pototo surface defect detection

African Journal of Agricultural Research, 2012

Potato is cultivated as a major food resource in some countries that have moderate climate. Potato is sensitive to many diseases. Sorting is necessary for decreasing the transfer rate of diseases and preparing favourite conditions. Grading with workers has disadvantages such as: instability, time needed and its expensive, to solve these problems, use of machine vision system is necessary. 90 Agria potatoes were prepared. The potatoes were graded to 6 classes with 15 samples that they were: healthy, cracked, rhizoctonia fouled, cutting, rotting and greening. The samples were placed in lighting chamber and images of them were captured by means of a CCD camera. The images were transferred to a personal computer by a frame grabber. These images were analyzed by MATLAB software. For different defect sorting had been used a compound of colour and physical properties of defects. Sorting accuracy was 97.67%.

International Journal on Advanced Science, Engineering and Information Technology, 2020

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.

D Ravindra Babu, 2024

The characteristics of crack, rotten, sprout, skin peel and good potatoes non -destructively with gray level co-occurrence matrix properties (GLCMP), radon, gabor, local binary patterns (LBP) and histogram of oriented gradients (HOG) with default parameters and values i.e. adapted method were compared with improved method. Gabor feature length (16) of improved method was lower compared adapted method and improved method requires less time to plot gabor magnitude and spatial kernels for all potato classes. Radon feature row vector size is same for both adapted and improved methods for all potato classes but differ in column size. At theta value of 90 degrees (improved method), the time taken to plot radon transforms is lower compared to adapted method (using theta value 180 degree). Gray level co-occuerrence matrix properties (GLCMP) such as contrast, correlation, energy and homogeneity values were compared to both adapted and improved methods for all potato classes. Contrast values found lower in adapted method for all potato classes compared to an improved method. But remaining three properties found highest in adapted method for all potato classes compared to improved method. The default values used in adapted method of HOG feature vector length (26140) is higher compared to improved method (1330) for all types of potato images. For crack and rotten potato images, an improved method required higher time to plot visualization than adapted method, while for sprout, good and skin peel images, adapted method has more visualization time. The LBP feature length in improved method was found higher (185) compared to adapted method (59) for all potato classes. The mean time to plot squared errors in adapted and improved methods for crack images were found to be 0.6378 s and 0.6305 s respectively, for rotten images 0.2098 s and 0,2622 s, for sprout images 0.1911 s and 0.2209 s, for skin peel images 0.2197 s and 0.2197 s, for good images 0.2672 and 0.2565 s.

This paper reports on a current project to develop a prototype system for the automatic identification and quantification of potato defects based on machine vision. The system developed uses off-the-shelf hardware, including a low-cost vision sensor and a standard desktop computer with a graphics processing unit (GPU), together with software algorithms to enable detection, identification and quantification of common defects affecting potatoes at near-real-time frame rates. The system uses state-of-the-art image processing and machine learning techniques to automatically learn the appearance of different defect types. It also incorporates an intuitive graphical user interface (GUI) to enable easy setup of the system by quality control (QC) staff working in the industry.

Applied Mathematical Sciences, 2015

The aim of this work was the implementation of an identification and evaluation methodology for external potato damage detection. A system for automatic image recognition was used and a methodology validation, by comparison with human visual selection, was performed. The potato surface image was acquired with a monochromatic video camera that operated in the visible spectrum and in the near infrared. This device was connected to a frame grabber card, interfaced to a PC, for image acquisition and elaboration. The software for the image elaboration has enabled the definition of algorithms for the automatic recognition and measurement of the damaged area. The obtained data were compared with the human visual evaluation demonstrating an adequate level of reliability of the applied methodology.

2015

Vegetable quality is frequently referred to size, shape, mass, firmness, color and bruises from which fruits can be classified and sorted. However, technological by small and middle producers implementation to assess this quality is unfeasible, due to high costs of software, equipment as well as operational costs. Based on these considerations, the proposal of this research is to evaluate a new open software that enables the classification system by recognizing fruit shape, volume, color and possibly bruises at a unique glance. The software named ImageJ, compatible with Windows, Linux and MAC/OS, is quite popular in medical research and practices, and offers algorithms to obtain the above mentioned parameters. The software allows calculation of volume, area, averages, border detection, image improvement and morphological operations in a variety of image archive formats as well as extensions by means of plugins written in Java.A qualidade vegetal freqüentemente se refere a tamanho, f...

Journal of Agricultural Science, 2012

Machine vision system is a modern technique that is used for grading of wide range of agricultural crops. Objective of this research is qualitative sorting of potatoes by means of lighting chamber, Camera, frame grabber and computer for catching proper images and analysis of them by MATLAB software. 110 numbers of Agria potatoes were selected randomly and placed in same lighting conditions. The images were transferred by frame grabber to computer memory to be analyzed. The samples had been pre-graded in the same face witch were placed in lighting chamber and percentage of health class was recorded. By performing pre-processing techniques on images, the compound of HSV color space and logarithmic transformation by coefficient of 0.5 was selected. The correction coefficient of health class of pre-graded method and results of implementing algorithm was 0.989 that it was the highest. Qualitative sorting accuracy in this method was 96.54%.

Acta Horticulturae, 2003

After-cooking darkening (ACD) phenomenon occurs when boiled or steamed potato tubers become grayish dark on exposure to air. The degree of darkening is controlled genetically and is strongly influenced by environmental factors. The pigment responsible for ACD is a complex of chlorogenic acid and iron, which is formed during cooking and oxidizes during cooling to a colored ferric di-chlorogenic acid complex. The evaluation of ACD is thus based either on the amount of chlorogenic acid in tubers (destructive method) or on the determination of the degree of color intensity on cut tuber surfaces (non-destructive method). The destructive method is HPLC based, complicated, and time consuming, particularly when a large number of samples is being evaluated. Thus, despite its deficiency, non-destructive visual examination is routinely utilized in many breeding programs. The objective of this study was to determine whether digital imaging analysis can be adapted to the evaluation of ACD. The method is based on the direct capture of the cooked and cut tuber surface image using a cooled CCD camera attached to a digital imaging system. The degree of the dark color is then measured by pixels using an imaging acquisition software. The system is calibrated at 0-255 pixel levels (0 as black, 255 as white) as standard, therefore the read values directly reflect the degree of darkening. The measurement procedure is fast, reliable, simple, and particularly applicable for handling a large number of samples. This is the first report on evaluation of ACD using a digital imaging system.

Article , 2023

This work aims to inspect tomato features and classify them based on color and morphological features into the three predefined regions using artificial neural networks (ANN). Different learning methods were analyzed for the task of inspecting tomatoes using image processing software in MATLAB. Tomatoes were collected from the eastern parts of Ethiopia. The neural classification was done by the shape and size feature alone. The ANN classifier on the selected color feature alone showed that from the total test examples of 180 images, 168 (93.3 %) were correctly classified and 12 (6.7 %) were misclassified. The ANN classifier on all features taken together showed that all the test images were correctly classified. This result is similar to the morphology (shape and size) features result, but if the number of data points is high, the result may vary significantly. The overall result revealed that shape and size features have more discriminating power than color features, and the discrimination power increases when individual features are trained together with shape and size features. This may be because the discriminating factor increases due to the increase in the number of included features. It was observed that the proposed method was successful as quantified by the cumulative error (CE) and percentage error (%E) of training, testing, and validation of color features: 6.35 %, 3.70 %, and 11.11 %, respectively, in evaluating the quality of tomatoes.