Search results for "Statistic"

Scenario-based discrimination of common grapevine varieties using in-field hyperspectral data in the western of Iran

2019

Abstract Field spectroscopy is an accurate, rapid and nondestructive technique for monitoring of agricultural plant characteristics. Among these, identification of grapevine varieties is one of the most important factors in viticulture and wine industry. This study evaluated the discriminatory ability of field hyperspectral data and statistical techniques in case of five common grapevine varieties in the western of Iran. A total of 3000 spectral samples were acquired at leaf and canopy levels. Then, in order to identify the best approach, two types of hyperspectral data (wavelengths from 350 to 2500 nm and 32 spectral indices), two data reduction methods (PLSR and ANOVA-PCA) and two classif…

Screening individual ability to perform descriptive analysis of food products : basic statements and application to a camembert cheese descriptive pa…

1995

A battery of sensory tests is proposed to select potential descriptive panelists. This set of tests is flavor specific. Several abilities are examined: odor and taste recognition, odor memory, discrimination and descriptive capacities. A detailed example of such a battery to select a Camembert cheese descriptive panel is given. The objectives of each test are discussed. Stimuli are chosen to be consistent with the sensory properties which can be perceived in cheese. Score distributions demonstrate the discrimination among candidates for each test. Globally, results show the difficulty to find 20 panelists amongst about a hundred with good scores on each test. The panel leader has to choose …

2016

The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk-outcome p…

The significance of climate variability on early modern European grain prices

2021

AbstractGrain was the most important food source in early modern Europe (c. 1500–1800), and its price influenced the entire economy. The extent to which climate variability determined grain price variations remains contested, and claims of solar cycle influences on prices are disputed. We thoroughly reassess these questions, within a framework of comprehensive statistical analysis, by employing an unprecedentedly large grain price data set together with state-of-the-art palaeoclimate reconstructions and long meteorological series. A highly significant negative grain price–temperature relationship (i.e. colder = high prices and vice versa) is found across Europe. This association increases a…

Crop Yield Estimation and Interpretability With Gaussian Processes

2021

This work introduces the use of Gaussian processes (GPs) for the estimation and understanding of crop development and yield using multisensor satellite observations and meteo- rological data. The proposed methodology combines synergistic information on canopy greenness, biomass, soil, and plant water content from optical and microwave sensors with the atmospheric variables typically measured at meteorological stations. A com- posite covariance is used in the GP model to account for varying scales, nonstationary, and nonlinear processes. The GP model reports noticeable gains in terms of accuracy with respect to other machine learning approaches for the estimation of corn, wheat, and soybean …

Modélisation du comportement des agriculteurs face au risque dans un modèle de programmation mathématique positive (PMP) à grande échelle

2017

Agricultural production is characterized for being a risky business due to weather variability, market instability, plant diseases as well as climate change and political economy uncertainty. The modelling of risk at farm level is not new, however, the inclusion of risk in Positive Mathematical Programming (PMP) models is particularly challenging. Most of the few existing PMP-risk approaches have been conducted at farm-type level and for a very limited and specific sample of farms. This implies that the modelling of risk and uncertainty at individual farm level and in a large scale system is still a challenging task. The aim of this paper is to formulate, estimate and test a robust methodol…

Interpretability of Recurrent Neural Networks in Remote Sensing

2020

In this work we propose the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for multivariate time series of satellite data for crop yield estimation. Recurrent nets allow exploiting the temporal dimension efficiently, but interpretability is hampered by the typically overparameterized models. The focus of the study is to understand LSTM models by looking at the hidden units distribution, the impact of increasing network complexity, and the relative importance of the input covariates. We extracted time series of three variables describing the soil-vegetation status in agroe-cosystems -soil moisture, VOD and EVI- from optical and microwave satellites, as well as available in si…

Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

2020

Abstract Nitrogen (N) is considered as one of the most important plant macronutrients and proper management of N therefore is a pre-requisite for modern agriculture. Continuous satellite-based monitoring of this key plant trait would help to understand individual crop N use efficiency and thus would enable site-specific N management. Since hyperspectral imaging sensors could provide detailed measurements of spectral signatures corresponding to the optical activity of chemical constituents, they have a theoretical advantage over multi-spectral sensing for the detection of crop N. The current study aims to provide a state-of-the-art overview of crop N retrieval methods from hyperspectral data…

Towards events ontology based on data sensors network for viticulture domain

2018

International audience; Wine Cloud project is the first "Big Data" platform on the french viticulture value chain. The aim of this platform is to provide a complete traceability of the life cycle of the wine, from the wine-grower to the consumer. In particular, Wine Cloud may qualify as an agricultural decision platform that will be used for vine life cycle management in order to predict the occurrence of major risks (vine diseases, grape vine pests, physiological risks, fermentation stoppage, oxidation of vine, etc...). Also to make wine production more rational by offering winegrower a set of recommendation regarding their strategy's of production development. The proposed platform "Wine …

Spatial and temporal stability of weed populations over five years

2000

Abstract The size, location, and variation in time of weed patches within an arable field were analyzed with the ultimate goal of simplifying weed mapping. Annual and perennial weeds were sampled yearly from 1993 to 1997 at 410 permanent grid points in a 1.3-ha no-till field sown to row crops each year. Geostatistical techniques were used to examine the data as follows: (1) spatial structure within years; (2) relationships of spatial structure to literature-derived population parameters, such as seed production and seed longevity; and (3) stability of weed patches across years. Within years, densities were more variable across crop rows and patches were elongated along rows. Aggregation of …