AED
UAD Dirham
Code: AED
Self Paced
AI and Machine Learning in Crop Genomics
Growing the Future: AI-driven Innovations in Crop Genomics
Early access to the e-LMS platform is included
About This Course
Explore how AI and machine learning are revolutionizing agricultural genomics through this next-generation DeepScience program. Tailored for scientists, researchers, and technologists in agriculture, genetics, and data science, this program unveils the power of predictive analytics, AI-driven genomic modeling, and genome-wide data processing to accelerate crop improvement and resilience.
Participants will engage in the structured learning of genomic architectures, AI modeling techniques, and bio-computational tools to interpret crop trait variability, disease resistance, and performance optimization. By aligning biological data with algorithmic precision, the course equips learners to design future-ready solutions in precision agriculture and food sustainability.
Aim
To cultivate AI-centric expertise for solving agricultural challenges through the integration of machine learning techniques and crop genomic insights, fostering innovation in genetic trait prediction, resilient crop breeding, and data-guided agricultural decision systems.
Program Objectives
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Apply AI/ML to analyze genomic data for crop yield, resistance, and optimization
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Construct predictive models to guide genomic selection strategies
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Leverage large-scale genetic datasets to inform breeding programs
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Advance sustainable agriculture using AI-powered genome interpretation
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Understand the ethical and ecological implications of DeepTech in agri-genomics
Program Structure
Week 1: Introduction to Crop Genomics and AI
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Role of genomics in agricultural transformation
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Genomic data types: sequence, SNPs, gene expression
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AI fundamentals: learning types and model structures
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Use cases of AI in crop yield forecasting and trait optimization
Week 2: Genomic Data Sources and Preparation
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Genomic data acquisition: platforms and repositories
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Data integrity: preprocessing, normalization, and feature extraction
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Structuring datasets for AI modeling
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Privacy, ethics, and responsible data stewardship
Week 3: Machine Learning Algorithms for Crop Genomics
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Trait prediction using supervised learning models
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Algorithm overview: decision trees, SVM, random forests
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Accuracy metrics: confusion matrix, precision, recall
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Unsupervised learning: clustering, dimensionality reduction, GWAS insights
Week 4: AI in Genomic Forecasting and Future Trends
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Deep learning in agri-genomics: CNNs, RNNs
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AI-assisted genomic sequence interpretation
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Trends in responsible AI and bio-automation
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Final project briefing and applications in industrial and research settings
Who Should Enrol?
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Graduates and professionals in agriculture, biotechnology, bioinformatics, or computer science
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R&D specialists in genomic selection, crop breeding, or precision farming
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Data science enthusiasts seeking to innovate within the agricultural genomics domain
Program Outcomes
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Expertise in applying machine learning to crop genomics data
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Ability to create AI models for agronomic trait prediction
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Capacity to evaluate model performance using scientific benchmarks
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Readiness to contribute to DeepTech-enabled agriculture research and innovation
Fee Structure
Standard: ₹8,998 | $198
Discounted: ₹4499 | $99
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- One-on-one project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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