Design of a waste classification system using a low experimental cost capacitive sensor and machine learning algorithms

Academic Editors: Tomohiro Tabata, Haoqi Yang, Ganyu Zhu and Jun-Ye Zhang Received: 25 November 2024 Revised: 27 January 2025 Accepted: 29 January 2025 Published: 4 February 2025 Citation: Vesga Ferreira, J.C.; Perez Waltero, H.E.; Vesga Barrera, J.A. Design of a Waste Classification System Using a Low Experimental Cost Capacitive Sensor and Machine Learning Algorithms. Appl. Sci. 2025, 15, 1565. https://doi.org/10.3390/ app15031565 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Design of a Waste Classification System Using a Low Experimental Cost Capacitive Sensor and Machine Learning Algorithms Juan Carlos Vesga Ferreira 1,* , Harold Esneider Perez Waltero 1and Jose Antonio Vesga Barrera 2 1Telecommunications Engineering Program, School of Basic Sciences, Technology and Engineering (ECBTI), Universidad Nacional Abierta y a Distancia, Bogotá 110231, Colombia; harold.per[email protected] 2Faculty of Engineering, Corporación Universitaria de Ciencia y Desarrollo, Bogotá 110111, Colombia; [email protected] *Correspondence: [email protected]; Tel.: +57-3212759948 Abstract: The management and classification of solid waste is one of the most important challenges worldwide. The objective is to design a basic waste classification system at the source using a low-cost experimental capacitive sensor and machine learning algorithms. For this, two types of sensor models were established (Traditional Model (MT) and NonTraditional Model (MNT)), which were built with recyclable material and tested with different types of materials, in order to evaluate their behavior and sensitivity level. The results obtained demonstrated that the two sensors responded with acceptable sensitivity levels for each of the materials used as a test; however, the MNT was the one that generated the values with the greatest variability, an aspect that is deemed highly significant, because, thanks to this type of response to various types of materials, it facilitates the classification processes through the use of machine learning algorithms. Finally, the two prototypes of sensors manufactured can be considered of significant relevance for the development of more complex solutions, related to the classification and possible characterization of materials, when compared to the capacitive sensors found on the market, which only then allow us to identify if there is a presence or not of some object through adjustment by potentiometer, generating as a result a digital output. This aspect largely limits the use of commercial capacitive sensors to applications exclusively related to presence or level detection. Keywords: sensor; capacitive; solid waste; machine learning; artificial intelligence 1. Introduction Solid waste management stands among the most pressing global challenges today. The generation of waste has steadily increased, driven by factors such as rapid population growth, accelerated industrialization, and economic development [ 1 ]. This trend has given rise to a series of environmental and social problems, particularly regarding public health and the conservation of the natural environment [ 2 ]. Proper waste management is essential to mitigate these negative effects, requiring the implementation of efficient processes that encompass waste collection at the source, transportation, treatment, segregation, and final disposal [ 3 ]. Developing countries, in particular, face the challenge of optimizing these processes, whose effectiveness largely depends on the knowledge and resources available to the institutions responsible for their implementation [4]. Within the framework of environmental policies, solid waste management holds a fundamental position. Its proper implementation aims not only to reduce environmental Appl. Sci. 2025,15, 1565 https://doi.org/10.3390/app15031565 Appl. Sci. 2025,15, 1565 2 of 19 impact but also to improve living conditions, prevent public health issues, and promote sustainability [ 5 ]. Solid waste classification has gained prominence as a key measure to achieve sustainable economic development goals, reduce poverty, and preserve ecosystems. The adoption of efficient solid waste classification systems is crucial to creating jobs, stimulating the recycling sector, and fostering social well-being [ 6 ]. Therefore, efforts must focus on improving waste separation practices at the source, benefiting both the environment and the communities that rely on these economic activities. Given this scenario, there is a need to develop innovative technologies that optimize waste classification at its origin, enabling a more effective resolution of this issue. With the advancement of emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence, governments worldwide are working toward the creation of “smart cities,” where efficient waste management plays a fundamental role [ 7 ]. These technologies have great potential to automate and optimize waste classification processes. However, most solutions developed for waste classification focus on image recognition using techniques such as convolutional neural networks, optical methods, and spectrophotometry, among others. Below are some of the works related to this field: Machine learning-based systems for specific materials: • In [ 8 ], researchers designed recycling machines equipped with ML to classify glass and plastic bottles. Although effective, these solutions are limited to transparent materials due to the optical properties required for their detection. Hybrid solutions with spectrophotometry and ML: • In [ 9 ], a study integrated spectrophotometry with ML, achieving high accuracy in waste classification. However, the high cost of implementation makes it difficult to adopt in resource-limited environments. Classification using convolutional neural networks (CNN): • In [ 10 ], researchers proposed YOLO-Green, a model optimized for real-time detection that classifies waste from images. Although efficient, its performance decreases considerably when processing contaminated waste, a common problem in recycling centers. In [ 11 ], researchers developed a system to classify recyclable materials using CNN, achieving an accuracy of 94.5%. However, its implementation depends on expensive machine vision equipment and controlled conditions, which limits its applicability in contaminated environments. Deep Learning Techniques: • In [ 12 ], researchers propose ConvoWaste, which uses deep neural networks to classify waste into categories such as plastics and metals, achieving an accuracy of 98%. • In [ 13 ], the research proposes an intelligent model to categorize waste using deep learning techniques. AlexNet, DenseNet121, and SqueezeNet have been implemented to perform the classification tasks. The obtained results showed great success in the classification process. DenseNet121 achieved the best performance with a value of 0.9415 in terms of accuracy. However, its reliance on specific databases restricts its generalization to other contexts. While these methods offer high levels of effectiveness, they often require expensive equipment and are, in many cases, sensitive to the presence of impurities. This makes them unsuitable for environments with high levels of contamination, such as waste collection points, significantly increasing implementation costs and limiting their large-scale adoption. In this context, the present article proposes an innovative solution through the development of a low-cost capacitive sensor with the capability and sensitivity to facilitate the classification of various types of solid waste at the source, including glass, plastic, metal, Appl. Sci. 2025,15, 1565 3 of 19 and organic waste. This solution utilizes machine learning algorithms, offering an efficient and sustainable alternative to this pressing issue. 2. Materials and Methods 2.1. Solid Waste Classification The classification or segregation of solid waste is considered by numerous studies as one of the fundamental pillars of the Solid Waste Management System [ 14 ]. These wastes are composed of materials, many of which are recyclable, such as paper, cardboard, glass, plastic, rubber, and ferrous and non-ferrous metals, which can be reused in various industrial processes. On the other hand, organic waste has the potential to be transformed for the production of biogas and biofertilizers. However, due to inadequate waste management and incorrect collection practices, common in many cities, serious social, health, and environmental problems have been generated, in addition to economic, resource, and energy losses [ 15 ]. As a consequence of this poor management, recycling rates are extremely low and the amount of waste deposited in landfills exceeds their capacity, which negatively impacts society and the natural environment. When talking about the correct way to separate waste, the method best known and implemented by institutions and at the domestic level is the manual classification method, which consists of depositing waste in different containers. Taking into account their material, this method is supported by pedagogy programs that instruct people on the subject [ 16 ]. However, the technology of garbage sorting and environmental monitoring has developed rapidly in recent years, where the combination of the Internet of Things, artificial intelligence and environmental technology has become a trend for solving environmental and social problems. [17]. 2.2. Sensors A sensor is a device that is used to measure or detect a change in the environment or in a physical variable under study, which will deliver an output value under a specific format [ 18 ]. An analog sensor produces a voltage or continuous output signal, which is generally proportional to the quantity being measured. Digital sensors, on the other hand, produce discrete digital output signals or voltages that are a digital representation of the quantity being measured. Digital sensors produce a binary output signal in the form of a logic “1” or a logic “0”, (“ON” or “OFF”) or can be represented as a “byte” (8 bits). 2.3. Capacitive Sensor Capacitive proximity sensors are devices that, without the need for contact, allow the detection of the presence or absence of practically any object regardless of the material (glass, paper, metal, plastic, biological, etc.). They use the electrical property called “capacitance”, where by means of a change in the electric field produced by the object under observation (“dielectric”), it generates a change in the value of the resulting capacitance, thereby facilitating the detection of an object and the possibility of identifying the type of material that composes it [8]. Figure 1shows the appearance of the capacitive sensor. When an object is located within the sensitive zone, an alteration of the electric field is produced, varying the resulting capacitance and therefore the frequency of the oscillator. There are two categories of targets that capacitive sensors can detect: the first is conductive and the second is non-conductive. Conductive targets include metal, water, blood, acids, bases, and salt water, among other similar materials. These targets have a higher capacitance and the dielectric strength of the targets is irrelevant [ 19 ]. When it comes to metallic elements, the electric field decreases since a phenomenon equivalent to series capacitances occurs. Appl. Sci. 2025,15, 1565 4 of 19 Appl. Sci. 2025, 15, x FOR PEER REVIEW 4 of 19 Figure 1. Appearance of the capacitive sensor. When an object is located within the sensitive zone, an alteration of the electric field is produced, varying the resulting capacitance and therefore the frequency of the oscillator. There are two categories of targets that capacitive sensors can detect: the first is conductive and the second is non-conductive. Conductive targets include metal, water, blood, acids, bases, and salt water, among other similar materials. These targets have a higher capacitance and the dielectric strength of the targets is irrelevant [19]. When it comes to metallic elements, the electric field decreases since a phenomenon equivalent to series capacitances occurs. On the other hand, when dealing with a non-conductive target, it acts as an insulator for the electrode of the sensors. A target dielectric constant, also sometimes called a dielectric constant, is the measure of insulation properties used to determine the sensing distance reduction factor. For non-metallic objects, there is an increase in the electric field due to the increase in the dielectric constant. Capacitive sensors are normally used for the detection of non-metallic materials such as glass, oil, water, cardboard, ceramics, plastic, wood, and paper, among others [20]. Although there are several very high-precision methods for measuring parameters related to magnetic permeability, electrical permittivity, and conductivity of a material, among which microwave measurement methods stand out, these are usually very expensive to implement in materials and equipment, which is why they are not considered suitable for the solution required for the problems associated with the identification of materials for the classification of waste at the source. Given this situation, it is considered feasible to carry out the classification process alternately, using a low-cost sensor, articulated with classification techniques-based machine learning algorithms. 2.4. Design of the Proposed Capacitive Sensors For the fabrication of the two proposed sensors, recycled aluminum sheets from beer cans were utilized. This material was selected due to its accessibility, reusability, and alignment with the overarching objectives of the project. It is important to note that beer or soft drink cans can be made of either steel or aluminum, depending on the application and the production region, where the availability of these materials may vary. However, in most cases, cans are made of aluminum, as in this study, due to its effectiveness in Figure 1. Appearance of the capacitive sensor. On the other hand, when dealing with a non-conductive target, it acts as an insulator for the electrode of the sensors. A target dielectric constant, also sometimes called a dielectric constant, is the measure of insulation properties used to determine the sensing distance reduction factor. For non-metallic objects, there is an increase in the electric field due to the increase in the dielectric constant. Capacitive sensors are normally used for the detection of non-metallic materials such as glass, oil, water, cardboard, ceramics, plastic, wood, and paper, among others [20]. Although there are several very high-precision methods for measuring parameters related to magnetic permeability, electrical permittivity, and conductivity of a material, among which microwave measurement methods stand out, these are usually very expensive to implement in materials and equipment, which is why they are not considered suitable for the solution required for the problems associated with the identification of materials for the classification of waste at the source. Given this situation, it is considered feasible to carry out the classification process alternately, using a low-cost sensor, articulated with classification techniques-based machine learning algorithms. 2.4. Design of the Proposed Capacitive Sensors For the fabrication of the two proposed sensors, recycled aluminum sheets from beer cans were utilized. This material was selected due to its accessibility, reusability, and alignment with the overarching objectives of the project. It is important to note that beer or soft drink cans can be made of either steel or aluminum, depending on the application and the production region, where the availability of these materials may vary. However, in most cases, cans are made of aluminum, as in this study, due to its effectiveness in preserving beverages by protecting them from air, light, and potential contamination risks, including bacteria, viruses, hazardous chemicals, and physical agents. Moreover, aluminum is an economical and sustainable material, thanks to its high recyclability [ 21 ]. Cans are typically coated with a thin layer of paint and plastic. The plastic serves to separate the beverage from the aluminum, preventing chemical reactions between the liquid and the metal or contamination from direct contact. The most common plastics used for this type of coating include polyethylene (PE), polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). As an insulating Appl. Sci. 2025,15, 1565 5 of 19 material, plastic possesses a relative electrical permittivity, which directly influences the capacitance value of each sensor. Table 1provides the relative electrical permittivity values for the aforementioned types of plastic [22]. Table 1. Electrical permittivity values for some types of plastic. Plastic Type Electrical Permittivity (εr) Polyethylene 2.3 Polystyrene 2.6 Polypropylene 2.2 to 2.6 PVC 2.9 Polyethylene Terephthalate PET 2.8 During the construction process of the MT and MNT sensors, a plastic layer (polypropylene) was used as protection against the environment in which they will be used, establishing a value of εr= 2.6. In addition, this layer allowed maintaining a constant distance between the plates, avoiding direct contact between them. 2.4.1. Sensor Design MT This sensor is made up of two concentric plates, where one of the plates is circular with radius aand the other plate has the shape of a circular crown with inner and outer radius b and c, respectively. Figure 2shows the proposed diagram for the MT sensor, accompanied by the distribution of electrical charges on each of the plates and electric field lines, in order to establish the conditions and key elements for estimating the capacitance value. Appl. Sci. 2025, 15, x FOR PEER REVIEW 5 of 19 preserving beverages by protecting them from air, light, and potential contamination risks, including bacteria, viruses, hazardous chemicals, and physical agents. Moreover, aluminum is an economical and sustainable material, thanks to its high recyclability [21]. Cans are typically coated with a thin layer of paint and plastic. The plastic serves to separate the beverage from the aluminum, preventing chemical reactions between the liquid and the metal or contamination from direct contact. The most common plastics used for this type of coating include polyethylene (PE), polypropylene (PP), polystyrene (PS), polyethylene terephthalate (PET), and polyvinyl chloride (PVC). As an insulating material, plastic possesses a relative electrical permittivity, which directly influences the capacitance value of each sensor. Table 1 provides the relative electrical permittivity values for the aforementioned types of plastic [22]. Table 1. Electrical permittivity values for some types of plastic. Plastic Type Electrical Permittivity (𝜺𝒓) Polyethylene 2.3 Polystyrene 2.6 Polypropylene 2.2 a 2.6 PVC 2.9 Polyethylene Terephthalate PET 2.8 During the construction process of the MT and MNT sensors, a plastic layer (polypropylene) was used as protection against the environment in which they will be used, establishing a value of 𝜀=2.6. In addition, this layer allowed maintaining a constant distance between the plates, avoiding direct contact between them. 2.4.1. Sensor Design MT This sensor is made up of two concentric plates, where one of the plates is circular with radius a and the other plate has the shape of a circular crown with inner and outer radius b and c, respectively. Figure 2 shows the proposed diagram for the MT sensor, accompanied by the distribution of electrical charges on each of the plates and electric field lines, in order to establish the conditions and key elements for estimating the capacitance value. Figure 2. Diagram of the MT sensor, illustrating the concentric plates and electric field lines. The dielectric distance ( d ) and charge distribution are fundamental parameters for calculating the capacitance. For the process to estimate the capacitance value for the MT sensor model, it begins with Equation (1), which corresponds to Gauss’ Law [ 23 ], which defines the mathematical expression that allows calculating the electric field (E), by defining a Gaussian surface radius (r) containing the total charge of the capacitor (q) uniformly distributed over the surface area (A) and electrical permittivity ε(ε=εoεr). EA =q ε=q εoεr(1) Appl. Sci. 2025,15, 1565 6 of 19 where •εo: Electrical permittivity in a vacuum (8.85 ×10−12[ C2/Nm2]) •εr: Relative permittivity of the dielectric between the plates of the capacitor. Solving for the electric field (E) and replacing the area of the circumference ( A=πr2 in expression (1) results in Equation (2): E=q εA=q ε(πr2)(2) Subsequently, we proceed to define the mathematical expression that allows us to calculate the value of the voltage (V) existing between points aand b. The result of this process is described in Equation (3): V=−Za bEdr=−Za b qdr ε(πr2)=−q πεZa b dr r2V=q πε1 a−1 b=q πεb−a ab (3) Finally, the value of the capacitance for the MT sensor ( CMT ) is given by the relationship between the total charge (q) and the voltage (V), as presented in Equation (4): CMT =q V=πεab b−a=πεoεrab b−a(4) 2.4.2. MNT Sensor Design This sensor consists of two “E” shaped plates, a design inspired by that of a resistive humidity sensor. In this case, both the area of the plates ( A=A1=A2 ) and the number of slots interspersed between them are equal, and are separated by a distance (d). Figure 3 presents the proposed diagram for the MNT sensor, whose parameters are defined by the dimensions a, b, c, and d. Furthermore, illustrates how the positive and negative electric charges are distributed on the plates of the capacitive sensor, showing the electric field lines and their similarity to the model of a parallel plate capacitor, characterized by an area (A) and separation (d). Appl. Sci. 2025, 15, x FOR PEER REVIEW 7 of 19 Figure 3. Diagram of the MNT sensor, illustrating two flat plates in the shape of an ‘E’, separated by the dielectric distance (𝑑). The dimensions (𝑎,𝑏,𝑐), the area (𝐴) and charge distribution are key parameters for calculating the capacitance. Figure 4. Capacitance value with prototype sensor traditional model (MT) without residuals for reading. 3. Results 3.1. Comparative Analysis of Theoretical and Experimental Capacitance for MT and MNT Sensors In the experiment, a UNI-T UT603 LCR digital capacitance and inductance meter (UNI-T, Shenzhen, China) with a tolerance of 1% was used to determine the capacitance in pF of each of the proposed sensors, considering the conditions and type of waste material. Figure 4 shows a capacitance value 𝐶 󰆒=21 𝑝𝐹 delivered by the LCR for the implemented prototype of the MT sensor, when it is free of residues, accompanied by the proposed diagram. From Equation (4), we proceed to calculate the capacitance value (𝐶) according to the sensor dimensions that were considered at the discretion of the researchers for its construction (a = 2.1 cm, b = 2.3 cm, c = 3 cm, and d = 0.2 cm). Figure 3. Diagram of the MNT sensor, illustrating two flat plates in the shape of an ‘E’, separated by the dielectric distance ( d ). The dimensions ( a , b , c ), the area ( A ) and charge distribution are key parameters for calculating the capacitance. Appl. Sci. 2025,15, 1565 7 of 19 Therefore, the capacitance value for the MNT capacitive sensor (CMNT) can be calculated from Equation (5): CMNT =εA d=εoεrA d=εoεr[3(a−c)b+ac] d(5) 3. Results 3.1. Comparative Analysis of Theoretical and Experimental Capacitance for MT and MNT Sensors In the experiment, a UNI-T UT603 LCR digital capacitance and inductance meter (UNI-T, Shenzhen, China) with a tolerance of 1% was used to determine the capacitance in pF of each of the proposed sensors, considering the conditions and type of waste material. Figure 4shows a capacitance value C′ MT = 21 pF delivered by the LCR for the implemented prototype of the MT sensor, when it is free of residues, accompanied by the proposed diagram. From Equation (4), we proceed to calculate the capacitance value (CMT) according to the sensor dimensions that were considered at the discretion of the researchers for its construction (a= 2.1 cm, b= 2.3 cm, c= 3 cm, and d= 0.2 cm). CMT =πεoεrab b−a=17.458 pF (6) eabs =C′ MT −CMT =3.542 ×10−12 (7) er=C′ MT −CMT CMT =0.203 (8) Appl. Sci. 2025, 15, x FOR PEER REVIEW 7 of 19 Figure 3. Diagram of the MNT sensor, illustrating two flat plates in the shape of an ‘E’, separated by the dielectric distance (𝑑). The dimensions (𝑎,𝑏,𝑐), the area (𝐴) and charge distribution are key parameters for calculating the capacitance. Figure 4. Capacitance value with prototype sensor traditional model (MT) without residuals for reading. 3. Results 3.1. Comparative Analysis of Theoretical and Experimental Capacitance for MT and MNT Sensors In the experiment, a UNI-T UT603 LCR digital capacitance and inductance meter (UNI-T, Shenzhen, China) with a tolerance of 1% was used to determine the capacitance in pF of each of the proposed sensors, considering the conditions and type of waste material. Figure 4 shows a capacitance value 𝐶 󰆒=21 𝑝𝐹 delivered by the LCR for the implemented prototype of the MT sensor, when it is free of residues, accompanied by the proposed diagram. From Equation (4), we proceed to calculate the capacitance value (𝐶) according to the sensor dimensions that were considered at the discretion of the researchers for its construction (a = 2.1 cm, b = 2.3 cm, c = 3 cm, and d = 0.2 cm). Figure 4. Capacitance value with prototype sensor traditional model (MT) without residuals for reading. Equations (6)–(8) present the result of this process accompanied by the absolute error ( eabs) and the relative error ( er ) [ 24 ] in contrast to the result obtained experimentally, where it can be seen that the theoretical value and the one obtained experimentally are very similar to each other, with reduced error levels taking into account that the capacitance values are of the order of 10−12(pF), an aspect that is very common in commercial capacitors, which establish a tolerance value given by the manufacturer and considering that the relative permittivity values of the materials used for the construction of the sensors may vary a little compared to those that were theoretically adopted. The proposed diagram and the capacitance value of C′ MNT = 28 pF for the implemented MNT sensor prototype, when free of residues, are presented in Figure 5. Using Appl. Sci. 2025,15, 1565 8 of 19 Equation (5), the capacitance value (CMNT) is determined based on the sensor’s dimensions, which were selected by the researchers during its construction (a= 5 cm, b= 0.9 cm, c= 1.5 cm and d= 0.1 cm). CMNT =εoεrA d=24.5 pF (9) eabs =C′ MNT −CMNT =3.49 ×10−12 (10) er=C′ MNT −CMNT CMNT =0.143 (11) Appl. Sci. 2025, 15, x FOR PEER REVIEW 8 of 19 𝐶 =𝜋𝜀𝜀𝑎𝑏 𝑏−𝑎=17.458 𝑝𝐹 (6) 𝑒 =𝐶 󰆒−𝐶 =3.542x10 (7) 𝑒=𝐶 󰆒−𝐶 𝐶 =0.203 (8) Equations (6)–(8) present the result of this process accompanied by the absolute error (𝑒) and the relative error (𝑒 ) [24] in contrast to the result obtained experimentally, where it can be seen that the theoretical value and the one obtained experimentally are very similar to each other, with reduced error levels taking into account that the capacitance values are of the order of 10(𝑝𝐹), an aspect that is very common in commercial capacitors, which establish a tolerance value given by the manufacturer and considering that the relative permittivity values of the materials used for the construction of the sensors may vary a little compared to those that were theoretically adopted. The proposed diagram and the capacitance value of 𝐶 󰆒=28 𝑝𝐹 for the implemented MNT sensor prototype, when free of residues, are presented in Figure 5. Using Equation (5), the capacitance value (𝐶) is determined based on the sensor’s dimensions, which were selected by the researchers during its construction (a = 5 cm, b = 0.9 cm, c = 1.5 cm and d = 0.1 cm). 𝐶 =𝜀𝜀 𝐴 𝑑=24.5 𝑝𝐹 (9) 𝑒 =𝐶 󰆒−𝐶 =3.49×10 (10) 𝑒=𝐶 󰆒−𝐶 𝐶 =0.143 (11) Figure 5. Capacitance value with non-traditional model sensor prototype (MNT) without residuals for reading. The results of this process are presented in Equations (9)–(11), together with the absolute error (𝑒) and the relative error (𝑒) when compared to the results obtained experimentally. It can be observed that the theoretical and experimental values are very similar, as in the previous case, showing low levels of absolute and relative error. From this, it can be concluded that the mathematical expressions proposed for each type of sensor are consistent with the experimental results, which allows establishing a valuable reference point for future research in which it is planned to use any of the proposed models or make modifications regarding their design or dimensions. Figure 5. Capacitance value with non-traditional model sensor prototype (MNT) without residuals for reading. The results of this process are presented in Equations (9)–(11), together with the absolute error ( eabs) and the relative error ( er ) when compared to the results obtained experimentally. It can be observed that the theoretical and experimental values are very similar, as in the previous case, showing low levels of absolute and relative error. From this, it can be concluded that the mathematical expressions proposed for each type of sensor are consistent with the experimental results, which allows establishing a valuable reference point for future research in which it is planned to use any of the proposed models or make modifications regarding their design or dimensions. 3.2. Estimation of the Number of Samples and Analysis of the Information To carry out the sampling campaigns, the estimation of the sample size required for two independent samples was made, since they are two different sensors (MT and MNT), establishing a power of the test ( β=0.9) , a confidence level of 95% ( α=0.05) and an effect size of 0.5 [ 25 ]. By using the XLSTAT tool, a minimum size of 85 was obtained as a result for each sample. However, in order to improve accuracy and ensure statistical representativeness, a final size of 100 samples per sensor (MT and MNT) was established. Additionally, the distribution of the samples was considered uniform for the four types of waste (plastic, glass, metal, and organic), with 25 records per category, establishing an adequate balance in the classification, avoiding biases during the training and testing phase of the machine learning model. Tables 2and 3show the mean, standard deviation, and confidence intervals for each characteristic, according to the type of material or waste used during the sampling campaign depending on the type of sensor. Appl. Sci. 2025,15, 1565 9 of 19 Table 2. Statistical results of capacitance measurements with sensor MT. Material Capacitance [pF] Mean Standard Deviation Confidence Interval (95%) Plastic 24.680 1.519 (24.084, 25.275) Glass 25.080 1.394 (24.538, 25.622) Metal 25.120 1.394 (24.573, 25.666) Organic 251.120 120.823 (203.757, 298.482) Table 3. Statistical results of capacitance measurements with sensor MNT. Material Capacitance [pF] Mean Standard Deviation Confidence Interval (95%) Plastic 34.680 1.574 (34.063, 35.297) Glass 34.880 1.666 (34.227, 35.533) Metal 31.960 0.934 (31.593, 32.326) Organic 680.960 324.396 (553.799, 808.120) Figures 4–7present some of the results obtained during the measurement campaigns, both in the absence of material or waste (Figures 4and 5), and in the presence of reusable waste such as plastic, glass, and metal, as well as in various types of organic waste. Figures 4and 6 show some of the results recorded in terms of capacitance using the MT capacitive sensor, registering a value of 21 pF in the absence of material or residue. In turn, values between 22 and 27 pF for reusable materials and values between 80 and 450 pF when dealing with organic waste. On the other hand, Figures 5and 7present some of the results in terms of capacitance for the MNT capacitive sensor, registering a value of 28 pF in the absence of material or residue. In turn, values between 31 and 37 pF for reusable materials and values between 150 and 1200 pF when dealing with organic waste. Appl. Sci. 2025, 15, x FOR PEER REVIEW 10 of 19 Figure 6. Records with Sensor—Traditional Model (MT) for various types of waste. Figure 7. Records with sensor—non-traditional model (MNT) for various types of waste. Additionally, the possibility was considered that the size of the sample that was established for the development of the project could have affected in some way the appearance of each of the resulting figures. However, from a practical point of view, according to [26], it is possible to consider that a box plot can be reliable as long as the sample size established for each item is equal to or greater than 20. Taking into account that 25 samples were established for each type of residue, it can be considered that this recommendation is fulfilled and with this the result of each figure can be considered reliable. Figure 6. Records with Sensor—Traditional Model (MT) for various types of waste. Appl. Sci. 2025,15, 1565 16 of 19 mental conditions vary considerably. Unlike commercial optical solutions, which can be affected by external factors such as lighting or dust, the MT and MNT sensors maintain consistent and reliable performance. These sensors are an innovative and efficient alternative that combine precision, low cost, and sustainability, making them ideal for real-world applications. Their accessible design enhances their feasibility in communities with limited resources compared to advanced technologies that, although precise, require expensive equipment and specialized maintenance. This accessibility also makes them a valuable tool for waste classification at the source, particularly in residential settings, marking a significant difference in waste management and contributing to environmental protection. Among the limitations identified in the present study is the possibility that the sensitivity of the sensors may be influenced by environmental factors, such as humidity and temperature, which could alter the recorded capacitance values. However, it is reasonable to assume that reusable waste, such as plastics, metals and glass, present significantly lower humidity levels than organic waste, where the humidity concentration could have a more representative impact on the measurements. Additionally, the long-term durability and scalability of sensors made from recycled materials constitute challenges inherent to any emerging technology. Although these aspects and hypotheses were not analyzed in detail in this manuscript, they do not compromise the validity of the results obtained. This is because the main objective of the study was to demonstrate the technical feasibility of low-cost capacitive sensors and their integration with machine learning algorithms for waste classification. Although the capacitance values recorded by the sensors are sufficiently differentiable to classify organic and reusable waste without the need to use machine learning algorithms, the inclusion of a classification algorithm such as Random Forest provides significant benefits to the design of the proposed system. Firstly, it offers robustness against possible fluctuations in capacitance values derived from external factors, such as humidity, variations in temperature, interference from electromagnetic fields generated by nearby devices and other environmental elements. This allows for more accurate classification in real scenarios where operating conditions are often less controlled and data may be affected by noise. In addition, the implementation of machine learning not only increases the scalability of the system, but also allows integration with more complex datasets, incorporating additional features such as temperature and chemical composition of the waste or historical values. This capability increases the accuracy and adaptability of the model, providing a basis for optimizing both sensor design and classification methodologies in future developments. From a practical perspective, the use of Random Forest simplifies system automation by reducing the need for human intervention. The model can dynamically adapt to new operating conditions without requiring manual reconfiguration, which is especially valuable in decentralized applications. For example, in communities with limited resources, having a robust and autonomous system is crucial to ensure reliable and continuous performance. In turn, the inclusion of the algorithm contributes significantly to the quantitative and qualitative analysis of the data, improving the reproducibility and validation of the results both in experimental scenarios and in real applications, reinforcing the reliability of the system in line with current trends in the design of smart, sustainable, and scalable technologies. Finally, these challenges represent clear opportunities for future research, in which controlled tests can be carried out to evaluate the stability of the sensors under variable environmental conditions and to carry out lifespan studies in real application scenarios. In this context, the results presented in this article constitute a fundamental starting point Appl. Sci. 2025,15, 1565 17 of 19 for the advancement of knowledge in this area, providing a solid foundation for future developments that address these limitations with greater depth and rigor. 4. Conclusions Within the broad spectrum of problems related to environmental protection, solid waste management occupies one of the most important places within environmental management policies, where the main objective is to establish mechanisms that allow this process to be carried out efficiently and in compatibility with the environment and public health. For this reason, the use of technologies such as capacitive sensors and Artificial Intelligence could help to implement economic and efficient solutions that allow the classification of solid waste at the source, minimizing the time necessary for its classification compared to separation process manuals, reduce the health risks that may arise when working with contaminated waste and increase the percentage of waste that can be reused and reprocessed later thanks to the proper separation process. According to the results obtained, the results demonstrated that the two models of capacitive sensors proposed describe an adequate behavior in terms of sensitivity and coherence with the readings registered for each type of material or test residue that was used for its evaluation. However, the MNT sensor model was the one that showed optimal performance in relation to sensitivity and variability for each type of waste, with 95% confidence, allowing waste classification processes through the use of machine learning algorithms. Establishing an economic and reliable alternative for the implementation of this type of solutions at the source and in turn promoting the development of future projects related to the classification of materials in a more selective way, could be oriented to the separation of reusable waste such as glass, paper, plastic, and metal, among others independently or for the specific characterization of materials, with possible industrial applications. Author Contributions: J.C.V.F.: Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, supervision, project administration and funding acquisition; H.E.P.W.: validation, investigation, data curation, and supervision; J.A.V.B.: resources, visualization, supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding: The authors appreciate the financial support provided by UNAD (PG0501ECBTI2022) and the company Smarttic during the development of this study. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request. Conflicts of Interest: The authors declare no conflicts of interest. References 1. Pan, Z.; Sun, X.; Huang, Y.; Liang, T.; Lu, J.; Zhang, L.; Qi, C. Anaerobic Co-Digestion of Food Waste and Microalgae at Variable Mixing Ratios: Enhanced Performance, Kinetic Analysis, and Microbial Community Dynamics Investigation. Appl. Sci. 2024, 14, 4387. [CrossRef] 2. Gan, B.; Zhang, C. Research on the algorithm of urban waste classification and recycling based on deep learning technology. In Proceedings of the 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), Chongqing, China, 10–12 July 2020; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2020; pp. 232–236. [CrossRef] 3. Kokoulin, A.N.; Uzhakov, A.A.; Tur, A.I. The Automated Sorting Methods Modernization of Municipal Solid Waste Processing System. In Proceedings of the 2020 International Russian Automation Conference (RusAutoCon), Sochi, Russia, 6–12 September 2020; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2020; pp. 1074–1078. [CrossRef] Appl. Sci. 2025,15, 1565 18 of 19 4. Dadic, B.; Ivankovic, T.; Spelic, K.; Hrenovic, J.; Jurisic, V. Natural Materials as Carriers of Microbial Consortium for Bioaugmentation of Anaerobic Digesters. Appl. Sci. 2024,14, 6883. [CrossRef] 5. Colasante, A.; D’Adamo, I. The circular economy and bioeconomy in the fashion sector: Emergence of a “sustainability bias”. J. Clean Prod. 2021,329, 129774. [CrossRef] 6. Blumberga, D.; Ozarska, A.; Indzere, Z.; Chen, B.; Lauka, D. Energy, Bioeconomy, Climate Changes and Environment Nexus. Environ. Clim. Technol. 2019,23, 370–392. [CrossRef] 7. Saranya, M. A Survey on Health Monitoring System by using IOT. Int. J. Res. Appl. Sci. Eng. Technol. 2018,6, 778–782. [CrossRef] 8. Dhulekar, P.; Gandhe, S.T.; Mahajan, U.P. Development of Bottle Recycling Machine Using Machine Learning Algorithm. In Proceedings of the 2018 International Conference On Advances in Communication and Computing Technology (ICACCT), Sangamner, India, 8–9 February 2018; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2018; pp. 515–519. [CrossRef] 9. Saranya, A.; Bhambri, M.; Ganesan, V. A Cost-Effective Smart E-Bin System for Garbage Management Using Convolutional Neural Network. In Proceedings of the 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), Puducherry, India, 30–31 July 2021; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2021; pp. 1–6. [CrossRef] 10. Lin, W. YOLO-Green: A Real-Time Classification and Object Detection Model Optimized for Waste Management. In Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15–18 December 2021; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2021; pp. 51–57. [CrossRef] 11. Liu, K.; Liu, X. Recycling Material Classification using Convolutional Neural Networks. In Proceedings of the 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Nassau, Bahamas, 12–14 December 2022; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2022; pp. 83–88. [CrossRef] 12. Nafiz, M.S.; Das, S.S.; Morol, M.K.; Al Juabir, A.; Nandi, D. ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning. In Proceedings of the 2023 3rd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 7–8 January 2023; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2023; pp. 181–186. [CrossRef] 13. Ba Alawi, A.E.; Saeed, A.Y.A.; Almashhor, F.; Al-Shathely, R.; Hassan, A.N. Solid Waste Classification Using Deep Learning Techniques. In Proceedings of the 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Taiz, Yemen, 4–5 July 2021; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2021; pp. 1–5. [CrossRef] 14. Wang, B.; Zhou, W.; Shen, S. Garbage classification and environmental monitoring based on internet of things. In Proceedings of the 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 14–16 December 2018; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2018; pp. 1762–1766. [CrossRef] 15. Ravi, S.; Jawahar, T. Smart city solid waste management leveraging semantic based collaboration. In Proceedings of the 2017 International Conference on Computational Intelligence in Data Science(ICCIDS), Chennai, India, 2–3 June 2017; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2018; pp. 1–4. [CrossRef] 16. Rismiyati; Endah, S.N.; Khadijah; Shiddiq, I.N. Xception Architecture Transfer Learning for Garbage Classification. In Proceedings of the 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, 10–11 November 2020; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2020; pp. 1–4. [CrossRef] 17. Vesga Ferreira, J.C.; Sepulveda, F.A.A.; Perez Waltero, H.E. Smart Ecological Points, a Strategy to Face the New Challenges in Solid Waste Management in Colombia. Sustainability 2024,16, 5300. [CrossRef] 18. Vesga, J.C.; Barrera, J.A.; Sierra, J.E. Design of a prototype remote medical monitoring system for measuring blood pressure and glucose measurement. Indian J. Sci. Technol. 2018,11, 1–8. [CrossRef] 19. Harlacher, B.L.; Stewart, R.W. Common mode voltage measurements comparison for CISPR 22 conducted emissions measurements. In Proceedings of the 2001 IEEE EMC International Symposium. Symposium Record. International Symposium on Electromagnetic Compatibility (Cat. No.01CH37161), Montreal, QC, Canada, 13–17 August 2001; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2001; Volume 1, pp. 26–30. [CrossRef] 20. Shyam, G.K.; Manvi, S.S.; Bharti, P. Smart waste management using Internet-of-Things (IoT). In Proceedings of the 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 23–24 February 2017; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2017; pp. 199–203. [CrossRef] 21. Fernandez, G.P.; Acosta, A.M. Migración de Aluminio a los Alimentos Provenientes de Envases y Utensilios de Cocina Nacionales e Importados, Comercializados en Nuestro País. Seminario de Difusión de Resultados del Proyecto de Investigacion. Published Online 2020. Available online: http://repositorio.conacyt.gov.py/handle/20.500.14066/3994 (accessed on 18 August 2023). 22. Cardona, L.; Jiménez, J.; Vanegas, N. Caracterización de materiales empleados en la fabricación de artefactos explosivos improvisados. Rev. Colomb. Mater. 2014, 13–19. [CrossRef] Appl. Sci. 2025,15, 1565 19 of 19 23. Shikha, U.S.; James, R.K.; Pradeep, A.; Baby, S.; Jacob, J. Threshold Voltage Modeling of Negative Capacitance Double Gate TFET. In Proceedings of the 2022 35th International Conference on VLSI Design and 2022 21st International Conference on Embedded Systems (VLSID), Bangalore, India, 26 February–2 March 2022; Institute of Electrical and Electronics Engineers, Inc.: Piscataway, NJ, USA, 2022; pp. 287–291. [CrossRef] 24. González Manteiga, M.T.; Pérez de Vargas, A. Estadística Aplicada una Visión Instrumental; Ediciones Díaz de Santos: Madrid, Spain, 2010; Available online: https://books.google.com.co/books?id=8tocMTUkICkC&printsec=frontcover (accessed on 11 December 2017). 25. Domínguez, J.; Castaño, E. Diseño de Experimentos—Estrategias y Análisis en Ciencias e Ingeniería; Alfaomega: Mexico City, Mexico, 2016. 26. Walpole, R.; Myers, R.; Myers, S. Probabilidad y Estadística para Ingeniería y Ciencias, 9th ed.; Pearson: London, UK, 2012. 27. Rico Martínez, M.; Vesga Ferreira, J.C.; Carroll Vargas, J.; Rodríguez, M.C.; Toro, A.A.; Cuevas Carrero, W.A. Shapley’s Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM. Appl. Sci. 2024,14, 2704. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.