Quality classification of corn tortillas using computer vision

Computer vision is playing an increasingly important role in automated visual food inspection. However, quality control in tortilla production is still performed by human operators which may lead to misclassification due to their subjectivity and fatigue. In order to reduce the need for human operators and therefore misclassification, we developed a computer vision framework to automatically classify the quality of corn tortillas according to five hedonic sub-classes given by a sensorial panel. The proposed framework analyzed 750 corn tortillas obtained from 15 different Mexican commercial stores which were either small, medium or large in size. More than 2300 geometric and color features were extracted from 1500 images capturing both sides of the 750 tortillas. After implementing a feature selection algorithm, in which the most relevant features were selected for the classification of the five sub-classes, only 64 features were required to design a classifier based on support vector machines. Cross-validation yielded a performance of 95% in the classification of the five hedonic sub-classes. Additionally, using only 10 of the selected features and a simple statistical classifier, it was possible to determine the origin of the tortillas with a performance of 96%. We believe that the proposed framework opens up new possibilities in the field of automated visual inspection of tortillas.     

Predicting dairy cattle heat stress using machine learning techniques

The objectives of the study were to use a heat stress scoring system to evaluate the severity of heat stress on dairy cows using different heat abatement techniques. The scoring system ranged from 1 to 4, where 1 = no heat stress; 2 = mild heat stress; 3 = severe heat stress; and 4 = moribund. The accuracy of the scoring system was then predicted using 3 machine learning techniques: logistic regression, Gaussian naïve Bayes, and random forest. To predict the accuracy of the scoring system, these techniques used factors including temperature-humidity index, respiration rate, lying time, lying bouts, total steps, drooling, open-mouth breathing, panting, location in shade or sprinklers, somatic cell score, reticulorumen temperature, hygiene body condition score, milk yield, and milk fat and protein percent. Three different treatments, namely, portable shade structure, portable polyvinyl chloride pipe sprinkler system, or control with no heat abatement, were considered, where each treatment was replicated 3 times with 3 second-trimester lactating cows. Results indicate that random forest outperformed the other 2 methods, with respect to both accuracy and precision, in predicting the sprinkler group's score. Both logistic regression and random forest were consistent in predicting scores for control, shade, and combined groups. The mean probability of predicting non-heat-stressed cows was highest for cows in the sprinkler group. Finally, the logistic regression method worked best for predicting heat-stressed cows in control, shade, and combined. The insights gained from these results could aid dairy producers to detect heat stress before it becomes severe, which could decrease the negative effects of heat stress, such as milk loss.     

基于机器视觉与机器学习的火龙果重量估计

目的:解决火龙果人工称重耗时、费力且成本高昂等问题,提出一种基于机器视觉和机器学习的自动化重量估计方法。方法:首先,对106个火龙果进行称重、记录重量并拍摄、构建火龙果图像。然后,对火龙果进行降噪和分割,得到火龙果的二值图像,并从中提取出火龙果像素面积、长轴像素长度和短轴像素长度3项图像特征。将以上图像特征与重量组合成数据集,按照7∶3比例将数据集划分为训练集和测试集。最后,将训练集输入梯度提升、随机森林、K近邻和人工神经机器模型中训练,并利用测试集进行模型评估。结果:人工神经网络评价指标相较于其他模型更优,决定系数为0.986,均方根误差为13.091。结论:该方法能够有效地完成火龙果重量估计,满足火龙果重量估计的要求。     

基于机器视觉技术预测玉米质量等级的方法研究

国家标准规定玉米的质量定等指标为容重,为了实现利用机器视觉快速预测玉米等级,采用自行构建的基于机器视觉技术的玉米检测系统获取4种不同等级的玉米籽粒图像,通过均值滤波、最大类间方差法和形态学运算对玉米籽粒和背景进行处理、分割和特征参数的选取,并采用主成分分析法确定图像特征信息的最佳主成分因子数,建立以玉米容重为基础的8-21-4三层BP神经网络质量等级识别模型。结果表明:利用BP神经网络对基于完整籽粒图像和籽粒横切面图像的玉米等级的总体识别率均在90%以上,因此利用该模型对玉米等级的检测识别具有较高的可行性。     

基于机器视觉的苹果在线分级

通过CCD相机动态采集苹果两个面的实时图像,提出了泛洪填充+自适应Ostu阈值分割算法提取苹果的轮廓,采用最小外接圆法对苹果上表面图像进行处理得到苹果果径,采用最小外接矩形法对苹果侧表面图像进行处理提取苹果果形特征;将图像进行RGB到HSV空间转换,提取苹果的着色度、果锈,以及疤痕特征,采用基于改进粒子群算法的SVM决策树的分类方法进行苹果的分级。结果表明,该方法对特级果、一级果、二级果和等外果的识别准确率分别达96%,94%,98%,98%,分级速率达4个/s,可以满足苹果在线分级的要求。     

基于计算机视觉的牛肉分级技术研究进展

自从主要的牛肉生产国相继颁布牛肉分级系统以来,计算机视觉牛肉分级技术一直就是牛肉分级领域中的研究重点.本文概述了目前世界上主要的牛肉分级体系,着重论述了国内外计算机视觉牛肉分级技术的发展情况,提出了现行的计算机视觉牛肉分级技术面临的主要问题及其发展方向.     

基于计算机视觉的织物疵点检测技术研究进展

对基于计算机视觉的织物疵点检测技术进行回顾,介绍了灰度共生矩阵法,局部二值模式算法,邻域关联分析,自组织映射,支持向量机,学习向量量化分类器,多分类器组合和决策融合等算法等在图像预处理,特征提取、分类和识别等方面的应用情况,着重讨论了一种基于多数投票原则的多分类器决策融合技术,试验结果证实该技术有较高精确性.     

基于机器视觉的草莓自动分级方法研究

针对传统草莓自动分级处理系统利用单一特征衡量草莓品质的不足,提出从成熟度、质量、形状三方面综合评估的方法,以快速有效地完成草莓自动分级处理。该方法先计算分析HSV颜色模型中H参数判断草莓成熟度,利用图像投影面积-质量函数关系对成熟度达标的草莓进行质量测算,再采用K均值聚类法与判别分析相结合对质量达标的草莓进行形状分类,并利用加权法计算草莓质量与形状评级分确定草莓品质等级。试验表明,该方法与人工评级相比准确率达到90%以上。     

Evaluation of pork color by using computer vision

The objective of this study was to determine the potential of computer vision technology for evaluating fresh pork loin color. Software was developed to segment pork loin images into background, muscle and fat. Color image features were then extracted from segmented images. Features used in this study included mean and standard deviation of red, green, and blue bands of the segmented muscle area. Sensory scores were obtained for the color characteristics of the lean meat from a trained panel using a 5-point color scale. The scores were based on visual perception and ranged from 1 to 5. Both statistical and neural network models were employed to predict the color scores by using the image features as inputs. The statistical model used partial least squares technique to derive latent variables. The latent variables were subsequently used in a multiple linear regression. The neural network used a back-propagation learning algorithm. Correlation coefficients between predicted and original sensory scores were 0.75 and 0.52 for neural network and statistical models, respectively. Prediction error was the difference between average sensory score and the predicted color score. An error of 0.6 or lower was considered negligible from a practical viewpoint. For 93.2% of the 44 pork loin samples, prediction error was lower than 0.6 in neural network modeling. In addition, 84.1% of the samples gave an error lower than 0.6 in the statistical predictions. Results of this study showed that an image processing system in conjunction with a neural network is an effective tool for evaluating fresh pork color.     

Measurement of meat color using a computer vision system

The limits of the colorimeter and a technique of image analysis in evaluating the color of beef, pork, and chicken were investigated. The Minolta CR-400 colorimeter and a computer vision system (CVS) were employed to measure colorimetric characteristics. To evaluate the chromatic fidelity of the image of the sample displayed on the monitor, a similarity test was carried out using a trained panel. The panelists found the digital images of the samples visualized on the monitor very similar to the actual ones (P < 0.001). During the first similarity test the panelists observed at the same time both the actual meat sample and the sample image on the monitor in order to evaluate the similarity between them (test A). Moreover, the panelists were asked to evaluate the similarity between two colors, both generated by the software Adobe Photoshop CS3 one using the L*, a* and b* values read by the colorimeter and the other obtained using the CVS (test B); which of the two colors was more similar to the sample visualized on the monitor was also assessed (test C). The panelists found the digital images very similar to the actual samples (P < 0.001). As to the similarity (test B) between the CVS- and colorimeter-based colors the panelists found significant differences between them (P < 0.001). Test C showed that the color of the sample on the monitor was more similar to the CVS generated color than to the colorimeter generated color. The differences between the values of the L*, a*, b*, hue angle and chroma obtained with the CVS and the colorimeter were statistically significant (P < 0.05–0.001). These results showed that the colorimeter did not generate coordinates corresponding to the true color of meat. Instead, the CVS method seemed to give valid measurements that reproduced a color very similar to the real one.     

机器视觉技术在果蔬的识别和分级中的应用

机器视觉技术在农产品品质检测方面的研究和应用发展迅速 ,为了能充分了解国内外在果蔬方面的研究状况 ,本文综述了机器视觉技术在果品和蔬菜的识别和分级中的研究进展 ,以供参考     

An expert egg grading system based on machine vision and artificial intelligence techniques

The main purpose of this research was design and development of an intelligent system based on combined fuzzy logic and machine vision techniques for grading of egg using parameters such as defects and size of eggs. The detected defects were internal blood spots, cracks and breakages of eggshell. The Hue-Saturation-Value (HSV) color space was found useful in obtaining visual features during Image Processing (IP) stage. The fuzzy inference system (FIS) was designed based on triangular and trapezoidal membership functions, fuzzy rules with logical operator of AND inference system of Mamdani and method of center average for defuzzifier. The evaluation results of IP algorithms showed that use of IP technique has good performance for defects and size detection. The Correct Classification rate (CCR) was 95% for size detection, 94.5% for crack detection and 98% for breakage detection. The overall accuracy FIS model in grading of the eggs was 95.4.     

Applications of computer vision techniques in the agriculture and food industry: a review

Over the last decades, parallel to technological development, there has been a great increase in the use of visual inspection systems. These systems have been widely implemented, particularly in the stage of inspection of product quality, as a means of replacing manual inspection conducted by humans. Much research has been published proposing the use of such tools in the processes of sorting and classification of food products. This paper presents a review of the main publications in the last ten years with respect to new technologies and to the wide application of systems of visual inspection in the sectors of precision farming and in the food industry.     

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning

Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observation of cow status because they physically occlude each other. In this study, we propose a method that can monitor the respiratory behavior of multiple cows. Initially, 4,000 manually labeled images were used to fine-tune the YOLACT (You Only Look At CoefficienTs) model for recognition and segmentation of multiple cows. Respiratory behavior in the resting state could better reflect their health status. Then, the specific resting states (lying resting, standing resting) of different cows were identified by fusing the convolutional neural network and bidirectional long and short-term memory algorithms. Finally, the corresponding detection algorithms (lying and standing resting) were used for respiratory behavior monitoring. The test results of 60 videos containing different interference factors indicated that the accuracy of respiratory behavior monitoring of multiple cows in 54 videos was >90.00%, and that of 4 videos was 100.00%. The average accuracy of the proposed method was 93.56%, and the mean absolute error and root mean square error were 3.42 and 3.74, respectively. Furthermore, the effectiveness of the method was analyzed for simultaneous monitoring of respiratory behavior of multiple cows under movement, occlusion disturbance, and behavioral changes. It was feasible to monitor the respiratory behavior of multiple cows based on the proposed algorithm. This study could provide an a priori technical basis for respiratory behavior monitoring and automatic diagnosis of respiratory-related diseases of multiple dairy cows based on biomedical engineering technology. In addition, it may stimulate researchers to develop robots with health-sensing functions that are oriented toward precision livestock farming.     

Data-driven recipe completion using machine learning methods

BackgroundCompleting recipes is a non-trivial task, as the success of ingredient combinations depends on a multitude of factors such as taste, smell and texture.Scope and approachIn this article, we illustrate that machine learning methods can be applied for this purpose. Non-negative matrix factorization and two-step regularized least squares are presented as two alternative methods and their ability to build models to complete recipes is evaluated. The former method exploits information captured in existing recipes to complete a recipe, while the latter one is able to also incorporate information on flavor profiles of ingredients. The performance of the resulting models is evaluated on real-life data.Key findings and conclusionsThe two machine learning methods can be used to build models to complete a recipe. Both models are able to retrieve an eliminated ingredient from a recipe and the two-step RLS model is also capable of completing an ingredient set to create a complete recipe. By applying machine learning methods on existing recipes, it is not necessary to model the complexity of good ingredient combinations to be able to complete a recipe.     

The application of a fast algorithm for the classification of olives by machine vision

Processed olives have traditionally been classified by human experts but the process is slow, expensive and without a good repeatability. The main objective of this paper is to design a system to sort olives automatically using machine vision. First of all, human experts classified different batches of olives in four categories to extract the parameters related with each class. After characterising the processed olives, an algorithm to sort automatically olives was implemented and tested. Different resolutions were considered to study the effect of resolution in the classification results. Finally, a comparison between human and machine results is done.     

Development of a machine vision system: real-time fabric defect detection and classification with neural networks

In this study, a machine vision system is developed to achieve fabric inspection and defect classification processes automatically. The system consists of an image acquisition hardware and an image processing software. A simple and portable system was designed so that it can be adapted easily to all types of the fabric inspection machines. The software of the system consists of defect detection and classification algorithms. The defect detection algorithm is based on wavelet transform, double thresholding binarization, and morphological operations. It was applied real time via a user interface prepared by using MATLAB^® program. The defect classification approach is based on gray level co-occurrence matrix and feed forward neural network. Five commonly occurring defect types, warp lacking, weft lacking, soiled yarn hole, and yarn flow, were detected and classified. The defective and defect-free regions of the fabric were detected with an accuracy of 93.4% and the defects are classified with 96.3% accuracy rate.