Programme Parameters
Advanced Postgrad
3 Days (4.5 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This intensive 3-day course bridges the gap between agronomy and data science, designed specifically for researchers and industry professionals. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This intensive 3-day course bridges the gap between agronomy and data science, designed specifically for researchers and industry professionals.
📋 Course Objectives
1. Translate AI in Industry & Manufacturing theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI in Industry & Manufacturing
• R&D engineers and working professionals applying AI in Industry & Manufacturing in industry
• Academics and educators building research or teaching capacity in AI in Industry & Manufacturing
🚀 Key Learning Outcomes
• Tangible, reproducible AI in Industry & Manufacturing work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Instrumenting a Field
• Soil moisture, temperature, humidity and weather sensors and their accuracy
• Calibration, drift and the sensor placement that makes data meaningless
• LoRaWAN, NB-IoT and connectivity in areas with no reliable network
Seeing the Crop from Above
• Satellite and drone imagery, and the resolution each provides
• NDVI, NDRE and their saturation at high canopy cover
• Cloud cover, atmospheric correction and revisit interval as practical limits
Prediction on Agricultural Data
• Yield prediction and the small number of seasons available as training data
• Disease and pest detection from images, and field conditions against clean datasets
• Spatial and temporal autocorrelation, and why random splits overstate accuracy
Building the Pipeline
• Edge against cloud processing when bandwidth and power are constrained
• Data storage, gateway design and handling intermittent connectivity
• Dashboards and alerts that a farmer will actually act on
Making It Work in Practice
• Smallholder economics and the cost ceiling for any deployed system
• Agronomic validation with trials rather than model accuracy alone
• Data ownership, advisory liability and trust in automated recommendations
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Pandas |
| Covered Tool / Platform | NumPy |
| Covered Tool / Platform | Matplotlib |
| Covered Tool / Platform | XGBoost |
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