Every day, humans generate vast amounts of waste that impact the environment and pose significant challenges for waste management systems worldwide. The world generates 2.01 billion tons of municipal solid waste annually, with at least 33% of the extremely conservative is not managed in an environmentally safe manner ^[1]. Moreover, the amount of waste produced annually around the world is predicted to rise dramatically from the current 2.01 billion tons to 3.40 billion tons by 2050 ^[2]. According to the Saudi Press Agency, the Riyadh Municipality removed more than 2 million tons of solid garbage from the capital’s various districts during the first half of 2022 ^[3]. The improper management of waste can have severe consequences for the planet, such as air and water pollution, soil degradation, climate change, and biodiversity loss, which threaten the health and well-being of both humans and wildlife. Recycling is a critical process that contributes to reducing the amount of waste that ends up in landfills, oceans, or other ecosystems ^[4]. Among the work needed for recycling, garbage sorting is the most fundamental step to enable cost-efficient recycling. However, sorting waste materials manually can be time-consuming, laborious, costly, and error prone. Moreover, the management of solid waste in major urban contexts has become a challenging issue due to the rising volume of waste produced daily by both companies and individuals. Resulting in several issues such as public health, environmental pollution and many others. Fortunately, advances in deep learning and computer vision techniques offer a promising solution to automate waste classification and enable more efficient recycling processes.

In the most celebratory work by Filimonau [5,6,7^{[5][6][7][8][9]},8,9], he emphasized food waste management in various sectors around the world. In ^[5], Filimonau and Gherbin presented exploratory research on food waste management practices in grocery stores in the United Kingdom (UK). As an outcome of the study, it was highlighted that though good policies for food waste management exist in the governance, food donations still need more attention in terms of improvement in consumer awareness, regulations, and effective policies. Based on the study, recommendations were made for retail stores. Similarly, in ^[6], the authors critically reviewed food waste management in the hospitality industry and highlighted the potential areas of improvement. Moreover, the feasibility analysis was made in terms of core in-house skills; training needs; preliminary financing costs; potential fiscal savings. In ^[7], the authors presented an important study on food waste management in Shanghai full-service restaurants. In this regard, comprehensive interviews were conducted with senior management to figure out the potential gaps in food waste management in the kitchens. As a result, the study concluded the ways to mitigate food waste by means of social campaigns, involving celebrities for public awareness programs and free-to-attend trainings for the senior management. In ^[8], the authors extended their work to address the similar as well as diverse nature of issues in ethnic food restaurants with special emphasis on the Chinese and UK markets.

An important and most significant study was conducted in ^[9] to reveal the aspects of waste management in the hospitality sector in the post-COVID-19 era. It is apparent that amid the COVID-19 pandemic ^[10], waste management was among the most significant areas of research especially plastic waste management when it comes to strictly restricting the fatal epidemic around the globe. The following were important highlights of the study in ^[9]:

• COVID-19 has increased food and plastic waste in hospitality operations.
• Alternative food networks (AFNs) can assist in food waste avoidance.
• Short food supply chains (SFSCs) can assist in effective food waste management.
• Corporate coopetition is essential to execute AFNs and SFSCs.
• Administrative revolution and official support can assist in plastic waste mitigation.

Based on the provided introduction to waste management, it is apparent that it is among the most important areas of research for a better, sustainable, and greener planet. Its benefits are manifold, for instance, food donations, public health and safety, recycling products and cleanliness. Moreover, in Saudi Arabia food waste management needs serious attention at the individual as well as government levels. The undergoing study is a contribution in this regard and motivation for a sustainable and green kingdom. It is also aligned to the kingdom’s Vision2030 for a greener and more sustainable future.

2. Recyclable Products Classification

Over the past several years, deep learning (DL) has become increasingly popular in image classification. For this purpose, multiple studies implemented various DL techniques to create classifier models using image data. Below are some literature reviews that identify potential areas for improvement in this research. In the study conducted by Rahman et al. [12]^[11], they introduce a DL-based automatic waste management system. The system utilizes the CNN algorithm as its basis. It is enhanced through the implementation of three key improvements: combining multiple input images with different features, repurposing remaining module features, and designing a novel activation function. The effectiveness of this new classification algorithm was then validated through experimentation using a public garbage dataset from GITHUB. The results of the study show that the proposed system exhibits a classification accuracy of 95.3125%. Moreover, an image classification model is presented in the study by Niu et al. [13]^[12], which efficiently distinguishes recyclable materials. The “Dual-branch Multi-output CNN” is introduced, which is a custom CNN comprised of two branches designed to classify recyclables and identify the type of plastic. The proposed architecture includes two classifiers trained on distinct datasets to encode different attributes of the recyclable materials. The Trash net dataset was used in combination with data augmentation techniques, and the WaDaBa dataset was leveraged using physical variation techniques. The joint utilization of the datasets enabled the learning of separate label combinations. The effectiveness of the model is confirmed through experiments, which shows an accuracy of 90.02% in waste material classification. Furthermore, in the study by Majchrowska et al. [14]^[13], the authors suggest a waste detection method that employs deep learning in a constructive manner. Initially, standardized datasets were formulated for waste detection and classification, integrating open-source information for all probable categories of waste, including metals, plastics, paper, unknown waste, non-recyclables, vital waste, and glass. Following this, a two-stage garbage localizing and classification detector was introduced. The garbage locator was created using Efficientdet-D2, while the waste classifier applied Efficientnet-B2 to sort the detected waste into seven classifications. Semi-supervised training was used to prepare the classifier by exploiting unclassified images. The approach proposed delivered up to 70% of the mean accuracy in waste detection and approximately 75% of accuracy in classification for the test dataset. Similarly, the authors conducted a study to propose a multi-layer system for classifying waste. The proposed method is a deep learning model that combines layers of the CNN model with a multi-layer perceptron (MLP). The study trained the model with a dataset of 5000 images, with 100 images for each waste class. The results showed that the MHS model outperformed the CNN model, achieving 92% and 91% accuracy in two testing scenarios. These findings suggest that the proposed model has the potential for improving waste classification accuracy [15]^[14]. The study aimed to develop an automated system for sorting trash and proposed a deep neural network called Deep Neural Network for Trash Classification (DNN-TC). DNN-TC uses the ResNext model with several improvements, including adding two fully connected layers after the global average pooling layer. The model was trained on the VN-trash dataset, which includes 5904 images from Vietnam. Testing the model on two different datasets, VN-trash, and TrashNet, showed that it achieved accuracies of 94% and 98%, respectively. In comparison to state-of-the-art methods, the DNN-TC model outperformed them by a significant margin. These findings demonstrate the potential for the DNN-TC model to improve the accuracy and efficiency of automated trash sorting systems [16]^[15]. The aim of this restudyearch was to develop a deep neural network image classifier that can identify and classify different types of waste material. The authors utilized multiple CNNs, such as VGG 16 and ResNet, to extract features from the images and feed them into the classifier to make predictions. Among the models tested, Densenet169 outperformed the others with 94.9% accuracy, as measured on a specific dataset after image scraping. These findings demonstrate the potential of Densenet169 for improving waste classification and management systems [17]^[16]. The authors of [18]^[17] aimed to create a system that can accurately identify metal objects and classify them with high accuracy. Rather than creating a new model, the authors focused on understanding the already-existing models to find the most suitable one. The system proposed in this restudyearch consists of four modules: the first is a smart camera to capture the object, the second extracts the region of interest, the third is where preprocessing takes place, and finally, the preprocessed data is fed to a deep learning model. The authors conducted experiments with multiple deep learning models, such as GoogleNet, VGGNet, and AlexNet, and found that AlexNet was the most suitable, with the highest recognition rate in both experiments. Another study aimed to develop a system that automatically classifies waste based on its material without human intervention. The dataset used in the study is the classification of trash for recyclability status. Since the dataset was not large enough, the authors used image augmentation techniques to generate more data. The study employed a CNN model with an input layer that takes an image of size 150 × 150 × 3 and 9 hidden layers, including the output layer. The study found that using hyperparameters such as dropout with a value of 0.5 and the Rectified Linear Unit (ReLU) activation function for the CNN resulted in the highest accuracy of around 85% to 90% on the training data and 80-86% on the validation data. This automated waste classification system has the potential to reduce the environmental impact of improper waste disposal, increase recycling rates, and promote a more sustainable future [19]^[18]. Ruiz et al. [20]^[19] presented a study that aims to use the TrashNet dataset to improve a deep-learning model for classifying isolated garbage. Mindy Yang and Gary Thung created the dataset at Stanford University, which contains 2527 RGB images of six waste classes. Researchers used several attractive CNN models for the automatic classification of waste. Also, the researchers used many methodologies, and the experiments were about OscarNet based on VGG-19 pre-trained with an accuracy of 88.48% and GarbeNet based on CNN with an accuracy of 87.69%. In conclusion, the best result on the Trash Net dataset was achieved using the Inception-ResNet model with 88.66% of average accuracy. In the future, the researchers want to generate realistic synthetic images with more types of waste for their training model and then test them with actual photos that combine several types of garbage. In a study by Alsubaei et al. [21]^[20], the researchers were interested in developing a novel deep learning model to detect and classify the small object for garbage waste management (DLSODC-GWM) technique. In their research, they used data from benchmark datasets to predict the performance validation of the method. Therefore, the goal of using and designing this technique was to detect objects utilizing an arithmetic optimization algorithm (AOA) to select the optimal hyperparameter values to improve the RefineDet (IRD) model detection efficiency. In addition, the researchers applied a model for classifying waste objects into multiple categories called the Functional Link Neural Network (FLNN) model. Thus, after comparing other technologies such as MLH-CNN, AlexNet, RestNet50, and VGG16, the DL model with (DLSODC-GWM) technique reached a high score of 95.23% in precision, 94.29% in the recall, and 94.73% in F-score. The study by Meng and Chu [22]^[21] focused on improving the learning model that can detect the garbage entity from an image and classify it into one of the categories by employing deep learning methods. For garbage classification, the used dataset collected from Kaggle consists of 2527 images. The dataset was divided randomly and the experiment was conducted using the Support Vector Machines (SVM), convolutional neural network (CNN), and the histogram of oriented gradients (HOG), the models runs with and without the data augmentation then models were trained with different hyperparameters which are ReLU, and SoftMax activation functions, with the optimizers Adam and Adadelta, besides 40 epochs, 32 and 16 batch size, the dropout rate of 0.5, and the cross-entropy loss function. The results concluded that the best-performing algorithm was a simple CNN model with 82% training accuracy and 81% test accuracy. In another study by Fu et al. [23]^[22], the authors proposed a deep learning-based system to classify wastes. As for the dataset used in this model, it is from the Huawei challenge cup for classifying the garbage [24]^[23] including 40 categories and a total of 24,000 images. The design of the system includes two components: the hardware contains six devices and the classification models including ResNEt34, VGG126, InceptionV3, DenseNet121, MobileNetV3, and GNet the experiments were conducted using several learning rates and epochs. The study found that the best results were achieved using the Gnet algorithm and the accuracy was 92.62% for testing. Furthermore, In a paper authored by Ozkaya and Seyfi [25]^[24]. The authors provided deep learning-based techniques for developing garbage classification model. The dataset that was collected from TrashNet includes 2527 images and six categories. The predictive model was built using several CNN structures for fine-tuning which are: AlexNet, VGG-16, GoogleNet, ResNet, and SquezeeNe, likewise two classifiers were used to assess the execution Softmax and SVM. The highest accuracy is 97.86% and was obtained by GoogleNet and SVM. Chen and Xiong [26]^[25] aimed to build a garbage classification model by YOLOVE. The model was built based on aVOC dataset consisting of 22,000 images, and three classes each with five kinds of garbage. The dataset was split into 70:30 proportions for training and testing after that trained using YOLOV3, YOLOV4, and improved YOLOV4 algorithms with 1200 iterations and 16 batch size, along with Ciou-Loss’s regression loss, and Diou-nums’s classification loss. The results showed that the YOLOV4 achieved the highest FBS with 92 f/s and mAP with 64%. Moreover, Zeng et al. [27]^[26] focused on developing a model for classifying the garbage by utilizing CNN’s structure using a collected public dataset from Stanford University consisting of 10,624 images with four main classes and 10 sub-categories. The Keras package with TensFlow was used to train the algorithms: DenseNet121, DenseNet169, ResNet50, ResNet101, ResNeXt50, ResNeXt101, Efficientnet-B3, and Efficientnet-B4, respectively. Besides the dataset split with a 5:1 ratio to training and testing sets, further, the study used various data augmentation methods, with random flip, random rotation, random translation, center clipping, and random erasure with Adam 0.0001 leaning rate and label smoothing together with PublicGarbageNet that was the best-performing model with 96.35% accuracy. In [28]^[27], authors attempted to improve the efficiency of classifying social garbage and built a CNN classifier. To average the brightness of the image’s background, which led to low accuracy due to interference in the light and shadow, they used an adaptive image-brightening algorithm. Moreover, the Canny operator has been utilized to help crop blank backgrounds. The result of the study shows that the classifier reached an accuracy of 96.77% on the self-built dataset and 93.72% on the TrashNet dataset. Similarly, the authors in [29]^[28] developed an automated garbage sorting tool to make it simpler for locals to categorize garbage as the problem becomes more prevalent. They divided garbage into six categories using the TrashNet dataset. They were able to accomplish their goals by using CNN classifier and exploring numerous well-known architectures in the beginning phases. They arrived in a modified version of AlexNet by removing two layers, and they experimented with other model architecture-based strategies, such as dropout, data augmentation, and learning rate decay. In the final layer of the model, they experimented with two classifiers: Softmax and SVM. The result of this study attained an accuracy of 79.94% on the test dataset. The authors in [30]^[29] developed a CNN classifier to address the real-world waste management system’s practical issue. Researchers could attain an accuracy of 79%, according to the study’s final findings. According to [31]^[30], India generates more than 2 billion tons of waste annually, while solid waste is separated by laborers in an inefficient manner that is both time-consuming and impractical. To distinguish the type of garbage and classify it into predetermined categories, authors created a real-time system. using a CNN classifier on four datasets: Garythung Yang, Waste classifier master, TrashNet, and Real images, they successfully reached a test accuracy of 89% on the TrashNet dataset. The authors of [32]^[31] stated that automation of waste classification is one of the efficient approaches to fully utilize these resources because garbage is an underutilized resource. For the recognition of garbage images, certain deep-learning models were employed. Also, a Garbage Classification Network (GCNet) based on model fusion and transfer learning was suggested in this preseaperrch. The EffcientNetv2, Vision Transformer, and DenseNet, respectively, were combined to develop the Neural Network model of GCNet. The dataset was expanded through data augmentation, and the resultant dataset contained 41,650 garbage images. The suggested model has good convergence and a high accuracy compared to other models, in which it successfully attained an accuracy of 97.54%. In another study presented in [33]^[32], the authors aimed to develop a model that can accurately identify and classify different types of garbage. They used the TrashNet dataset consisting of images of six types of garbage, with the YOLOv5 algorithm. YOLOv5 automatically learns features from input, so no feature selection was needed. The model’s performance was evaluated using five-fold cross-validation, resulting in 95.51% accuracy. One area that the study lacks is the size of its dataset and the type of garbage it represents. In [34]^[33], the authors proposed an intelligent waste classification system that uses convolutional neural networks to automate the process of waste sorting. The dataset they used, called the “Garbage Classification Dataset”, was collected by them from multiple sources, but may not have been representative of all garbage types. They used transfer learning on the pre-trained model VGG16 as the base, then added additional layers to fine-tune the network. Using the five-fold cross validation technique, they reported an accuracy of 86%. The study in [35]^[34] aimed to improve waste classification accuracy by developing a system using a fusion of deep learning features. The authors made the dataset [36]^[35] using images of 4 different types of waste: plastic paper, metal, and glass. They used a fusion-based deep learning approach that combined the features learned from pre-trained models, including VGG16, ResNet50, InceptionV3, and MobileNetV2. They fine-tuned each model with transfer learning, then performed the final classification using an SVM classifier. For validation, 10-fold cross-validation was used. In the end, they achieved 87% accuracy. Likewise, a study in [37]^[36] presents a waste classification model based on a multilayer hybrid CNN (MLHCNN). The authors created the dataset from images of plastic, metal, paper, glass, and residual waste. The images were collected from garbage sorting stations and garbage transfer stations in China. The MLHCNN consists of a feature extraction module that uses two pre-trained CNNs to extract features from the images, a feature fusion module that combines the extracted features, and a classification module that classifies the waste images. The authors used 10-fold cross-validation to evaluate the performance of their MHCNN model. The model achieved an accuracy of 92.6%. The study in [38]^[37] proposes a trash classification approach using deep learning. The authors used a deep learning-based approach called ScrapNet, which uses a CNN architecture. They fine-tuned a pre-trained InceptionV3 model on their dataset, TrashNet. Also, data augmentation techniques were used to increase the dataset’s size and performance. With 10-fold cross-validation, the reported accuracy on the TrashNet dataset was 92.87%.