Hyperspectral image reconstruction of heritage artwork using RGB images and deep neural networks

The application of our research is in the art world where the scarcity of available analytical data from a particular artist or physical access for its acquisition is restricted. This poses a fundamental problem for the purpose of conservation, restoration or authentication of historical artworks. We address part of this problem by providing a practical method to generate hyperspectral data from readily available RGB imagery of artwork by means of a two-step process using deep neural networks. The particularities of our approach include the generation of learnable colour mixtures and reflectances from a reduced collection of prior data for the mapping and reconstruction of hyperspectral features on new images. Further analysis and correction of the prediction are achieved by a second network that reduces the error by producing results akin to those obtained by a hyperspectral camera. Our method has been used to study a collection of paintings by Amadeo de Souza-Cardoso where successful results were obtained. CCS CONCEPTS • Computing methodologies → Neural networks; Artificial intelligence; • Applied computing → Arts and humanities.info:eu-repo/semantics/publishedVersio

Review:New sensors and data-driven approaches—A path to next generation phenomics

At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for “next generation phenomics” based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts

Role of Drones in Characterizing Soil Water Content in Open Field Cultivation

Soil water content is a central topic in open field cultivation. In Finland’s boreal region with four thermal seasons, it has many roles which alter throughout the year. Climate change is changing the weather patterns, affecting all water-related processes and challenging the current farming practices. Better understanding of soils and their characteristics regarding response to water processes is called for, and data collection has a key role in this. Precision agriculture has been driving data intensification in farming. Unmanned aerial vehicles, or drones, have many applications and overall wide interest as an emerging technology in agriculture. Yet they lack an established role in day-to-day farming practices. Regarding data collection in open field cultivation, drones can be compared – or combined – with satellites, rovers, stationary devices, as well as plain old on-site observations by the farmer. In this study we give an overview of recent published literature, looking at data collection from the perspective of soil water information. We assess the opportunities and challenges of using drones in characterizing soil water content, mainly using soil and plant properties as proxies for it. Drones are useful in on-demand, nonintrusive, high-resolution spatial mapping of field properties. Soil moisture monitoring however requires frequent measurements, limiting the applicability of current drones.acceptedVersionPeer reviewe