Image preprocessing techniques applied on NIR images for fruit bruise detection

Abstract

This study investigates the transformative potential of image preprocessing techniques when applied to near-infrared (NIR) images for early bruise detection. It emphasizes the nuanced selection of filters to retain essential image features while accentuating bruise characteristics. Filters as noise-reduction tools, rendering bruises more visible without erasing critical details. Subsequently, the limitations of conventional edge detection filters were examined such as Sobel, Prewitt, and Canny, which excel in outlining fruit edges but fall short in delineating bruises. Adaptive thresholding methods were introduced, exemplified by Otsu’s, showcasing their capacity to distinguish objects from backgrounds while acknowledging their challenge in preserving crucial edge pixels. Image enhancement techniques, such as Histogram Equalization, Contrast Stretching, and Sigmoid Correction, enhance fruit edge visibility and elevate bruise detection. In the frequency domain, filters such as Ideal Lowpass, Bandpass, and Highpass were harnessed to accentuate diverse bruise types. The Butterworth filter was introduced, capable of concurrently highlighting all relevant features, a pivotal innovation in comprehensive bruise detection. Through extensive experimentation and analysis of NIR images of various fruit varieties, including plums, peaches, and apples, our findings underscore the significance of tailored preprocessing techniques for optimal fruit bruise detection. These insights offer promise for agricultural industries and quality control processes seeking to enhance fruit quality assessment.