The thrust areas of AgriHub include but are not limited to
Genomic-based crop improvement
This area involves using genomics to identify genes and traits that can help crops become more productive and resilient to environmental stressors. This includes identifying markers for disease resistance, drought tolerance, and nutrient use efficiency.
Genomic visualization and assembly tool for clustering of SNP for different crops.
Genome Wide Association Studies tool for special traits.
An advanced tool for molecular breeding for various crops.
Phenomics-based crop improvement
Study plant traits, including growth patterns, root architecture, and physiological responses to different environmental conditions.
Technologies to identify the most promising crop varieties for specific environments and conditions.
Crop Yield Prediction.
Data repository and analysis
This area involves the development of cloud-based repositories for genomics and phenomics data, along with tools for data analysis and visualization. This includes facilitating the discovery of new patterns and correlations in crop data.
Automated platforms for sequencing and data analysis
Use of high-throughput sequencing and automated data analysis platforms to accelerate the pace of crop improvement.
Precision agriculture
Soil Mapping: Mapping soil attributes such as moisture content, organic matter, and pH levels to make better irrigation schedules, decisions around inputs and management practices.
Crop Monitoring: Using sensors and remote sensing technology to monitor plant growth, detect early signs of stress, groundwater management system for crop cycle management, and optimize irrigation and nutrient management.
Yield Mapping: Mapping yield variability within fields to identify areas of high and low performance and to optimize inputs for better yields.
AI-based disease and pest diagnosis
Automated Diagnosis: Developing AI algorithms to detect and diagnose crop diseases and pests from images or other data sources.
Disease Prediction: Using machine learning to analyze historical data and environmental factors to predict disease outbreaks and pest infestations.
Decision Support: Providing farmers with real-time recommendations based on AI analysis, including pest control strategies and crop protection measures.
Drone application and image Analysis
Aerial Imaging: Using drones to capture high-resolution images of crops and fields, which can be analyzed for plant health, yield prediction, and crop mapping.
Crop Spraying: Developing drone-based crop spraying technology to precisely apply inputs such as pesticides and fertilizers, reducing waste and improving efficiency.
Field Inspection: Using drones for rapid field inspections, such as counting plants, measuring growth rates, and detecting anomalies.
Smart Agri Robots for various applications.
AI-powered demand and supply forecast
Market Analysis: Using AI to analyze market trends and demand patterns to help farmers make informed decisions around crop selection and production.
Weather Forecasting: Analyzing weather patterns and data to predict market demand and optimize planting schedules.
Supply Chain Optimization: Using AI to optimize supply chain logistics and distribution, reducing and reusing agriculture waste, and improving efficiency.