A Bayesian Conditional Diffusion Regression Framework with Hierarchical Global-Local Priors for Robust Agricultural Image-to-Yield Prediction

Kumar Ch; Upender reddy G; Varalakshmi K

doi:10.5269/bspm.82344

A Bayesian Conditional Diffusion Regression Framework with Hierarchical Global-Local Priors for Robust Agricultural Image-to-Yield Prediction

Auteurs-es

Kumar Ch
Upender reddy G Univeristy college of Engineering(A), Osmania Univeristy,
Varalakshmi K MJPTBCWR Degree College (W), Wargal, Siddipet-502279, Telangana, India.

DOI :

https://doi.org/10.5269/bspm.82344

Résumé

Accurateestimationofcropyieldfromimage-deriveddataremainsdifficultduetothecombined
effectsofhighpredictordimensionality,stronginter-featuredependence,non-standardnoisebehavior,andthe
presenceofanomalousobservations.Manycommonlyusedregressionapproaches, includingBayesianmodels
withGaussianerrorassumptions,struggletoremainstableundertheseconditionsandoftenprovideunreliable
uncertaintyestimates.ThisworkpresentsBayesianConditionalDiffusionRegression(BCDR),aprobabilistic
modelingframeworkthatapproachesregressionthroughconditional responsegeneration. Themethodinte
gratesadiffusion-basedmechanismformodelingtheresponsevariablewithhierarchicalglobal–localshrinkage
tocontrolmodelcomplexity,whileaheavytailedlikelihoodimprovesrobustnesstooutliers. Posterior infer
enceiscarriedoutusingahybridGibbsandHamiltonianMonteCarlostrategytoenabletractableestimation
inhigh-dimensionalsettings.Experimentsconductedonsyntheticdatasetsandarealagriculturalcasestudy
involvingpomegranateimagesandfruitweightmeasurementsdemonstrateimprovedpredictiveaccuracy,bet
teruncertaintycalibration, andstronger resistancetodatacontaminationwhencomparedwithestablished
statisticalandmachine-learningbaselines.TheseresultssuggestthatBCDRisarobustBayesianframework
forimage-basedyieldprediction

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Publié

2026-06-19

Numéro

Vol. 44 No. 17 (2026): Recent Trends in Mathematical Sciences and Technological Applications

Rubrique

Conf. Issue: Recent Trends in Mathematical Sciences and Technological Applic.

Licence

Cette œuvre est sous licence Creative Commons Attribution 4.0 International.

When the manuscript is accepted for publication, the authors agree automatically to transfer the copyright to the (SPM).

The journal utilize the Creative Common Attribution (CC-BY 4.0).

Comment citer

Ch, K., G, U. reddy, & K, V. (2026). A Bayesian Conditional Diffusion Regression Framework with Hierarchical Global-Local Priors for Robust Agricultural Image-to-Yield Prediction. Boletim Da Sociedade Paranaense De Matemática, 44(17), 1-13. https://doi.org/10.5269/bspm.82344