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It is essential to upgrade traditional farming practices and prepare for a technological revolution to develop eco-friendly systems for enhancing crop productivity. This course aims to cover the various applications of machine learning and deep learning methods for better soil and crop management. This course is specifically designed for those undergraduate students who wish to understand and apply their knowledge of machine learning, deep learning, digital soil mapping, image processing, and portable sensors for developing an integrated and advanced soil and crop management system.PRE-REQUISITE: NoneINDUSRTY SUPPORT: Soil and crop testing services, Soil remote sensing solution services, AI-based agricultural startups.INTENDED AUDIENCE: STUDENTS OF AGRICULTURAL ENGINEERING, AGRICULTURE, AND ENVIRONMENTAL SCIENCE.
Overview
Syllabus
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Week 1: General Oerview Of Ml And Dl Applications In AgricultureWeek 2: Basics Of Multivariate Data AnalyticsWeek 3: Principal Component Analysis And Regression Applications In AgricultureWeek 4: Applications Of Classification And Clustering Methods In AgricultureWeek 5: Diffuse Reflectance Spectroscopy: Basics And Applications For Crop And SoilWeek 6: Use Of Ml For Portable Proximal Soil And Crop SensorsWeek 7: Ml And Dl For Soil And Crop Image ProcessingWeek 8: Uav And Ml Applications In AgricultureWeek 9: Hyperspectral Remote Sensing And Ml Applications In AgricultureWeek 10: Digital Soil Mapping – General OverviewWeek 11: Digital Soil Mapping With Continuous VariablesWeek 12: Digital Soil Mapping With Categorical Variables
