Programme Parameters
Graduate / Intermediate
3 Weeks (30 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Agriculture shows how machine learning is transforming food production from planting to harvest. You work with the data sources that define modern farming — satellite and drone imagery, weather, soil and sensor readings — and build models for the field’s core problems: monitoring crop health and soil, detecting disease and pests from images, and predicting yield. The course connects these models to real decisions in precision agriculture, where inputs like water and fertiliser are targeted rather than uniform. You finish able to design an AI solution for a genuine agricultural problem. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to agriculture — crop and soil monitoring with remote sensing, disease and pest detection, yield prediction and precision-farming decision support.
📋 Course Objectives
1. Work with satellite, drone and sensor agricultural data.
2. Monitor crop health and soil condition with remote sensing.
3. Detect crop disease and pests from images.
4. Build yield-prediction models.
5. Support precision-farming input decisions.
👥 Who Should Enroll?
• Agri-tech developers and data scientists
• Agronomists and precision-farming professionals
• Researchers in agricultural technology
• Students specialising in agri-analytics
🚀 Key Learning Outcomes
• The ability to apply AI to an agricultural problem.
• A crop-monitoring or yield-prediction project.
• Domain-aware agri-analytics skills.
• 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
Grounding Models in Crop Science
• Crop growth stages, yield-limiting factors and agronomic decision points
• Soil, nutrient and water constraints that bound any model recommendation
• Why a statistically strong model can still give agronomically wrong advice
Field, Drone and Satellite Data
• Vegetation indices from multispectral imagery and their saturation limits
• Drone survey planning, radiometric calibration and mosaicking
• Soil sensors, weather stations and the sparsity of ground truth
Crop Health and Pest Management
• Disease and pest identification from field imagery, and lookalike confusion
• Weed detection for targeted spraying and the economics of precision application
• Early stress detection and separating water stress from nutrient deficiency
Yield and Advisory Systems
• Yield prediction across seasons and its transferability between regions
• Irrigation scheduling and fertiliser recommendation engines
• Advisory delivery to smallholders under low-connectivity, low-literacy conditions
Making It Work on Real Farms
• Farm data ownership, platform lock-in and cooperative models
• Cost of adoption against realistic yield gains at smallholder scale
• Evaluating impact in the field rather than on a validation set
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | ArcGIS |
| Covered Tool / Platform | CropSyst |
| Covered Tool / Platform | DSSAT |
| Covered Tool / Platform | QGIS |
| Covered Tool / Platform | Drone Technology |
Frequently Asked Questions
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