1. Using TensorFlow deep learning neural network to classify beef cuts.

Modern consumers are becoming more interested about the production story for the foods they select and put on their dinner plate. Information regarding the source of the food, nutrition, and product quality has become an important purchase decision factor as household incomes increase. For example, beef quality has been an important factor evidenced by consumers’ willingness to pay a premium for tender steaks (Lusk et al., 2001; Kukowski et al., 2005). Other studies have shown that nutrient and meat quality profiles are related to muscle fiber composition and the proximate composition of meat cuts (Jung et al., 2015; Jung et al., 2016).

Efforts have been made to profile individual beef muscles’ nutrition and palatability characteristics (Jeremiah et al., 2003). Providing profile characteristics for different meat cuts allows the consumer to make a more informed purchase decision. Seggern and Gwartney (2005) identified muscles in the chuck and round that had the potential to be a value-added cut. Several new beef cuts were identified, including the flat iron, chuck eye steak, and Denver cut. The success of these innovative retail sub-primal cuts resulted in increased revenue for the beef industry by adding value to previously underutilized cuts of meat that often ended up as trim for ground beef. Cuts such as the flat iron steak became more and more popular, and it became increasingly apparent that consumers were not familiar with the new names and had difficulty identifying them in the retail case. Consumers can educate themselves regarding the different beef cuts by using charts produced by the U.S. Cattlemen’s Beef Board and National Cattlemen’s Beef Association that are available online or at the point of purchase. However, modern consumers use information from multiple sources regarding nutrition information for their healthy cooking methods and to identify the correct cuts that match the nutrition and palatability expectations. Obtaining accurate information is often time-consuming, and consumers are often directed to wrong information due to the lack of available information on beef cuts. Therefore, a fast, accurate objective technology is needed to recognize beef cuts information, so the consumers can obtain useful nutrition information for their health. In addition, the meatpacking industry can use this kind of novel technology to put correct cuts/nutrition information on the meat package.

Artificial intelligence (AI) has been used to recognize different targets such as text/words, expression of disease, food identification, and identity authentication system (Curtis, 1987; Anwar and Ahmad, 2016; Bai, 2017; Buss, 2018; Liu et al., 2018; Sun et al., 2018; Jia et al., 2019; Islam et al., 2019). Known for being efficient, accurate, consistent, and cost-effective, AI suits the meat industry’s rapid mass production (Liu et al., 2017). Recent studies showed that AI technology has great potential to detect marbling in beef and pork (Chmiel et al., 2012; Liu et al., 2018), fresh color of pork (Sun et al., 2018), tenderness of beef (Sun et al., 2012), and grading of beef fat color (Chen et al., 2010). Moreover, Schmidhuber (2014) provided an overview of many deep learning neural networks used for pattern recognition relevant for several domains such as facial recognition (Russakovsky et al., 2014), disaster recognition (Liu and Wu, 2016), and voice recognition (Wu et al., 2018; You et al., 2018). Moreover, deep learning has been applied to sheep breeding classification (Abu et al., 2019), food classification (Hnoohom and Yuenyong, 2018), bacon classification (Xiao et al., 2019), classification of species in meat (Al-Sarayreh et al., 2020), and the farming and food industries. The CNN is a popular deep learning tool, which has been used widely in classification problems. The most significant advantage of CNN is its automatic learning ability from an input image without feature extraction (Hinton et al., 2006), but CNN requires a larger image data set to train the model from the scratch (Krizhevsky et al., 2017). To overcome this situation, the transfer learning technique has been used in which a pretrained model is used in a new problem. Both VGG16 and Inception Res Network are the two popular CNN architecture models, which use the transfer learning approach to solve the image classification problems.

The ImageNet competition winner VGG16 model is also a CNN proposed by K. Simonyan and A. Zisserman from the University of Oxford in the article “Very Deep Convolutional Networks for Large Scale Image Recognition.” It makes an improvement over AlexNet by replacing 5 × 5 kernel size by 3 × 3 kernel-sized filters one after one. Practically, a stack of 5 × 5 kernel is related to two 3 × 3 kernels, and a 7 × 7 kernel is equivalent to three 3 × 3 kernels. In short, VGG16’s nonlinear transformation increases the ability of a CNN to learn features in a better way. In the convolutional structure of VGGNet, a (1 × 1) convolutional kernel is used, and without affecting the input and output dimensions, nonlinear transformation is introduced to increase the efficiency of a network and reduce calculations. During the training process, training is performed at the low-level layers, and then the weights of ImageNet are used to initialize the complex models that follow in order to speed up the convergence of training.

Another CNN-based model is the inception network, which is widely used in the field of artificial intelligence (Deng et al., 2010; Too et al., 2019). CNN classifiers perform better with a deep layer but face issues with overfitting, smaller kernel, and vanishing gradient. Inception networks reduce these problems with multiple filter sizes in the same level that is addressed as “wider,” rather than “deeper” in the case of neural network architecture. It performs convolution of an input with multiple different size filters such as 1 × 1, 3 × 3, and 5 × 5 (Szegedy et al., 2017). Inception Resnet V2 modifies the Inception network and reduces the computational cost with hyperparameter tuning of three major blocks.

To avoid the overfitting problem in limited image data sets, image augmentation technique could be applied to the image data set before feeding the image into the Inception Network and VGG16 architecture (Olsen et al., 2019).The image augmentation technique is widely used to expand or enlarge existing image data sets artificially using different processing methods. To build a robust image classifier using very little training data, image augmentation is required to boost the performance of neural networks (Ahsan et al., 2019). Widely used augmentations are random rotation, shifts, shear, flips, zooming, filtering etc. Random rotation replicates the original image by rotation in between 0 and 360°. Horizontal and vertical shift move the pixels of original images by 2-dimensional direction. Flipping indicates reversing image data by row and columns. Zooming randomly zooms different parts of the input image. Different types of filtering help generate images from low light to brighter, low contrast to high contrast, and various saturation levels (Shijie et al., 2017). We can also use different types of domain-specific functional processing and create augmented images to better perform image classifiers. Image augmentation is highly recommended for object detection, but the right processing choice is important for a robust model with a limited number of training input. An inappropriate selection of an augmentation technique can have a detrimental effect on the classifier. Multiple image augmentation techniques are preinstalled in the TensorFlow library (TensorFlow, nd) and there are functionalities to add user-defined techniques (Sokolova and Lapalme, 2009).

In 2016, the Google^TM Brain team released a new deep learning neural network open-source software package called TensorFlow (Abadi et al., 2016). This free, open-source deep learning algorithm library provides an effective, fast, and accurate source of artificial intelligence for industry applications (Zhang and Kagen, 2017; Han et al., 2018; Chen and Gong, 2019; Qin et al., 2019; Suen et al., 2019; Vázquez-Canteli et al., 2019). Furthermore, the TensorFlow backend model could be deployed to the mobile application and IoT devices using TensorFlow Lite (tflite) (Google Inc., nd), which is lean and fast to get real-time results. Some literature has used the tflite model file in a mobile application in their research (Tarale and Desai 2020; Pandya et al., 2020). The CNN deep learning technologies using TesnorFlow has not yet been applied for the classification of meat cuts.

The objective of this study was to develop a beef cut classification system based on off-the-shelf TensorFlow deep learning neural network coupled with the image augmentation technique, to measure prediction performance with images acquired in varying lighting and background conditions and processing levels, and to provide fast, accurate beef cuts information to the consumers and meat industry.

In this research, the selected VGG16 model with data augmentation technique combination was able to achieve the highest accuracy of 98.57% on 116 test images. However, the training accuracy reached 100% during experiments using 887 training and 110 validation images. The training process was performed on 50 epochs, and the loss factor was optimized by the Adam optimizer effectively from the first epoch. Since we set steps per epochs as 32, the training process helped to reach the global minima after 10 epochs. Based on the decrement of categorical cross-entropy the prediction from the softmax function was aligned with an actual class label. Figure 6 shows that the VGG16 training loss reached the lowest point during epoch 47 and the validation loss was lowest during epoch 45. Though the accuracy is the most intuitive performance measurement to observe the model’s prediction ratio, the precision score is better to indicate the model’s robustness (Denton et al., 2016; Ahsan et al., 2018; Gomes et al., 2018). High precision indicates the model has low false-positive rates. From Figure 7, we can observe that the false-positive rates of the VGG16 are very low on average. Table 3 shows that sensitivity (recall) is relatively higher than the standard 50%. Also, the F1 score consistently follows the accuracy and precision and was always more than 94%.

The Inception ResNet V2 was also implemented with the same parameter setup as the VGG16 on a similar data set. The early epochs showed promising results, but on average, the model did not perform better than VGG16. The accuracy and loss graph of Inception ResNet V2 (Figure 7) showed inconsistency at different stages during the training process. Also, the false-positive rates and F1 score indicate that it failed to accurately predict all the Flank steak, New York strip, and tenderloin classes’ images. The primary assumption is that since the kernel size of Inception ResNet V2 is larger than that of VGG16, it failed to localize some inputs in detail information. The highest accuracy of Inception ResNet V2 on the testing data set was documented as 95.71% (Figure 8), significantly or slightly lower than VGG16 accuracy (98.57%) for the same data set. The detailed comparison of both models’ key performance indicators (KPI) can be observed in Table 3.

The TensorFlow deep learning neural network showed a great potential in recognizing and classifying beef cuts with reasonably good accuracy. Accuracy is the most intuitive performance measure and a simple way to observe the prediction ratio but often misleading when the data set has not symmetric false-negatives and false-positives (Powers, 2011). To further investigate our VGG16 model, we measured the ratio of correctly predicted positive observations as the precision score. High precision indicates low false-positive rates which is observed on the accuracy metrics graph (Figure 9). The graphs also showed that the recall (sensitivity) is always above the standard value of 0.5 (Ahsan et al., 2018). Since our class distribution is uneven, the F1 score is a useful metric to measure performance. Except the boneless rib eye steak, every class has a very good weighted average of precision and recall, which infers that our model is practical and reusable.

Artificial intelligence (AI) utilizing the deep learning algorithm has potential to accurately classify different retail cuts of beef. Previously, researchers have successfully classified meat adulteration with better accuracy using support vector machine (SVM) and CNN from hyperspectral images (HSI) (Al-Sarayreh et al., 2018). The feature extraction technique and model complexity were adequate for only HSI. However, these technologies were not applicable for compressed images such as images from the cellular phone, digital camera, and Internet as input. Object detection is also applied for meat cut traceability using radio frequency identification (RFID) and physical tagging which seem promising for block chain technology (Larsen et al., 2014). Other than computer vision algorithms, different machine learning techniques are used by researchers to classify meat cut, which includes extensive feature extraction process and often hard to generalize. Some research has used a lot of noninvasive in vivo data which are collected from different categories to predict the meat cuts using the artificial neural network (ANN) and multiple linear regression (MLR) (Alves et al., 2019). The ANN is proven useful for lean tissue detection in the early research application of AI in meat cut using a hybrid image segmentation technique to produce RMSE as low as 0.044 (Hwang et al., 1997). These results are still not reusable as the sample size is only 40, which is very low for a neural network to be trained.

The beef cuts image classification system in this study was inspired by the convolutional neural network architecture based on the transfer learning approach (Abu et al., 2019; Ahsan et al., 2019). An end-to-end open-source machine learning platform developed by the Google research team called TensorFlow was used in this study. The TensorFlow deep learning library has many advantages in that it is an open-source system that is easily access to the public. Therefore, the present study results can be incorporated into the open-source repository and made available to everyone interested in classifying beef cuts. A free-access and off-the-shelf deep learning neural network with AI technology was improved by the incorporating image augmentation technique and evaluated to classify seven different types of beef cut images quickly. Both VGG16 and Inception Resnet V2 architecture coupled with image augmentation techniques, namely, rotation, flip, and shift using TensorFlow and Keras libraries were able to successfully identify and classify the beef cut correctly over 96% of the time. This study demonstrated that higher classification accuracy can be achieved using the pretrained CNN model coupled with the image augmentation technique in beef cuts classification. VGG16 (98.6%) outperformed the Inception ResNet V2 (95.7%) model in terms of classification accuracy.

This research is the harbinger of an efficient AI-based meat cut system, which is merely depicted as a prototype model. Higher classification accuracy and easy deployment of the AI model in the backend application program interface (API) for any type of application (web or mobile) have proven the significance of AI in meat classification. The deep learning model developed in this research has shown the potential to be used in a phone application to provide consumers a real-time beef cut recognition tool in meat industries. Therefore, the model developed for beef cuts classification was converted into a tflite format and deployed in a mobile application. Later, some of the random images from Google were tested in mobile application. Figure 10 shows the beef cuts classification part on a mobile application. Based on the type of the beef cuts recipe information, the mobile application provides the recipe information for the consumer. Statista (2016) noted that by 2020, the number of global smart phone users will reach to 2.87 billion. Thus, anyone with a smartphone with Internet access will have access to this beef cut classification tool through a phone application platform. The seven beef cuts selected for this research were identified as the most popular beef cuts sold at local retail markets. Future classification training could be added to the model that includes additional beef retail cuts like those available in print or online from the beef cuts guide maintained by the National Cattlemen's Beef Association (National Cattlemen’s Beef Association 2012).