Waste management is most serious problem that the world in 21st century is about to face. A recent study reported that the amount of waste annually reaches up to 2.2 billion tons in the next 3–4 years that might cost around $375 billion to the management of waste [1]. Other studies of the Food and Agriculture Organization (FAO) of the United Nations has shown that almost 33% of the overall food is produced worldwide for everyday utilization which is almost 1.3 billion tons in weight, is wasted or lost [2]. This irregular waste management might cause a negative effect on the environments, public health, and economy [3]. The waste recycling method is extremely important interms of the environment and economy [4]. In many emerging nations, the local municipal corporation manually recycles the waste that is costly, time utilizing, and very inefficient since the corporation has to recruit a great number of laborer’s to manually work [5]. Fig. 1 illustrates the application of smart city.

Machine Learning (ML) method describes an important process of Artificial Intelligent (AI) technique which permits a system the capacity for automatically making the decision and learn with no being explicitly trained [6]. The ML method is scientific research of several statistical algorithms and models. Because of the exceptional feature in computing, the prevalence of ML was obtained the maximum peaks. These statistics obviously shows the prominence of ML based application. Likewise, deep learning (DL) methods are an important part of ML method [7]. The convolution neural networks (CNN) are momentous classes of deep neural network (DNN) systems, especially, DL method. The CNN presents considerable advances in image classification. Generally, it can be employed to estimate visual imagery and frequently functioned besides image recognition.

CNN was enjoyed a great achievement in image classification and recognition [8]. The accuracy and popularity of the CNN to an image classifier was improved because of the high-speed GPU, wide largescale integrated system, and accessibility of public data of image for the learning and image processing [9]. The hybrid model of utilizing a multi-layer perceptron with CNN is another effective method. The sensor technique fused in the scheme is capable of detecting metal waste object that is later categorized into certain groups [10]. Therefore, image processing using DL method has assisted municipalities to discard manual waste classification. The application of smart waste classification has been employed and provides better outcomes.

This study develops a novel Deep Consensus Network with Whale Optimization Algorithm for Recycling Waste Object Detection (DCNWO-RWOD) in Smart Cities. The proposed DCNWO-RWOD technique involves the design of deep consensus network (DCN) to detect waste objects in the input image. For improving the overall object detection efficiency of the DCN model, the whale optimization algorithm (WOA) is exploited. Finally, Naïve Bayes (NB) classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones. The performance validation of the DCNWO-RWOD technique takes place using the open access dataset.

Ghanshala et al. [11] presented ML based structure for classifying the regions that are free in garbage and regions containing maximum density garbage. Baker et al. [12] examined the developing requirement for automatic e-waste recycling as a vital condition for coping with fast-developinge-waste streaming and it can be shed the light on effect of AI from supportive the recycling procedure with smart classifier of devices, in which the smartphone was analysis. This technique was executes transfer learning (TL) as a particular method of AI with fine-tune the resultant layer of AlexNet as pre-training method and implementing the execution on small-size data set which has 12 classes in 6 smartphone brands. Niu et al. [13] analyzed for implementing TL approaches for constructing a robust method dependent upon somewhat small set of trained data with transmitting data in present deep network namely AlexNet, Resnet, and DensNet. While the alteration, can be present a novel domain loss function, Dual Dynamic Domain Distance (4D) for producing further correct area distance measurements.

Alqahtani et al. [14] progress the IoT based Urban Waste Management system. An IoT device is utilized for monitoring human activity and for supporting waste management. The information on a city has gathering and processing from cuckoo search (CS)-optimizing long short-term recurrent neural network (RNN). The network enabled the study of waste sources, waste variety, and truck size. This data notified the waste management center for appropriating performances are obtained. Khoa et al. [15] propose the examination case executed at the real campus of Ton Duc Thang University (Vietnam) for evaluating the efficiency and feasibility of model execution. It can be examining data transfer on LoRa component and illustrate the benefits of presented method that is executed with easy circuit planned with minimum cost, usability, and replaceability. This method saved time by determining an optimum route from the management of waste collections.

In [16], an effective solution to smart and effective waste management utilizing ML and IoT was presented. During the presented solution, the researchers have utilized moisture sensor, Arduino UNO microcontroller, and ultrasonic sensor. The hardware prototype is also established to present structure. Therefore, the projected solution to effective waste management realizes the purpose of establishing clean and pollution-free city. In [17], the concept of realtime smart waste classifier technique was projected which utilizes a hybrid method for classifying waste as to different classes. There are 2 ML techniques as multilayer perceptron (MLP) and multilayer CNN (ML-CNN) are executed. The MLP was utilized for providing binary classifiers, for instance, metal or non-metal wastes, and CNN recognizes the class of non-metal wastes. The camera was located facing the waste conveyor belt that gets an image of wastes and classifies it.

In this study, an effective DCNWO-RWOD technique has been developed to properly identify and classify the objects into recyclable and non-recyclable ones. The proposed DCNWO-RWOD technique undergoes major subprocesses namely DCN based object detection, WOA based hyperparameter tuning, and NB based classification. For improving the overall object detection efficiency of the DCN model, the WOA is exploited.

The performance validation of the proposed model takes place using the benchmark dataset from Kaggle repository (available at https://www.kaggle.com/asdasdasasdas/garbage-classification). The dataset comprises images under six class labels namely cardboard, glass, metal, paper, plastic, and trash. The results are examined in two ways namely training/testing of 80:20 and 70:30. The amount of samples under each class is depicted in Table 1. Figs. 3–4 illustrates the sample images of waste classification.

Fig. 5 depicts the confusion matrix derived by the DCNWO-RWOD technique on the train/test data of 80:20. The figure reported that the DCNWO-RWOD technique has proficiently classified 75 images into class 0, 95 images into class 1, 80 images into class 2, 115 images into class 3, 93 images into class 4, and 22 images into class 5.

Table 2 and Fig. 6 demonstrate the overall classifier result analysis of the DCNWO-RWOD technique under train/test data of 80:20. The results have shown the effectual outcome of the DCNWO-RWOD technique under all class labels. For instance, with class 0, the DCNWO-RWOD technique has attained precn of 0.9747, recal of 0.9747, accuy of 0.9919, Fscore of 0.9747, and MCC of 0.9699. Simultaneously, with class 3, the DCNWO-RWOD methodology has gained precn of 0.9831, recal of 0.9915, accuy of 0.9939, Fscore of 0.9872, and MCC of 0.9833. Concurrently, with class 5, the DCNWO-RWOD approach has obtained precn of 0.9600, recal of 0.9600, accuy of 0.9959, Fscore of 0.9600, and MCC of 0.9579.

Fig. 7 showcases the confusion matrix derived by the DCNWO-RWOD approach on the train/test data of 70:30. The figure portrayed that the DCNWO-RWOD system has proficiently classified 115 images into class 0, 144 images into class 1, 117 images into class 2, 173 images into class 3, 138 images into class 4, and 32 images into class 5.

Table 3 and Fig. 8 illustrate the overall classifier result analysis of the DCNWO-RWOD approach under train/test data of 70:30. The outcomes exhibited the effectual outcome of the DCNWO-RWOD approach under all class labels. For instance, with class 0, the DCNWO-RWOD method has obtained precn of 0.9664, recal of 0.9746, accuy of 0.9905, Fscore of 0.9705, and MCC of 0.9648. Besides, with class 3, the DCNWO-RWOD method has attained precn of 0.9886, recal of 0.9886, accuy of 0.9946, Fscore of 0.9886, and MCC of 0.9850. Finally, with class 5, the DCNWO-RWOD methodology has gained precn of 0.8889, recal of 0.8421, accuy of 0.9865, Fscore of 0.8649, and MCC of 0.8581.

Fig. 10 portrays the receiver operating characteristic (ROC) analysis of the DCNWO-RWOD method train/test data of 70:30. The figure depicted that the DCNWO-RWOD approach has reached improved outcomes with a maximum ROC of 99.9469.

Fig. 9 demonstrates the sample waste classification results obtained by the DCNWO-RWOD technique. The figure reported that the DCNWO-RWOD technique has effectually identified and classified different classes of waste objects.

A comparison study of the garbage waste classification model with recent approaches is shown in Table 4. Fig. 10 illustrates the comparative precn analysis of the DCNWO-RWOD approach with recent methods. The figure reported that the CNN model has accomplished ineffectual outcome with the lower precn value of 0.8860. In line with, the MHS technique has resulted in slightly increased precn value of 0.9710. Followed by, the MLP and ML-CNN techniques have accomplished moderately closer precnvalues of 0.9800 and 0.9800 respectively. But the DCNWO-RWOD technique has surpassed the other methods with precn of 0.9807.

Fig. 11 depicts the comparison recal analysis of the DCNWO-RWOD approach with recent techniques. The figure revealed that the CNN approach has accomplished ineffectual outcome with the lower recal value of 0.0680. Likewise, the MHS approach has resulted in somewhat superior recal value of 0.9230. Also, the MLP and ML-CNN systems have accomplished moderately closer recal values of 0.0840 and 0.9800 respectively. But the DCNWO-RWOD method has surpassed the other techniques with recal of 0.9804.

Fig. 12 demonstrates the comparative accy analysis of the DCNWO-RWOD system with recent methods. The figure portrayed that the CNN method has accomplished ineffectual outcomes with the lesser accy value of 0.8770. Also, the MHS approach has resulted in slightly higher accy value of 0.9160. Afterward, the MLP and ML-CNN systems have accomplished moderately similar accy values of 0.9900. But the DCNWO-RWOD methodology has surpassed the other approaches with accy of 0.9946.

In this study, an effective DCNWO-RWOD technique has been developed to properly identify and classify the objects into recyclable and non-recyclable ones. The proposed DCNWO-RWOD technique undergoes major subprocesses namely DCN based object detection, WOA based hyperparameter tuning, and NB based classification. For improving the overall object detection efficiency of the DCN model, the WOA is exploited. Finally, the NB classifier is used for the classification of detected waste objects into recyclable and non-recyclable ones. The performance validation of the DCNWO-RWOD technique takes place using the open access dataset. The extensive comparative study reported the enhanced performance of the DCNWO-RWOD technique interms of several measures. Therefore, the DCNWO-RWOD technique has been employed as an effective tool for waste object detection and classification. In future, the detection performance of the DCNWO-RWOD technique can be boosted by the design of ensemble of DL models.