Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

Food quality and safety have become the top priorities for agriculture and food processing industry due to the increasing consumer demand for high-quality healthy food. The food processing industry is currently focusing on using fast, precise, and nondestructive automated quality inspection techniques. Near-infrared spectroscopy, image processing, hyperspectral imaging, X-rays, and ultrasonic techniques have been researched and shown to have high potential for automated inspection. The biggest challenge in the automated inspection systems deals with signal preprocessing, denoising, feature extraction, and its re-synthesis for classification purposes. Several research studies have established that the technique of wavelet analysis can very well resolve these issues of signal processing in many systems used for quality inspection of agricultural and food products. The objective of this paper is to discuss the theory of wavelet analysis and review its application in signal processing and feature extraction for quality monitoring of agricultural and food products.