Search results for "Yield prediction"

showing 3 items of 3 documents

Downscaling rice yield simulation at sub-field scale using remotely sensed LAI data

2019

Abstract Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel–2A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m × 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluate…

0106 biological sciencesSoil SciencePlant Science01 natural sciencesYield (wine)WARM modelCrop modelLeaf area indexCropping systemDecision support systemRemote sensing2. Zero hungerCrop yieldYield predictions04 agricultural and veterinary sciencesRemote sensing15. Life on landAgronomyData assimilation040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental sciencePrecision agricultureScale (map)Agronomy and Crop ScienceCropping010606 plant biology & botanyDownscalingEuropean Journal of Agronomy
researchProduct

The upgraded ISOLDE yield database – A new tool to predict beam intensities

2020

At the CERN-ISOLDE facility a variety of radioactive ion beams are available to users of the facility. The number of extractable isotopes estimated from yield database data exceeds 1000 and is still increasing. Due to high demand and scarcity of available beam time, precise experiment planning is required. The yield database stores information about radioactive beam yields and the combination of target material and ion source needed to extract a certain beam along with their respective operating conditions. It allows to investigate the feasibility of an experiment and the estimation of required beamtime. With the increasing demand for ever more exotic beams, needs arise to extend the functi…

Radioactive ion beamsNuclear and High Energy PhysicsYieldsComputer sciencecomputer.software_genre114 Physical sciences01 natural sciencesISOLDEDatabaseFLUKACERN0103 physical sciencesddc:530Production Yield010306 general physicsInstrumentationLarge Hadron ColliderDatabase010308 nuclear & particles physicsIn-target productionYield predictionCross sectionsYield (chemistry)ABRABLAIONIZATIONRelease efficiencycomputerRadioactive beamBeam (structure)Radioactive beamsNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
researchProduct

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

2021

Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…

hybrid neural networkSVDP::Landbruks- og Fiskerifag: 900::Landbruksfag: 910farm-scale crop yield prediction; deep learning; hybrid neural network; convolutional neural network; recurrent neural network; Sentinel-2 satellite remote sensing datadeep learningconvolutional neural networkSentinel-2 satellite remote sensing datarecurrent neural networkAgriculturefarm-scale crop yield predictionAgronomy and Crop ScienceAgronomy
researchProduct