Deep Learning Model Based on ResNet-50 for Beef Quality Classification

Food quality measurement is one of the most essential topics in agriculture and industrial fields. To classify healthy food using computer visual inspection, a new architecture was proposed to classify beef images to specify the rancid and healthy ones. In traditional measurements, the specialists are not able to classify such images, due to the huge number of beef images required to build a deep learning model. In the present study, different images of beef including healthy and rancid cases were collected according to the analysis done by the Laboratory of Food Technology, Faculty of Agriculture, Kafrelsheikh University in January of 2020. The texture analysis of the beef surface of the enrolled images makes it difficult to distinguish between the rancid and healthy images. Moreover, a deep learning approach based on ResNet-50 was presented as a promising classifier to grade and classify the beef images. In this work, a limited number of images were used to present the research problem of image resource limitation; eight healthy images and ten rancid beef images. This number of images is not sufficient to be retrained using deep learning approaches. Thus, Generative Adversarial Network (GAN) was proposed to augment the enrolled images to produce one hundred eighty images. The results obtained based on ResNet-50 classification achieve accuracy of 96.03%, 91.67%, and 88.89% in the training, testing, and validation phases, respectively. Furthermore, a comparison of the current model (ResNet-50) with the classical and deep learning architecture is made to demonstrate the efficiency of ResNet-50, in image classification

Appropriate and Optimal Classifier for Beef Quality Discrimination by A Low-Cost Optical Apparatus

In this paper, we present an optimal classifier for beef quality discrimination by a low-cost optical apparatus. Detecting beef spoilage in beef factories is a sophisticated process because beef spoilage is a mixture of physical and chemical changes. A low-cost Light-Dependent Resistor (LDR), and a light source were used to collect reflection spectra during the analysis of beef. The LabVIEW platform was programmed to acquire the obtained data from the microcontroller (Arduino) to predict beef quality. For the beef quality discrimination process, un-supervising machine learning called Principal Components Analysis (PCA) was used, and the score plot percentage was of the first (F1) and second (F2) dimensions of the most variation for forty samples were of 93.98% and 3.38% respectively. Supervised Machine Learning (SML) (Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA)) were used also to compare with other models of un-supervised machine learning. Optimum classifier was achieved by the classification algorithm of SVM that can represent 95.75% of the whole data

Optimizing the In-Vessel Composting Process of Sugarbeet Dry-Cleaning Residue

Rapid urbanization and industrialization around the world have created massive amounts of organic residues, which have been prioritized for conversion into valuable resources through the composting process to keep their harmful effect at a minimum. This research aimed to assess the influence of active and passive aeration on composting mass of sugar beet residues in the case of using additives (e.g., charcoal only or manure only or combination). Some physicochemical properties of composting mass were analyzed on certain days of composting. Some parameters including temperature–time profile, carbon to nitrogen ratio (C/N ratio), moisture content, electrical conductivity, pH, germination and microbial population enumeration of compost were measured. Cress germination test was conducted for each medium of germination which contains a mixture of soil and compost (at a ratio of 3:1) taken from each treatment. The results showed that temperature–time profile data of composting mass showed an irregularity. Forcedly aerated composting mass did not demonstrate a thermophilic phase while passively aerated ones did not show a mesophilic phase. Carbon to nitrogen (C/N) ratio reduction was greater in most forcedly aerated composting mass than passively aerated on days from 1 to 33 of composting period. The results further showed that electrical conductivity decreased at the end of the composting period where it ranged from 2.55 to 3.1 dS/m. Germination medium containing forcedly aerated compost treated with a combination of charcoal and manure achieved the highest germination index which was higher than the control sample by 58.63% followed by forcedly aerated composting mass treated by charcoal only which exceeded the control sample by 5.35%. Strong correlation coefficient (r > 0.80) for the relationship between germination index and number of bacteria was obtained on day 17th of composting period