Digital Farming 02 | Agricultural Sensing and Data Quality

A sensor reading becomes useful only when its meaning, uncertainty, location, and timing support the farming decision. This lecture develops a practical framework for trustworthy agricultural observations, from selecting the measurand to commissioning the complete data-to-action workflow. Explore in situ, proximal, and remote sensing; spatial and temporal sampling; resolution, bias, and field uncertainty; soil water content and matric potential; calibration and independent validation. Worked examples convert gravimetric water content, apply a two-point correction, interpret uncertainty around an irrigation threshold, and calculate the spatial effect of harvest transport delay. Learning objectives: • Design a representative sampling and calibration plan. • Distinguish raw observations, proxies, metadata, and estimates. • Diagnose unreliable readings using physical consistency and independent evidence. • Specify quality checks, maintenance, and acceptance tests before automation. Applications include soil profiles, weather stations, canopy temperature, conductivity, crop reflectance, disease-risk sensing, images, and yield maps. Later sections examine missing values, drift, filtering, provenance, and lifecycle costs. The closing exercise asks whether conflicting soil readings should open an irrigation valve. Basic familiarity with units and simple arithmetic is helpful; no coding is required. Chapters 00:00 Measurement purpose and sampling 02:05 Performance, units and time 03:49 Soil-water sensing and installation 06:29 Calibration, validation and uncertainty 08:11 Weather networks and consistency 10:35 Canopy, soil and crop sensing 13:24 Harvest and reference observations 15:12 Quality, provenance and missing data 18:53 Procurement, commissioning and diagnosis Lectures by Dr. Redmond R. Shamshiri, Assistant Professor of Robotics at the University of Southern Denmark (SDU). Contact: ramsha@mmmi.sdu.dk Narration uses an AI-generated voice. The educational content and examples follow the lecture slides; illustrative calculations and supplier-documented demonstrations are identified in the presentation. Adaptive AgroTech: https://www.adaptiveagrotech.com/ #DigitalFarming #AgriculturalSensors #DataQuality