Non-Destructive Quality Inspection of Potato Tubers Using Automated Vision System

Abstract

In this investigation, an automated vision system "AVS" for non-destructive quality inspection of potato tubers "PT" was developed. Color, size, mass, firmness, and the texture homogeneity of the "PT" surface, various sensitive features were studied, and extracted from the digital image by using the R program. Otsu threshold method, RGB, Lu*v*, CIE LCh uv color models, and texture analysis by using the package Gray-Level Co-Occurrence Matrices (GLCMs) were applied. The results showed a great correlation between the tuber pixel area percentages (DIM=dimension as a percentage of total pixels), and both mass and geometric mean diameter (GMD) of all "PT" varieties. The color results demonstrated that the hue angle (h uv ) ranged from 68.92 to 96.61 °, and the "PT" color was classified into deep and light color intensity. The "AVS" could predict the mass and size, and gave statistical data at the mass production level, in terms of the inspecting samples No., mass, and grades based on size, color, and free from injuries through the texture homogeneity of tuber surface. A predictive model hypothesized based on the tuber's surface texture characteristics for predicting the tubers firmness was statistically significant. This "AVS" can be applied as a non-destructive, precise, and symmetric technique in-line inspection, the quality of "PT", also helping decision-makers in the agricultural field and stakeholders to improve the horticulture sector through the statistical data issued by this system.

Thoughts for Foods: Imaging Technology Opportunities for Monitoring and Measuring Food Quality

“…In the case of RGB images, there are three bands (Red, Green, and Blue). In these cases, you canapplyatypeofanalysisthatrelies on grayscale arrays (GLMC) [11][12][13][14]. The images are first rendered grayscale and the GLCM algorithm observes the relationship between each pixel and its neighbors Figure 3.…”

“…Contour detector [24][25][26] refer to Otsu's algorithm [27], in an intention to discriminate bodies, backgrounds, particles and are currently used for image analysis in many sectors, including agro-industrial. An example of applying the identification of the contour and then the shape is shown in Figure 4, [13], which shows the reading of an image of a potato tuber.…”

“…Likewise, [36] explained that the positron-emitting tracer imaging system (PETIS) was developed to use the theory of PET in plants. It is equipped with a planar-type imaging apparatus and radioisotopes tracers such as 11 C, 13 N, 15 O, 52 Fe, 52 Mn, 64 Cu, and 107 Cd that are produced by a cyclotron and provides 2-D images. As well, [37] concluded that the 2-D and 3-D Gamma-ray imaging techniques have been successfully used in agriculture for the quantification and visualization of various compounds and mechanisms studies within plants such as water uptake and transportation, metal uptake, and transportation, photoassimilate translocation, etc.…”

“…Subsequently, many successful attempts were recorded to use automatic vision systems based on image processing for quality control in post-harvest stages. In this context, [13] inspected potato tubers according to some sensitive quality features such as color, size, mass, firmness, and the texture homogeneity of potato surface Figure 20 through a developed automated vision system. Concluded that the vision system can be applied as a non-destructive, precise, and symmetric technique inline inspection.…”

Effects of Oversampling Smote and Spectral Transformations in the Classification of Mango Cultivars Using Near-Infrared Spectroscopy

“…NIR has a wavelength of 750-2500 nm, the target sample is illuminated with light, and the reflected light or backscatter is measured with a spectrometer. Horticultural products can also use this NIR method in grading, sorting, internal quality, and determining harvest time [5]. Determination of the quality of horticultural products can be done non-destructively by using NIR spectroscopy that has been applied to watermelon [6] and melon [7], using spectral for detection Lycopene Content in Tomato [8], detection of mango quality [9], [10].…”