Suresh Raja Neethirajan

This paper develops a digital-twin framework that integrates video-derived feeding behavior with physiological data to improve precision nutrition in dairy cows, showing reduced prediction errors in dry matter intake.

This paper evaluates transfer learning for bovine facial pain recognition under weak labels, finding that protocol design and operating-point choices outperform pretraining.

This paper evaluates dairy cow vocalization models across farms, showing that reliability varies by vocalization type and that cross-farm generalizability is limited for certain categories.

The paper develops a video-based framework for detecting and classifying cattle behaviors to support digital twin systems in precision dairy farming.

This paper develops an AI system to decode bovine vocalizations and behavior using multimodal data, enabling real-time monitoring for improved livestock management and sustainability.

This paper uses NLP and transformer models to analyze poultry vocalizations, achieving 92% accuracy in classifying calls and revealing emotional and functional nuances.

Dairy DigiD uses deep learning and facial images to classify dairy cattle into four age categories with high accuracy, offering a non-invasive alternative to traditional identification methods.

This paper develops a cloud-based IoT system for livestock health monitoring using AWS and Python, demonstrating its scalability and effectiveness in tracking health indicators and enabling predictive analytics.

This study develops an AI-driven benchmarking tool to reduce methane emissions in Canadian dairy farms using machine learning and geospatial analysis, showing LSTM models provide the most accurate emission forecasts.

This paper uses the WHISPER NLP model to classify chicken vocalizations into emotional states, enabling non-invasive welfare monitoring in poultry farming.

This study uses CNN and MFCC to analyze stress-induced vocalizations in laying hens, finding age-related differences in vocal patterns under visual and auditory stressors.

This paper develops a paper-based biosensor using aptamers and gold nanoparticles to detect food allergens rapidly and sensitively, achieving nanogram-level detection without specialized equipment.

This paper develops an electrochemical immunosensor for detecting avian influenza A virus strains H5N1 and H4N6 in whole blood with improved sensitivity compared to commercial ELISA.

This paper develops a chiro-immunosensor using self-assembled chiral gold nanohybrids and quantum dots to detect viruses with picomolar sensitivity.

This study develops a paper-based microfluidic device for rapid and sensitive detection of norovirus using aptamer-labeled nanomaterials, achieving low detection limits and high specificity.

This paper introduces a chitosan-based biosensor that detects H2O2, glucose, and lactate with high sensitivity using a peroxidase-like activity on a paper-based platform.

This paper develops an electrochemical immunosensor for rapid influenza virus antigen detection in poultry farms, achieving high sensitivity and fast response times.

This study uses a microfluidic chip to analyze single-cell chemotactic responses of Helicobacter pylori to urea, showing its potential for studying bacterial chemotaxis.

This paper describes the synthesis and characterization of graphene oxide nanosheets decorated with hybrid Ag-Ru/chitosan nanoparticles, showing enhanced optical and electrochemical properties for biosensing applications.