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40 results for “Yield Prediction”
Ensemble BLUP, Machine Learning, and Deep Learning Models Predict Maize Yield Better Than Each Model Alone.
<p>Data and scripts exploring ensembling strategies using the models developed in <a href="https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkad006/6982634">Kick et al., 2023</a> (see also <a href="https://zenodo.org/record/7401113">1</a>, <a href="https://zenodo.org/record/6916775">2</a>). Download all files to a single directory then run setup.sh or manually unzip using tar.</p> <p> </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>setup.sh</td> <td>Simple script that unzips zipped directories</td> </tr> <tr> <td>ext_data</td> <td>Reduced data from Kick et al. 2023</td> </tr> <tr> <td>ext_data_notebooks</td> <td>Contains python notebooks containing analysis and R markdown file containing visualization of results. Python and R data objects are written to allow results to be read in instead of re-generated.</td> </tr> <tr> <td>output</td> <td>Folder containing a placeholder file.</td> </tr> </tbody> </table> <p> </p> <p>This research used resources provided by the United States Department of Agriculture’s Agricultural Research Service (project number 5070-21000-041-000-D). The SCINet project of the USDA Agricultural Research Service (project number 0500-00093-001-00-D) was instrumental in the training of the models used in this work. In addition, we would like to acknowledge those presently and historically involved in generating data for the Genomes to Fields Initiative.</p> <p> </p> <p> </p> <p> </p>
Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model
Open the record for dataset details and reuse information.
Data from: Prediction of maize grain yield before maturity using improved temporal height estimates of unmanned aerial systems
Open the record for dataset details and reuse information.
High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023
<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>
Dataset for "Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision"
<h2>Overall</h2> <p>A strawberry dataset for the paper "Qi Yang, Licheng Liu, Junxiong Zhou, Mary Rogers, Zhenong Jin, 2024. Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision, Computers and Electronics in Agriculture, 220, 108911. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compag.2024.108911" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compag.2024.108911</a>"</p> <h2>Plant traits measurements</h2> <p>The folder "measurement.zip" includes treatment-level and fruit-level ground truth data. </p> <h3>Treatment-level</h3> <pre><code>data_dryMatter_2022.csv data_dryMatter_2023.csv data_freshMatter_2022.csv data_freshMatter_2023.csv data_fruitNumber_2022.csv data_fruitNumber_2023.csv data_plantBiomass_2022.csv data_plantBiomass_2023.csv</code></pre> <h3>Fruit-level</h3> <p>Fruit conditions with five classes, 1-5 represent Normal, Wizened, Malformed, Wizened & Malformed, and Overripe, respectively.</p> <pre><code>data_size_freshWeight_condition_2022_0N.csv data_size_freshWeight_condition_2022_50N.csv data_size_freshWeight_condition_2022_100N.csv data_size_freshWeight_condition_2022_150N.csv</code></pre> <p>Fruit size for tagged fruits</p> <pre><code>data_taggedFruit_diameter_2022.csv data_taggedFruit_diameter_2023.csv data_taggedFruit_length_2022.csv data_taggedFruit_length_2023.csv</code></pre> <p>Fresh yield and lifespan for tagged fruits (only available in experiment 2023)</p> <pre><code>data_taggedFruit_freshMatter_2023.csv data_taggedFruit_lifespan_2023.csv</code></pre> <h3>Weather data</h3> <pre><code>weather_daily_2022.csv weather_daily_2023.csv</code></pre> <h2>Image data with label</h2> <h3>Object and phenology detection</h3> <p>The folder "strawberry_img_random.zip" contains images and the corresponding JSON labels for object and phenological stages detection.</p> <h3>Fruit size and decimal phenological stage</h3> <p>The folder "strawberry_img_tagged.zip" contains images and the corresponding JSON labels for fruit size and decimal phenological stages detection.</p> <pre><code>For example, "label": "small g, 8.84, 7.62, 0.4", This label means the fruit has an 8.84mm diameter and 7.62mm length, with the main stage being small green and the decimal stage being DS-4 </code></pre> <h3>Merge and split Data</h3> <p>A Python script, "datasetProcessing.py", can be used to merge and split the image data into training and testing set.</p> <h3>Pre-trained models</h3> <p>models.zip</p> <p> </p> <p><em>Data collector: Dr. Qi Yang, University of Minnesota, USA. Email: qiyang577@gmail.com</em></p> <p><em>All the files belong to Prof. Zhenong Jin, University of Minnesota, USA. Email: jinzn@umn.edu</em></p>
A Model Predicting The Relationships Between Seed Size, Yield, and Actual Yield in Dry Field Peas
<p>This is a model that predicts yield and actual yield in dry pea based on seed size. Seeding rate, seed cost, pod length, and expected grain yield can be varied in the model. The ideal seed size is displayed at the vertex of the curve in three different graphs. </p>
Data from: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
<p>Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5-17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.</p>
Supporting data for: Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics
Open the record for dataset details and reuse information.
Data from: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices
Open the record for dataset details and reuse information.
Dataset and codes for "BaHSYM: parsimonious Bayesian Hierarchical Model to predict river Sediment Yield"
<p>This folder contains:</p> <ul> <li>R project file</li> <li>R code for Best Fit model</li> <li>R code for temporal cross-validation</li> <li>R code for spatial cross-validation</li> <li>R code for cluster analysis</li> <li>dataset containing all input variables for the river gauges (and catchments) used for the development and testing of the BaHSYM model in Austria</li> </ul> <p>It also contains the same codes and datasets adapted to reproduce the model by de Vente et al. (2011), i.e. with the same structure but with the variables used in such model.</p>
Characterization of descriptors in machine learning for data-based sputtering yield prediction
<p>descriptor and target variables</p>
Characterization of descriptors in machine learning for data-based sputtering yield prediction
<p>table I-V</p>
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Crop growth models and remote sensing are useful tools for predicting crop growth and yield, but each tool has inherent drawbacks when predicting crop growth and yield at a regional scale. To improve the accuracy and precision of regional corn yield predictions, a simple approach for assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) products into a crop growth model was developed, and regional yield prediction performance was evaluated in a major corn-producing state, Illinois, USA. Corn growth and yield were simulated for each grid using the Crop Environment Resource Synthesis (CERES)-Maize model with minimum inputs comprising planting date, fertilizer amount, genetic coefficients, soil, and weather data. Planting date was estimated using a phenology model with a leaf area duration (LAD)-logistic function that describes the seasonal evolution of MODIS-derived leaf area index (LAI). Genetic coefficients of the corn cultivar were determined to be the genetic coefficients of the maturity group [included in Decision Support System for Agrotechnology Transfer (DSSAT) 4.6], which shows the minimum difference between the maximum LAI derived from the LAD-logistic function and that simulated by the CERES-Maize model. In addition, the daily water stress factors were estimated from the ratio between daily leaf area/weight growth rates estimated from the LAD-logistic function and that simulated by the CERES-Maize model under the rain-fed and auto-irrigation conditions. The additional assimilation of MODIS data-derived water stress factors and LAI under the auto-irrigation condition showed the highest prediction accuracy and precision for the yearly corn yield prediction (R2 is 0.78 and the root mean square error is 0.75 t ha-1). The present strategy for assimilating MODIS data into a crop growth model using minimum inputs was successful for predicting regional yields, and it should be examined for spatial portability to diverse agro-climatic and agro-technology regions.
Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat
<p>It contains supplementary materials(revised version)and supporting data of Tables of Prediction of Yield and Nitrogen-Use Efficiency by Using Consumer-Grade Unmanned Aerial Vehicle Multispectral Images of Winter Wheat.<em> </em>However, the artical has not published. Data is available upon request.</p> <p>If you need anything, please don't hesitate to contact me(liujk@ahstu.edu.cn).</p>
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Fine tuning N recommendations requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. Recent studies have demonstrated the ability of Normalized Difference Vegetation Index (NDVI) to assess crop N status and predict yield in wheat (Tricticum aestivum L.) and maize (Zea mays L.); however, there has been relatively little such research on rice (Oryza sativa L.). The objectives of this study were to determine how well NDVI measured at the panicle initiation (PI) rice growth stage assesses crop N status and predicts final grain yield. Nitrogen response trials were established over a 4-yr period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. Additionally, the relationship between NDVI and crop N status was characterized across 28 on-farm plots representing a range of environmental conditions and management practices. The NDVI at PI was best correlated with total N uptake (NUP, r2 = 0.66), followed by N concentration (NCONC, r2 = 0.54), and aboveground biomass (AGB, r2 = 0.51). The utility of NDVI was greatest at lower values of crop N status, whereas at higher values, NDVI saturated. The NDVI at PI was positively correlated with final grain yield (r2 = 0.58) indicating utility for developing in-season yield predictions. While NDVI is a potentially useful tool to improve N fertilizer management and develop in-season yield predictions in rice, alternative indices that do not saturate would likely provide a basis for a better tool.
Dataset for machine learning guided prediction of the yield strength and hardness of multi-principal element alloys
<p>These data sets were used to develop machine-learning models to predict yield strength and hardness of multi-principal element alloys. We mainly collected the alloys and their mechanical properties from different published works. A list of references is provided at the end of each data set.</p>
Factors Predicting Positive Yields on Biliary Brush Cytology During Endoscopic Retrograde Cholangiopancreatography
ClinicalTrials.gov study NCT00894348. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Assimilating MODIS data-derived minimum input data set and water stress factors into CERES-Maize model improves regional corn yield predictions
Open the record for dataset details and reuse information.
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Open the record for dataset details and reuse information.
Data and Script for: Olive Trees Health and Yield Prediction through EO data and Machine Learning (OLEA-PRED) / EO AFRICA – Research and Development Facility
<p>This database was collected under the "Olive Trees Health and Yield Prediction through EO data and Machine Learning" project, funded by the European Space Agency in the framework of the "EO AFRICA R&D Facility".</p> <p>- Shapefiles.rar contains shp files for Orchads boundaries and tree locations.</p> <p>- Field_data contains data collected in the field (Chllorophyl, Yield and Soil ).</p> <p>- SCRIPTS.rar contains all scripts and extracted data used for prediction for Settat orchad.</p> <p>- FBS images.rar contains UAV and M6 images for Fkih Ben Saleh orchad.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.