Role of AI in seed science Predictive modelling and Beyond

2. Presented By –
Nayeem Ul Bashir
31/Ag(SST)/22-M
M.Sc 2nd Year
Divison of Genetics
and Plant breeding

3. What is Artificial Intelligence ?
Artificial Intelligence
(AI) refers to the
simulation of human
intelligence in
machines that are
able to think, learn,
and act intelligently.
These systems utilize advanced computational methods, such as
 machine learning
 natural language processing
 cognitive computing
It enable them to
perform tasks
that typically
require human-
like perception,
decision-making,
and interaction.

4. Overview of Seed Science Predictive Modeling
Predictive modeling in seed science aims to forecast characteristics,
behavior or outcomes related to seeds, often utilizing machine learning
algorithms.
One example of this application involves building predictive models
to determine seed classes, which can enhance crop production and
improve farming practices.
Researchers commonly employ artificial neural networks (ANNs) to
analyze large sets of seed data and create models that accurately predict
seed classes.

5. Significance of AI in Seed Science
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6. Historical Perspective
• Seed science has evolved significantly
over time, with traditional methods
giving way to more advanced
techniques, including the integration of
artificial intelligence (AI).
• Historically, seed science focused on
understanding seed dormancy,
germination, and plant biology processes
using conventional statistical methods .
Evolution
of Seed
Science.

7. • Traditional methods in seed science involved analyzing data from
dormancy and germination studies using basic statistical
approaches.
• These methods often fell short in fully understanding the intricate
processes involved in seed biology due to limitations in studying
multiple factors simultaneously.
• They relied on statistical regressions and simple algorithms to
interpret data, which could not capture the complexity of seed
germination and dormancy interactions
Traditional
Methods in
Seed
Science

8. • The integration of AI in seed science has brought
significant advancements in predictive modeling,
understanding seed traits, and optimizing crop
production practices.
• AI tools, such as artificial neural networks (ANN)
shave enabled researchers to predict seed germination,
optimize dormancy processes, and model complex
biological interactions more accurately.
• By leveraging AI technologies, seed scientists can
enhance their ability to analyze large datasets, predict
seed performance, and optimize breeding strategies for
improved crop productivity
Emergence
and
Integration
of AI

9. AI Techniques in Seed Science
A. Machine Learning Algorithms
Supervised Learning for Seed Quality Prediction:
Researchers in Brazil have developed a methodology
based on artificial intelligence to automate and
streamline seed quality analysis using machine learning.
By acquiring images of seeds through light-based
technology and employing chemometrics and machine
learning, they were able to classify seed quality based on
chemical composition with high accuracy

10. Unsupervised Learning for Pattern Recognition:
Artificial intelligence tools, such as artificial neural
networks (ANNs) combined with fuzzy logic, have been
utilized to model and predict seed germination and
dormancy processes. These AI tools offer advantages
over traditional statistical methods by enabling the study
of complex interactions among multiple factors in seed
biology

11. B. Deep Learning Applications
Neural Networks in Germination Prediction:
• Artificial neural networks (ANNs) have been applied to
predict plant biology processes like seed germination.
This technology allows for the modeling and prediction
of seed dormancy release and germination, enhancing
the understanding of these critical processes in seed
science

12. Convolutional Neural Networks (CNN) for Seed
Image Analysis:
Convolutional Neural Networks (CNNs) have
been used for seed image analysis, enabling the
identification of seed varieties, species, and
abnormalities. By leveraging various imaging
techniques and machine learning algorithms,
researchers can distinguish different seed
characteristics effectively.

13. Advancements in Seed Science Predictive
Modeling
Precision agriculture techniques, driven by
AI, aim to optimize crop production by
offering smart farming techniques and AI-
driven crop monitoring. Examples include:
• Smart Farming Techniques: Utilization of AI algorithms
to manage irrigation, fertilizer application, and pest
control, thus improving crop yields and reducing waste
• AI-driven Crop Monitoring: Real-time monitoring of
crop health and growth, facilitating early intervention
and prevention of issues
Precision Agriculture and AI

14. Genomic data and AI are driving
improvements in seed trait
prediction and optimization. Notable
developments include:
Employing genetic
algorithms to accelerate the breeding process
and produce superior crop varieties
Genomic Data and AI

15. Future Direction of Seed
Science Predictive Modeling
A. Integration of AI with Other Technologies
•Integrating AI with existing technologies, such as
remote sensing, drones, and IoT devices, will expand
the capabilities of seed science predictive modeling.
•For example, AI-assisted crop monitoring and
precision agriculture will leverage these technologies
to optimize crop production and minimize waste

16. B. Potential Impact on Global Agriculture
• AI-enhanced seed science will contribute to addressing
global challenges, such as climate change, environmental
concerns, and rising demands for food.
• AI's ability to improve efficiency, sustainability, and
resource allocation makes it a promising tool for meeting
the needs of a rapidly growing global population

17. C. Research and Development Prospects
• Research and development efforts should prioritize creating
accurate and reliable predictive models, integrating AI with
other disciplines, and ensuring that AI-driven solutions are
accessible and affordable for farmers worldwide.
• Additionally, addressing ethical considerations in genetic
modification and data privacy concerns will be crucial for the
successful implementation of AI in seed science.

19. Case Study 1
Objectives: The study aims to develop an automated system for quality testing of corn seeds..

20. Materials & Methods: The researchers created a novel seed image acquisition setup capturing top
and bottom views of seeds, utilized a Conditional Generative Adversarial Network (CGAN) to
generate realistic images for underrepresented classes, and employed Batch Active Learning (BAL)
for efficient data annotation.

21. Result: The proposed system achieved up to 91.6% accuracy in testing the physical purity of seed samples.

22. Conclusion
In exploring the intersection of Artificial Intelligence (AI) and Seed
Science Predictive Modeling, several key findings have emerged:
• AI technologies, such as machine learning and deep learning
algorithms, are revolutionizing seed science by enabling precise
prediction of seed traits, optimizing breeding strategies, and enhancing
crop productivity.
• The integration of AI with other technologies, such as remote sensing
and genetic algorithms, is expanding the scope of predictive modeling
in seed science.
• Challenges such as data quality and availability, ethical considerations in
genetic modification, and the need for interdisciplinary collaboration
must be addressed to fully leverage the potential of AI in seed science.

23. THANK YOU