📚 Syllabus & Course Curriculum
Agriculture & Food Technology
Module-by-module breakdown of AI in Agriculture, from foundations to a certified capstone project.
Agronomy
• 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
Sensing
• 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
Detection
• 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
Prediction
• 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
Adoption
• 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
Earn government-registered certification in AI in Agriculture
e-Certificate and e-Marksheet issued on successful completion.
