Preprocessing and Enhancement Techniques for Nutritional Feature Extraction

Digital imaging utilities, such as RGB, multispectral imaging (MSI), and HSI, have been found to be nondestructive nutritional assessment tools in the food systems and agriculture industries. But field-acquired images are usually marred by variations in illumination, noise of the sensors, geometric distortions, as well as interferences of the surrounding environment that affect the accuracy of analysis. This chapter reviews advanced preprocessing and enhancement methods that were meant to accommodate these issues and consequently lead to the feasible extraction of the biochemical and biophysical characteristics. Some of the most important topics are radiometric and geometric corrections, noise reduction in space and frequency domains, contrast enhancement with techniques like contrast limited AHE (CLAHE), and color normalization to ensure consistent spectral representation. Hyperspectral band alignment, image registration, and dimensionality reduction strategies (e.g., principal component analysis [PCA], independent component analysis [ICA], and t-distributed stochastic neighbor embedding [t-SNE]) are also addressed, along with the automated design of processing pipelines and feature extraction frameworks for high-throughput applications, which facilitate scalable, real-time, and autonomous monitoring in nutritional contexts. However, guaranteeing their efficiency and interoperability in the future will require standardized calibration protocols, using solid edge-AI with the ability to combine data from multiple sources.