PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use

Global Rice Atlas: Disaggregated seasonal crop calendar and production

Purpose: Rice is an important staple crop cultivated in more than 163 million ha globally. Although information on the distribution of global rice production is available by country and, at times, at subnational level, information on its distribution within a year is often lacking in different rice growing regions. Knowing when and where rice is planted and harvested and the associated production is crucial to policy and decision making on food security. To examine seasonal and geographic variations in food supply, we developed a detailed rice crop calendar and linked it with disaggregated production data. Approach and methods used: We compiled from various sources detailed data on rice production, and planting and harvesting dates by growing season. To standardize the production data to the same period, we adjusted the production values so that the totals for each country will be the same as those of FAO for 2010-2012. We then linked data on rice production with the corresponding crop calendar information to estimate production at harvest time by month then we calculated totals for each country and region. Key results: The bulk of global annual harvests of rice is from September to November, corresponding with the harvest of the wet season rice in Asia and Africa. Total rough rice production during those peak months exceed 381 million tons, which account for about half of annual global rice output. Production is lowest in January with only 11 million tons in total. Regional production is lowest in Asia in January, Americas in December, Africa in July and rest of the world in May. Synthesis and Applications: A globally complete and spatially detailed rice crop calendar is important to crop growth simulation modelling and assessment of vulnerability of rice areas to biotic and abiotic stresses. Linked to production estimates, it can be used in analyzing spatial and seasonal production trends to better assess and predict price fluctuations , and to mitigate potential significant shortfalls in food production at certain times of the year

Broadband enhancement of light-matter interaction in photonic crystal cavities integrating site-controlled quantum dots

The fabrication of integrated quantum dot (QD)-optical microcavity systems is a requisite step for the realization of a wide range of nanophotonic experiments (and applications) that exploit the ability of QDs to emit nonclassical light, e.g., single photons. Thanks to their similar to 20-nm positioning accuracy and to their proven potential for single-photon operation, the QDs obtained by spatially selective hydrogen irradiation of dilute-nitride semiconductors-such as Ga(AsN) and Ga(PN)-are uniquely suited for integration with photonic nanodevices. In the present work, we demonstrate the ability to deterministically integrate single, site-controlled Ga(AsN)/Ga(AsN):H QDs within a photonic crystal (PhC) cavity. The properties of the fabricated QD-PhC cavity systems are then probed by photon correlation-providing clear evidence of single-photon emission-and time-resolved microphotoluminescence spectroscopy. Detailed information on the dynamics of our integrated nanodevices can be inferred by comparing these experiments to the solutions of a rate-equations system, developed by taking into account all the main processes leading to the capture, relaxation, and recombination of carriers in and out of the QD. This allows us to follow the evolution of the relevant recombination rates in our system for varying energy detuning, Delta E, between the QD and the PhC cavity. When the QD exciton transition is nearly resonant with the cavity mode, a large (&gt;tenfold) enhancement of the spontaneous emission rate is observed, in substantial agreement with Jaynes-Cummings (JC) theory. For intermediate detunings (Delta E similar to 1.5-3.5 meV), on the other hand, the observed enhancement is significantly larger than that predicted by JC theory, due to the important role played by acoustic phonons in mediating the QD-PhC cavity coupling in a solid-state environment. Apart from its fundamental interest, the observation of such phonon-mediated, broadband enhancement of light-matter interaction significantly relaxes the requirements for the realization of a large variety of cavity QED-based experiments and applications. These include many photonic devices for which the use of site-controlled Ga(AsN)/Ga(AsN):H QDs would be inherently advantageous, such as those based on the coupling between more than one QD and a single cavity mode (e.g., few-QD nanolasers and QD solids)

Hollandite Nanoparticles for Chemical Sensing: Strain or Pseudosymmetry?

Parm

RICA: A rice crop calendar for Asia based on MODIS multi year data

Information on when and where rice is planted and harvested is important for crop management under a changing climate and for monitoring crop production for early warning and market information systems. The diversity of plant genetic, crop management, and environmental conditions leads to a wide variation in the number of rice crops per year and the dates of crop establishment and harvesting across Asia. Asia-wide rice crop calendars exist (e.g., RiceAtlas) but are based on heterogeneous data sources with varying levels of detail and are challenging to update. Earth observations can contribute to consistent and replicable crop calendars. Here we demonstrate and validate a method for generating a rice crop calendar across Asia. Our analysis at administrative unit-level is based on pixel-level analysis with the PhenoRice algorithm using MODIS imagery (2003–16) to estimate start of season (SoS) and end of season (EoS) dates. PhenoRice outputs were post-processed to generate representative statistics on the number of rice crop seasons per year and their SoS/EoS dates per administrative unit across Asia, called RICA (a RIce crop Calendar for Asia). RICA SoS and EoS dates across all seasons correlated strongly with RiceAtlas crop establishment and harvesting dates (R2 of 0.88 and 0.82 respectively, n = 1,186). The mean absolute errors were around 26 and 33 days for SoS and EoS, respectively. A detailed assessment in the Philippines where data in RiceAtlas are particularly accurate had even better results (R2 of 0.93 and 0.85 respectively, n = 131). Comparisons to other published rice calendars also suggested that RICA captured rice cropping season dates well. Our study results in a unique and validated method to estimate rice crop calendar information on continental scale from remote sensing data

Spectral super-resolution spectroscopy using a random laser

Super-resolution microscopy refers to a powerful set of imaging techniques that overcome the diffraction limit. Some of these techniques, the importance of which was recognized by the 2014 Nobel Prize for chemistry, are based on the concept of image reconstruction by spatially sparse sampling. Here, we introduce the concept of super-resolution spectroscopy based on sparse sampling in the frequency domain, and show that this can be naturally achieved using a random laser source. In its chaotic regime, the emission spectrum of a random laser features sharp spikes at uncorrelated frequencies that are sparsely distributed over the emission bandwidth. These narrow lasing modes probe stochastically the spectral response of a sample, allowing it to be reconstructed with a resolution exceeding that of the spectrometer. We envision that the proposed technique will inspire a new generation of simple, cheap, high-resolution spectroscopy tools with a reduced footprint

Influence of olive (cv Grignano) fruit ripening and oil extraction under different nitrogen regimes on volatile organic compound emissions studied by PTR-MS technique

Volatile organic compounds of extra virgin olive oils obtained from the local Italian cultivar Grignano were measured by proton transfer reaction\u2013mass spectrometry (PTR-MS). Oils were extracted by olives harvested at different ripening stages across veraison, performing each extraction step and the whole extraction process in nitrogen atmosphere to observe the changes in the volatile profiles of the oils. Principal component analysis carried out on the full spectral signature of the PTR-MS measurements showed that the stage of ripening has a stronger effect on the global definition of volatile profiles than the use of nitrogen during oil extraction. The fingerprint-like chemical information provided by the spectra were used to construct a heat map, which allowed the dynamical representation of the multivariate nature of mass evolution during the ripening process. This provided the first evidence that some groups of volatile organic compounds displayed a time course of regulation with coordinated increasing or decreasing trends in association with specific stages of fruit ripening