RT 524: Digital Agriculture and Advanced Analytics Digital Farming Solution Advanced Statistical Learning Big Data in Ag

Course Content:

Lecture: Data driven farming concept, Types of Agricultural Data: Environmental, Weather, Soil, Crop, and Market Data. Methods for Collecting Agricultural Data: Sensors (proximal and remote), Drones/UAV, and IoT Devices. Data Logging and Management Systems. Data Accuracy and Consistency-Interoperability. Data cleaning and preprocessing, data Transformation. IoT based sensor network and data driven decisions. Data Management and Processing-digital image processing and feature identification. Foundations of advanced statistical learning algorithms-Artificial Neural Network, Perceptron and Neural Network architecture; Annotation and Self-supervised learning and case studies: weed detection, plant anomaly detection. Generative AI solutions for agriculture and predictive analytics; Introduction to digital twin-crop growth model and informatics. Managing Big Data in Agriculture, Data Warehousing Solutions for Farming Operations. Integrating Sensors for Real-Time Data Collection, Big data analytics and cloud computing. Introduction to Open Geospatial Consortium (OGC) web services, OGC Geospatial Sensor Web framework; Case studies on sensor web implementations in various environmental applications. Data fusion in agricultural information system, Geo-computational methods, Basics of spatio-temporal data, Spatio-temporal data mining, Space-time cube data for decision making in agricultural systems with case studies: real time irrigation and fertilizer application. High-performance plant phenotyping (infrastructure, analytics with AIML/DL).

Practical: Refreshing python programming and data visualization, coding and deployment platforms. IoT based sensors for agricultural data collection, Data logging. Image based data collection and post processing. Real time sensor data processing and decision support system. Advanced hands-on experience on building web services oriented systems for sensor web enablement and geospatial applications; Cloud computing practices and AI based application development for agricultural application cases: ‘Kisan e-Mitra’ an AI-powered chatbot, National Pest Surveillance System for tackling the loss of produce due to climate change, AI based analytics using field photographs for crop health assessment and crop health monitoring and app deployment. Mini projects and invited industry talk in related domain.