Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

Abstract

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose of adapting the system to an on-line grading machine. In particular, this work was focused on studying the feasibility of identifying organic stains on brown eggshells (dirty eggshell) caused by blood, feathers, feces, etc., from natural stains caused by deposits of pigments on the outer layer of clean eggshells. During the analysis, a total of 384 eggs were evaluated (clean, 148; dirty, 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the color of the natural stains (clear or dark). For each sample, digital images were acquired by employing a charged coupled device camera endowed with 15 monochromatic filters (440-940 nm). A Matlab® function was developed in order to automate the process and analyze images with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the image samples into the dirty or clean group on the basis of the geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify nearly 98% of the samples with a very low processing time (0.05 s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n=178), obtaining a similar percentage of correctly classified samples (97%).

A Machine Vision System for Detecting Fertile Eggs in the Incubation Industry

“…Otsu algorithm has been widely used in previous related work such as approaches in Refs. 10,12,13,17 . This algorithm exhaustively searches for the threshold that minimizes the intra-class variance defined as a weighted sum of variances of the two classes.…”

“…Some of the research is dedicated to the detection of cracks and dirt on eggs, 4,5,6,7,8,9,10,11,12,13 or measuring physical coordinates of eggs 5,11,14,15,16,17 . In a similar manner to the candling machines, most of these researches usually use light sources such as the incandescent lamp, fluorescent lamp or halogen lamp to back-illuminate the egg.…”

Evaluation of biological contaminants in foods by hyperspectral imaging: A review

“…and Taenia solium in pork and beef, is related to the incidence of many diseases. [187][188][189] Parasitic infection is a severe food safety challenge, and it is important for the food industry to rely on compatible preventive action and online detecting methods to avoid the presence of parasites. [64] For example, fish fillets are inspected by transillumination on candling tables and parasites are removed manually by trained personnel, [60] achieving to identify up to 70% of contaminated pieces.…”

Study on detection of crack on the quail egg shell using image processing and neural networks

“…Ngoài phương pháp thu nhận ảnh rọi là ánh sáng xuyên qua trứng, Loredana Lunadei và công sự [8] đã sử dụng ánh sáng phản xạ từ trứng để ghi nhận và phân tích. Độ chính xác đạt được của phương pháp này là 97% khi áp dụng trên trứng gà.Đối với trường hợp các vết nứt nhỏ, tác giả K.C.…”