GWHD dataset offers 4,700 high-res images of wheat heads.
problem Challenges in wheat head detection from high-resolution imagery.
method Large, diverse dataset with detailed metadata.
result Benchmark for wheat head detection methods.
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168,695 papers · 148 categories
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22 results for “wheat”
Study finds intrinsic multifractality in maize and barley spot markets, but not in wheat and rice.
problem Understanding the complex price behavior of global grain spot markets.
method Utilized multifractal fluctuation analysis (MF-DFA) to investigate intrinsic multifractality.
result Intrinsic multifractality found in maize and barley sub-indices, but not in wheat and rice.
Paper detects anomalies in wheat and rapeseed crops using satellite data.
problem Detecting anomalies in crop development at parcel-level.
method Unsupervised outlier detection using SAR and multispectral features.
result Best performance with a 10% outlier ratio, achieving 94.1% true positives for rapeseed and 95.5% for wheat.
Study finds multifractal cross-correlations between agricultural markets and external uncertainties.
problem Investigating relationships between agricultural spot markets and external uncertainties.
method Multifractal detrending moving-average cross-correlation analysis (MF-X-DMA).
result Maize exhibits intrinsic joint multifractality with all uncertainty proxies.
Bayesian model predicts phenotype effects from multi-environmental factors.
problem Predict phenotype effects from multi-environmental trials.
method Bayesian tensor regression with spike-and-slab structure.
result Model outperforms previous methods in simulation and real-world data.
Paper uses robust GMM to reconstruct missing data in Sentinel-2 images for crop monitoring.
problem Missing data in remote sensing images, especially from multispectral and SAR sensors.
method Robust Gaussian Mixture Models (GMM) with outlier detection using isolation forest.
result Robust GMM outperforms standard GMM in reconstructing imputed values, reducing errors.
Apparently random financial fluctuations often exhibit varying levels of complexity, chaos. Given limited data, predictability of such time series becomes hard to infer. While efficient methods of Lyapunov exponent computation are devised, knowledge about the process driving the dynamics greatly facilitates the complex…
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
problem Impact of Russia-Ukraine conflict on global agricultural futures and spot markets' extreme risks.
method Analytical framework for tail dependence, Copula-CoVaR method, ARMA-GARCH-skewed Student-t model.
result The outbreak of the conflict intensified risks in the wheat market the most and showed significant asymmetries in extreme risk spillovers.
Uncovering the Sino-US dynamic risk spillovers effects: Evidence from agricultural futures marketsecon.GN
Study measures risk spillovers between US and China's agricultural futures markets.
problem Interconnectedness and risk transmission in agricultural futures markets.
method TVP-VAR-DY model with quantile method.
result CBOT corn, soybean, and wheat are primary risk transmitters; DCE corn and soybean are main receivers.
Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks bec…
The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset ( 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of . The DNN …
Non-Lévy Distribution of Commodity Price Fluctuationscond-mat.stat-mech
Price fluctuations of commodities like cotton and wheat are thought to display probability distributions of returns that follow a Lévy stable distribution. Recent analysis of stocks and foreign exchange markets show that the probability distributions are not Lévy stable, a plausible result since commodity markets have …
We provide a machine learning solution that replaces the traditional methods for deciding the pesticide application time of Sunn Pest. We correlate climate data with phases of Sunn Pest in its life-cycle and decide whether the fields should be sprayed. Our solution includes two groups of prediction models. The first gr…
We introduce a multi-factor stochastic volatility model for commodities that incorporates seasonality and the Samuelson effect. Conditions on the seasonal term under which the corresponding volatility factor is well-defined are given, and five different specifications of the seasonality pattern are proposed. We calcula…
For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencie…
We analyze the market efficiency of 25 commodity futures across various groups -- metals, energies, softs, grains and other agricultural commodities. To do so, we utilize recently proposed Efficiency Index to find that the most efficient of all the analyzed commodities is heating oil, closely followed by WTI crude oil,…
The paper classifies U.S. crop types using hyperspectral satellite imagery.
problem Classifying crop types from hyperspectral satellite imagery.
method Gaussian Bayesian models and neural networks applied to NASA data.
result Bayesian methods outperform standard LDA and QDA.
Study examines grain futures connectedness during Russia-Ukraine conflict.
problem Quantile return connectedness of grain futures markets during geopolitical instability.
method Dynamic quantile VAR combined with frequency-domain decomposition.
result Heterogeneous spillovers across quantiles, with strong transmitters and persistent receivers.
Optimal transport and neural networks improve trade modeling accuracy.
problem Capturing subtler drivers of trade beyond supply and demand.
method Employing optimal transport and deep neural networks to learn a time-dependent cost function from data.
result Consistently outperforms traditional gravity models in accuracy.
Study analyzes climate impact on agricultural prices, offering insurance solutions.
problem Financial risk from climate-induced agricultural price volatility.
method Historical and future climate projections, EGARCH and SARIMAX models, Black-Scholes framework.
result Improved agricultural risk modeling and insurance mechanisms.
Long-Range Dependence in Financial Markets: Empirical Evidence and Generative Modeling Challengesq-fin.ST
Study finds long-range dependence in financial markets, but deep generative models struggle to replicate it.
problem Long-range dependence in financial markets and challenges of deep generative models.
method Empirical analysis of financial data from three sectors, including LRD through various statistical methods and deep learning models.
result Deep generative models can reproduce stylized features but fail to capture long-range dependence structures.
On Possible Influence of Space Weather on Agricultural Markets: Necessary Conditions and Probable Scenariosphysics.ao-ph
We present the results of study of a possible relationship between the space weather and terrestrial markets of agricultural products. It is shown that to implement the possible effect of space weather on the terrestrial harvests and prices, a simultaneous fulfillment of three conditions is required: 1) sensitivity of …