector by providing farmers and producers with a wide range of scientifically robust and commercially viable capabilities.
He was co-founder of Silent Herdsman Ltd, a spin out company providing wirelessly enabled decision support tools within the dairy farming sector. The company has recently been acquired by Afimilk providing greater international reach for the product.
He is involved in a number of research projects for the development of end-to-end IoT solutions from physical devices, wireless communication, data acquisition, data analysis and interpretation. Damien Jacques is a freelance consultant in data innovation for development. He has led projects across the globe requiring i designing and implementing algorithms to extract key insights from large unstructured data, ii develop strategies to leverage the entire data value chain of companies and development agencies; and iii successfully scale up data solutions in complex and multi-stakeholder ecosystem.
He has established a strong academic track record through several projects in Data Science and AI for social good including projects in food security, poverty and agricultural economics. His projects involved the use of mobile phone data and satellite images and GIS, machine learning and geostatistics analyses in Central America and Africa. Damien holds a Ph. Currently working as a researcher at NIOO with funding from Maj and Tor Nessling foundation investigating the relationship between soil diversity, agricultural practices and carbon sequestration potential of the soils.
Kristof then joined the VITO Remote Sensing Agricultural Applications team in , using remote sensing technologies for the mapping and monitoring of agriculture on local to global scales.
He synergises optical and radar SAR remote sensing using deep learning to improve crop monitoring from space, focusing on observations obscured by clouds. The framework of the research ranges from local Belgian agriculture studies, to projects supporting African farming practices, and the provision of more industry-oriented information.
Gert Kootstra M is Assistant Professor in computer vision and robotics. He received his PhD in artificial intelligence from the University of Groningen in He worked on the topic of machine vision for robotic manipulation at the Royal Institute of Technology KTH in Stockholm from At Wageningen Research, Gert started working on the application of machine vision and robotics in food production and agriculture in close collaboration with industry.
In , he started his current position at Wageningen University. Gert currently co-supervises 10 PhD students and 4 postdocs all working on the development of machine-vision and robotic technologies with application in the agri-food sector. He has been working with UAVs and field robotics for more than 10 years. Drones and people. He is author of more than 40 journals and conferences papers in this topic. My toolbox includes multivariate statistics, graph theory, metabolic control analysis, genome wide association mapping and mathematical modeling of metabolic processes.
In my group we combine computational approaches and mass spectrometry-based chemical analytics to study metabolic and quality traits of crop plants. Print page. No Comments. Predrag Lugonja. Sanja Brdar. Ivana Gadjanski. Anastasios Stamoulakatos. Jim Wilson. Robert Atkinson. Ben Scott Robinson. Shamal Mohammad. Craig Michie. Christos Tachtatzis. Damien Jacques. Emilia Hannula.
Kristof Van Tricht. Gert Kootstra. Joao Valente. Jedrzej Jakub Szymanski.
Remote sensing and disaster risk reduction
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ISBN 13: 9788617115546
ISBN 13: 9788617115546