Digital Livestock Farming
Mooanalytica Features
Our Goals
To create new insights, concepts, processes and solve problems in agri-food industry using instrumentation and sensing approaches. Better understand the livestock’s ability to endure their environments’ selective pressures using advanced sensors and data driven models.
Our Vision
To be recognized as a leader in the Digital Livestock Farming via contribution to the development and application of sensor technologies and sensor-enabled AI big-data models for enhancing livestock welfare and health and agri-food applications.
Our Projects
Dr. Suresh’s research focuses on developing leading-edge, innovative technological solutions that improve the digitization of the agri-food animal health and welfare; Transforming laboratory inventions into practical industrial solutions using instrumentation, actionable analytics, end-to-end data management and modeling.
Teaching
Dr. Suresh is involved in teaching graduate and undergraduate students; supervising BSc, MSc and PhD thesis projects; seminar courses; regularly provides guest lectures on topics concerning instrumentation and sensors technologies.
Our Facilities
Directed by Dr. Suresh, Mooanalytica has priority access to several state-of-the-art research laboratories within The Netherlands.
Innovation
- Sensors for On-site Detection of Coronavirus
- Sensor for Avian Influenza Detection for Poultry
- Cattle Health and Disease Management and Decision Support Tools
- Sensors for Food Allergen Detection
- Automated Animal Emotions Measurement Platforms
- Paper & Thread as Matrix for Biomedical Sensing Applications
- Genome based Detection for Johne’s and Crohn’s Disease – LAMP Sensor
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Sensing Tools to Understand Farm Animal Behavioural Traits and Their Cognition
Emotional contagion could be a powerful tool for improving animal welfare by inducing positive emotions or preventing high-arousal emotions. Livestock perception of emotional cues can be only measured using sensing tools and instruments. Dr. Suresh has developed a Python and Yolo based platform called ‘WUR WOLF – An Automated Facial Recognition Coding System’ for measuring emotions from farm animals using big data and visual images and videos of cows and pigs from farms. Results would help animal husbandry systems adjust for farm animal preferences and enhance their welfare, improving group coordination and strengthening social bonds of livestock.
Non-Invasive Physiological Monitoring of Bodily Functions
A number of sensing platforms and support tools are currently under development for non-invasive and rapid animal side measurement of physiological parameters. Some of these includes Acoustic Respiratory Monitoring Sensors; Fall Detection Sensing Tools; Saliva, Sweat, Breath and Sputum (Nasal Discharge) Based Biomarker Diagnostics; Blood Oxygen Saturation, Blood Pressure Pulse Oximetry; Wearable ECG for Heart Variable Activity; Wearable Sensors for Biochemical and Physiological Profile Assessment; Sensors for Measuring Involuntary Physiological Signals; Electrical Signals by Brain, Heart, Muscles, Skin.
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Sensors and Biosensing Assays to Measure MicroRNAs
Dr. Suresh and his team has developed MiRNA based sensors for predicting breast cancer from blood samples of post and pre-menopausal women through collaboration with human hospitals; MiRNA sensor for measuring markers for Mycobacterium Paratuberculosis in Dairy Cattles. Based on this previous research experience, Mooanalytica is currently focused on developing sensing solutions for solving problems related to dry secretions in cows, muscle tissue development, lactation, Heat stress, milk fat metabolism, pathogenesis predictions.
Sensors for measuring Behavioural change and activity Tracking
- Parnutrition Measurement Using Accelerometer Ear Tags
- Quadcopter Vision System for Grazing Animals in Farms
- Photoplethysmography Instrument for Heat Detection in Livestock
- Development of Models for Circadian Rhythms Assessment in Dairy Cows
- Vocalization Monitoring System for Determining Pig and Poultry Welfare
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Wearable Sensors for Measuring the Physiological Profile of Farm Animals
- Measuring Biomarkers from Sweat or Saliva
- Measuring Mental Acuity and Stress
- Comparative Analysis of Biomarker Sources for Assessing
- Animal Behaviour and Traits
- Multimodal physiological analysis (heart rate variability, galvanic skin response and biochemical profile changes)
Models
- Sensors to determine Individual Physiological Adaptations vs Group Physiological Adaptations
- Resilience Sensing Models – Per Animal vs Per Herd Approach
- Models developed with Sensor Data for Enclosure Experiments and Community Composition Dynamics
- Develop and Evaluate the Use of Non-Contact Vital Signs Technology to Measure Physiological Parameters in Dairy and Swine
- Design and Develop a Subcutaneous Insert Device for Non-Contact Measurement of inflammation biomarkers in cattle to detect early infection
- To Establish the Data Management Systems necessary for early illness and physiological functions detection of farm animals
- Protocols and Predictive Tools Based on Sensor Data and Models
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Past Projects
Pig Emotion Recognition with a Deep Neural Network
Both farmers and engineers collaborate and improve pig welfare conditions with pig emotion recognition (PER). Applying the PER can reduce the amount of labor expenditure and stress among domestic pigs without frequent human intervention. However, the unprocessed PER...
Big Data Enabled Non-Invasive Rapid Sex Detection of Incubated Chicken Eggs
Paper (Link) Current commercial, pre-commercial, and experimental in ovo techniques for the sex determination of fertilised eggs employ either minimally invasive biomolecular assays (extracting fluid via a small laser-drilled window in the eggshell, for detection of...
ChickTrack – Digital tracking tool for chicken activity
ChickTrack – A quantitative tracking tool for measuring chicken activity Paper (Link) The automatic detection, counting and tracking of individual and flocked chickens in the poultry industry is of paramount to enhance farming productivity and animal welfare. Due to...