Chapter 34

Precision Viticulture

Digital Image Processings

In precision viticulture, where most remote sensing data is recorded in digital format, virtually all image interpretation and analysis involve some element of digital processing. Digital image processing may involve numerous procedures, including data formatting and correction, digital enhancement to improve visual interpretation, and automated classification of targets and features entirely by computer. To process remote sensing imagery digitally, the data must be recorded and stored in a digital format suitable for computer tape or disk. Obviously, the other requirement for digital image processing is a computer system, sometimes referred to as an image analysis system, with the appropriate hardware and software to process the data. Several commercially available software systems have been developed specifically for remote sensing image processing and analysis.

Digital Image Processing Methods

For discussion purposes, most of the common image processing functions available in image analysis systems can be categorized into the following seven categories:

  • Preprocessing
  • Image Enhancement
  • Image Transformation
  • Image Segmentation
  • Image Classification
  • Feature Extraction
  • Interpretation & Application

Applications of Digital Image Processing in Precision Viticulture

Digital image processing in remote sensing for viticulture uses techniques such as object-based image analysis, hyperspectral analysis, and machine learning (AI) on UAV- and satellite-derived data to monitor vine health, predict yield, assess quality, map variability, and detect diseases, enabling precision management for optimized resource use and terroir understanding. Applications include zoning vineyards for targeted treatments, tracking water stress, and differentiating cultural practices, all by analyzing spectral signatures and canopy structure to inform management decisions.

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