Description:Brief Description of the Drawings

Generally, the present disclosure relates to air quality monitoring systems. Particularly, the present disclosure relates to an IoT-based smart fungi detection system.
Background
The background description includes information that may be useful in understanding the present invention.It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
The monitoring of air quality within indoor environments is crucial due to the direct impact on human health and well-being. Various state-of-the-art systems have been developed to assess and manage the levels of pollutants such as carbon dioxide, volatile organic compounds, and particulate matter. Monitoring devices integrated with Internet of Things (IoT) technologies have shown potential in providing real-time and precise air quality data.
Furthermore, the use of sensors for detecting specific air quality parameters like temperature, humidity, and levels of carbon dioxide has been well-established. Such sensors are typically configured to collect environmental data which is crucial for determining air quality indices. However, the data collected by these sensors often requires extensive preprocessing to correct errors and ensure accuracy, which can be a cumbersome and error-prone process.
Moreover, the integration of machine learning techniques with sensor data for predictive analytics is becoming a common practice. Machine learning models are trained to identify patterns and predict environmental conditions based on historical data. However, conventional systems frequently encounter challenges in effectively training these models to detect subtle environmental changes, such as those caused by the presence of fungi, which can significantly affect air quality and health.
Feature extraction from preprocessed data also plays a pivotal role in enhancing the effectiveness of machine learning models. Temporal and statistical analysis of the data helps in establishing correlations which are essential for accurate prediction of air quality issues. Nevertheless, the extraction of relevant features and the performance of comprehensive correlation analysis often involve complex computations and can be limited by the capabilities of the processing modules in conventional systems.
Collating the problems identified, it is evident that while existing air quality monitoring systems incorporate advanced sensors and machine learning capabilities, they are often hampered by inefficiencies in data preprocessing, feature extraction, and the overall management of data accuracy and reliability. These issues necessitate frequent manual interventions and can lead to delays in the detection of critical environmental changes such as the presence of fungi.
In light of the above discussion, there exists an urgent need for solutions that overcome the problems associated with conventional systems and/or techniques for detecting fungi presence and managing air quality using IoT technologies.

Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
In an aspect, the present disclosure provides an IoT-based smart fungi detection system designed to monitor and analyze air quality parameters effectively. The system includes a plurality of sensors configured to collect comprehensive data on various air quality metrics such as temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter. These sensors transmit data wirelessly to a central hub. The received data undergo preprocessing at a data processing module that performs operations like cleaning, normalization, smoothing, and interpolation to prepare it for further analysis. A feature extraction module processes the preprocessed data to extract relevant temporal and statistical features and performs correlation analysis on these features. A machine learning model, trained with these features, detects patterns indicative of fungi presence and other air quality issues. An alerting mechanism in the system is responsible for issuing notifications based on the anomalies or issues detected by the machine learning model, ensuring timely responses to potential air quality threats.
In a further aspect, the system incorporates advanced functionalities to enhance detection capabilities and data accuracy. The machine learning model includes an evaluation phase that assesses the detection efficacy using metrics such as accuracy, precision, recall, and F1-score. The user interface aids in the interpretation of the environmental data through graphical representations like charts, heatmaps, and graphs, providing an intuitive visualization of the air quality.
Moreover, each sensor in the system is specifically calibrated to detect its respective air quality parameter within predetermined accuracy ranges, enhancing the system's overall reliability. Additionally, the data processing module features a data augmentation capability that synthesizes additional training data, helping the machine learning model improve detection of rare air quality events. The machine learning techniques employed include a range of advanced algorithms from decision trees and support vector machines to more complex architectures like neural networks and ensemble methods. The feature extraction module also utilizes dimensionality reduction techniques to improve computational efficiency and model performance. Finally, the alerting mechanism is equipped to initiate automatic responses such as activating air filtration or ventilation systems upon detecting significant air quality issues or the presence of fungi.

Field of the Invention

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a block diagram of an IoT-based smart fungi detection system (100), in accordance with the embodiments of the present disclosure.
FIG. 2illustratesa method (200) for detecting fungi, in accordance with the embodiments of the present disclosure.
Fig. 3 depicts a flowchart of an IoT-based smart system designed for detecting fungi.
Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
The term "IoT-based smart fungi detection system" as used throughout the present disclosure relates to an integrated system designed to monitor and analyze air quality to detect the presence of fungi and other air quality issues. The system referred to as the IoT-based smart fungi detection system, comprises a series of interconnected components including a plurality of sensors, a central hub, a data processing module, a feature extraction module, a machine learning model, and an alerting mechanism.
The term "plurality of sensors" as used throughout the present disclosure relates to a group of sensors configured to collect data on air quality parameters such as temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter. The system comprises a plurality of sensors. The plurality of sensors is configured to collect data indicative of various environmental conditions, essential for assessing air quality and detecting potential health hazards such as fungi.
The term "central hub" as used throughout the present disclosure relates to a central hub configured to receive data wirelessly from the plurality of sensors. The system comprises a central hub. The central hub serves as a focal point for data collection, receiving wirelessly transmitted data from the plurality of sensors, which enables real-time monitoring and immediate data retrieval for processing.
The term "data processing module" as used throughout the present disclosure relates to a data processing module operative to preprocess the collected data. The system comprises a data processing module. The data processing module is responsible for the initial handling of data, including cleaning, normalization, smoothing, and interpolation. Such preprocessing prepares the data for more accurate and efficient analysis, enhancing the system’s overall performance in detecting air quality issues.
The term "feature extraction module" as used throughout the present disclosure relates to a feature extraction module that extracts temporal and statistical features from the preprocessed data and performs correlation analysis. The system comprises a feature extraction module. The feature extraction module processes the preprocessed data to extract meaningful patterns and relationships, using statistical and temporal analysis, which are crucial for effective pattern detection by the machine learning model.
The term "machine learning model" as used throughout the present disclosure relates to a machine learning model trained to detect patterns indicative of fungi presence and other air quality issues. The system comprises a machine learning model. The machine learning model utilizes the features engineered by the feature extraction module to identify and learn from patterns that indicate the presence of fungi and other air quality problems, providing a predictive capability that is central to the system’s functionality.
The term "alerting mechanism" as used throughout the present disclosure relates to an alerting mechanism configured to issue notifications based on anomalies or issues detected by the machine learning model. The system comprises an alerting mechanism. The alerting mechanism is designed to issue notifications to users or systems, alerting them to detected anomalies or issues. This functionality is critical for initiating prompt responses to mitigate potential health risks associated with poor air quality or fungi presence.
FIG. 1 illustrates a block diagram of an IoT-based smart fungi detection system (100), in accordance with the embodiments of the present disclosure. Said diagram illustrates the interconnection and arrangement of components within the system (100). A plurality of sensors (102) is configured to collect data indicative of air quality parameters. These parameters include, but are not limited to, temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter. Data indicative of air quality parameters, once collected by said plurality of sensors (102), is transmitted wirelessly to a central hub (104). Upon receipt by the central hub (104), the collected data is forwarded to a data processing module (106). Said module (106) is operative to preprocess the collected data, which includes tasks such as cleaning, normalizing, smoothing, and interpolating the data to ensure its readiness for further analysis. Subsequently, a feature extraction module (108) is tasked with extracting temporal and statistical features from the preprocessed data. In addition, such module (108) performs correlation analysis on the extracted features. Processed information is then utilized by a machine learning model (110). Said model (110) is trained using the features engineered by the feature extraction module (108) to detect patterns that are indicative of fungi presence and other air quality issues. Upon successful pattern detection by the machine learning model (110), an alerting mechanism (112) is engaged. Such mechanism (112) is configured to issue notifications based on the anomalies or issues detected, thereby providing a crucial means for timely intervention in response to air quality threats.
In an embodiment, the machine learning model (110) includes an evaluation phase using accuracy, precision, recall, and F1-score to validate the detection efficacy. The system (100) as disclosed in claim 1 comprises a machine learning model (110) that employs a comprehensive evaluation phase. This phase utilizes key performance metrics such as accuracy, precision, recall, and F1-score to assess and validate the efficacy of fungi detection. These metrics provide a quantitative basis for evaluating the reliability of the detection processes carried out by the model, ensuring that the model performs optimally in real-world scenarios where accurate fungi detection is critical to health and environmental safety.
In another embodiment, the user interface of the system (100) employs graphical representations including charts, heatmaps, and graphs to convey environmental data. The system (100) includes a user interface designed to enhance user interaction and data readability through the use of various graphical representations such as charts, heatmaps, and graphs. This feature enables users to visually interpret complex data sets related to air quality, facilitating easier understanding and quicker decision-making regarding air quality management and response strategies.
In another embodiment, the plurality of sensors (102) includes at least one sensor selected from the group consisting of a temperature sensor, a humidity sensor, a CO2 sensor, a VOC sensor, and a particulate matter sensor, each sensor being calibrated to detect its respective air quality parameter within predetermined accuracy ranges. The system (100) comprises a plurality of sensors (102) that are specifically calibrated to ensure high precision and reliability in measuring specific air quality parameters. Each sensor in the group is fine-tuned to detect changes within a defined accuracy range, enhancing the system's overall sensitivity and responsiveness to environmental changes.
In another embodiment, the data processing module (106) further includes a data augmentation feature that synthesizes additional training data for the machine learning model (110) to improve detection of rare air quality events. The system (100) features a data processing module (106) equipped with a data augmentation capability. This functionality synthesizes additional data points for training the machine learning model (110), particularly enhancing the model's ability to detect and respond to infrequent but critical air quality events, thus broadening the system’s applicability and robustness in diverse environmental conditions.
In another embodiment, the machine learning model (110) is based on at least one of the following techniques: decision trees, support vector machines, neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, or ensemble methods. The machine learning model (110) of the system (100) incorporates a variety of advanced computational techniques including decision trees, support vector machines, and several types of neural networks. This diverse toolkit allows for flexible and robust pattern recognition capabilities, making it highly effective in identifying complex patterns indicative of fungi presence and other air quality issues from multidimensional data sets.
In another embodiment, the feature extraction module (108) utilizes dimensionality reduction techniques to improve computational efficiency and model performance. The system (100) includes a feature extraction module (108) that employs dimensionality reduction techniques. These techniques streamline the analysis by reducing the number of random variables under consideration, thus enhancing both the computational efficiency and the performance of the subsequent machine learning analysis.
In another embodiment, the alerting mechanism (112) is configured to initiate automatic responses, including activating air filtration or ventilation systems, upon detection of air quality issues or fungi presence. The system (100) incorporates an alerting mechanism (112) designed not only to notify users of detected issues but also to initiate automatic corrective actions such as the activation of air filtration or ventilation systems. This proactive feature significantly contributes to maintaining air quality and preventing the adverse effects associated with poor environmental conditions.
FIG. 2illustratesa method (200) for detecting fungi, in accordance with the embodiments of the present disclosure.The method (200) comprises several steps: step (202), collecting data indicative of air quality parameters such as temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter using a plurality of sensors (102); step (204), wirelessly transmitting the collected data to a central hub (104); step (206), preprocessing the collected data at a data processing module (106), where preprocessing includes cleaning, normalizing, smoothing, and interpolating the collected data; in step (208) extracting, at a feature extraction module (108), temporal and statistical features from the preprocessed data and performing correlation analysis on the extracted features; in step (210), training a machine learning model (110) using the extracted features to identify patterns indicative of fungi presence and other air quality issues; step (212) detecting, via the trained machine learning model (110), the patterns indicative of fungi presence and other air quality issues; and step (214) issuing notifications based on the detected patterns using an alerting mechanism (112). This method (200) enables a systematic approach to monitoring and responding to air quality issues, particularly those related to fungi, utilizing advanced data processing and machine learning techniques to ensure accurate and timely detection.
In a further embodiment of the method (200), collecting data further comprises calibrating the sensors (102) to specific sensitivities for each air quality parameter to improve accuracy. This step involves fine-tuning the sensors (102) involved in the data collection process, ensuring that each sensor is specifically calibrated to accurately measure its respective air quality parameter within predetermined sensitivity ranges. This calibration is crucial for enhancing the reliability of the data collected, thereby improving the overall accuracy of the fungi detection method.
Fig. 3 depicts a flowchart of an IoT-based smart system designed for detecting fungi. The process begins with sensors that collect data pertinent to fungi detection and collected data then undergoes a series of processing steps such as transformation of format and cleansed of any irrelevant or erroneous information. The next step involves feature extraction, where data is transformed or extracted to enhance the machine learning model’s ability to discern patterns related to fungi presence. The preprocessed data is stored in a data store repository, from which can be retrieved for the purpose of training and evaluating a machine learning model. The machine learning model categorized the processor data (based on extracted features to determine presence of fungi) and triggered appropriate notification (e.g., SMS, email, voice mail, push notification etc.) indicating.
In an embodiment, the fungi detection system of present disclosure utilize various IoT based array of sensors designed to measure various environmental parameters such as temperature, humidity, air pressure, carbon dioxide levels, volatile organic compounds, and particulate matter. These sensors are strategically positioned indoors to monitor air quality. The temperature and humidity sensors detect climatic conditions, CO2 sensors gauge the presence and levels of carbon dioxide, which reflect occupancy and ventilation status. VOC sensors are tasked with identifying potentially harmful emissions from everyday household items and electrical appliances, whereas PM sensors ascertain the concentration of fine particulate matter that can affect respiratory health. Following collection, the sensor data is wirelessly transmitted to a central hub, which acts as a gateway between the sensors and a cloud-based data repository, allowing for remote access and real-time analysis. The collected data undergoes preprocessing which encompasses data cleaning to rectify errors, normalization to unify data scales, smoothing to eliminate noise, and interpolation to estimate missing or aggregate data points.
In another embodiment, the feature extraction is critical aspect of present disclosure, for feature extraction data attributes are selected, modified, or engineered from raw sensor readings to augment the machine learning system's efficacy in detecting trends and anomalies relevant to indoor air quality and fungi growth. The feature can comprising temporal features that capture the dynamics of air quality over time, statistical features that summarize the central tendencies and dispersions, and performing correlation analysis to discern relationships between various sensor outputs.
In another embodiment, one or more machine learning models can be developed that employs advanced algorithms to learn from the processed data and identify patterns indicative of air quality concerns and potential fungi proliferation. The model can be trained with the prepared dataset to recognize patterns and correlations associated with fungi growth conditions. The model's performance is rigorously evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure it can predict fungi presence reliably. Once the model demonstrates satisfactory performance, same can be deployed for an operational environment where it integrates seamlessly with the existing sensor network and data storage infrastructure to process real-time data. Continuous performance monitoring post-deployment is essential to maintain the model's accuracy and reliability, necessitating regular maintenance and updates. Optionally, the system can also utilize data visualization techniques which transform sensor data into intuitive graphs, charts, and heat maps. Further, system can also trigger notification through emails, text messages, and push notifications about air quality issues and fungi detection, which they can customize according to their preferences.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We Claims

An IoT-based smart fungi detection system (100), comprising:
a plurality of sensors (102) configured to collect data indicative of air quality parameters, including at least temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter;
a central hub (104) configured to receive data wirelessly from said plurality of sensors (102);
a data processing module (106) operative to preprocess the collected data, including cleaning, normalization, smoothing, and interpolation of the data;
a feature extraction module (108) extracts temporal and statistical features from the preprocessed data and perform correlation analysis;
a machine learning model (110) trained using the engineered features to detect patterns indicative of fungi presence and other air quality issues; and
an alerting mechanism (112) configured to issue notifications based on anomalies or issues detected by the machine learning model (110).
The system (100) of claim 1, wherein the machine learning model (110) includes an evaluation phase using accuracy, precision, recall, and F1-score to validate the detection efficacy.
The system (100) of claim 1, wherein the user interface employs graphical representations including charts, heatmaps, and graphs to convey environmental data.
The system (100) of claim 1, wherein the plurality of sensors (102) includes at least one sensor selected from the group consisting of a temperature sensor, a humidity sensor, a CO2 sensor, a VOC sensor, and a particulate matter sensor, each sensor being calibrated to detect its respective air quality parameter within predetermined accuracy ranges.
The system (100) of claim 1, wherein the data processing module (106) further includes a data augmentation feature that synthesizes additional training data for the machine learning model (110) to improve detection of rare air quality events.
The system (100) of claim 1, wherein the machine learning model (110) is based on at least one of the following technique: decision trees, support vector machines, neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, or ensemble methods.
The system (100) of claim 7, wherein the feature extraction module (108) utilizes dimensionality reduction techniques to improve computational efficiency and model performance.
The system (100) of claim 1, wherein the alerting mechanism (112) is configured to initiate automatic responses, including activating air filtration or ventilation systems, upon detection of air quality issues or fungi presence.
A method (200) for detecting fungi, the method (200) comprising:
collecting data indicative of air quality parameters, including at least temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter, using a plurality of sensors (102);
wirelessly transmitting the collected data to a central hub (104);
preprocessing the collected data at a data processing module (106), wherein preprocessing includes cleaning, normalizing, smoothing, and interpolating the collected data;
extracting, at a feature extraction module (108), temporal and statistical features from the preprocessed data and performing correlation analysis on the extracted features;
training a machine learning model (110) using the extracted features to identify patterns indicative of fungi presence and other air quality issues;
detecting, via the trained machine learning model (110), the patterns indicative of fungi presence and other air quality issues; and
issuing notifications based on the detected patterns using an alerting mechanism (112).
The method (200) of claim 9, wherein collecting data further comprises calibrating the sensors (102) to specific sensitivities for each air quality parameter to improve accuracy.

IOT-BASED SMART FUNGI DETECTION SYSTEM

Disclosed is an IoT-based smart fungi detection system comprising a plurality of sensors configured to collect data indicative of air quality parameters including at least temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter; a central hub configured to receive data wirelessly from the plurality of sensors; a data processing module operative to preprocess the collected data including cleaning, normalization, smoothing, and interpolation of the data; a feature extraction module extracts temporal and statistical features from the preprocessed data and performs correlation analysis; a machine learning model trained using the engineered features to detect patterns indicative of fungi presence and other air quality issues; and an alerting mechanism configured to issue notifications based on anomalies or issues detected by the machine learning model.
, Claims:I/We Claims

An IoT-based smart fungi detection system (100), comprising:
a plurality of sensors (102) configured to collect data indicative of air quality parameters, including at least temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter;
a central hub (104) configured to receive data wirelessly from said plurality of sensors (102);
a data processing module (106) operative to preprocess the collected data, including cleaning, normalization, smoothing, and interpolation of the data;
a feature extraction module (108) extracts temporal and statistical features from the preprocessed data and perform correlation analysis;
a machine learning model (110) trained using the engineered features to detect patterns indicative of fungi presence and other air quality issues; and
an alerting mechanism (112) configured to issue notifications based on anomalies or issues detected by the machine learning model (110).
The system (100) of claim 1, wherein the machine learning model (110) includes an evaluation phase using accuracy, precision, recall, and F1-score to validate the detection efficacy.
The system (100) of claim 1, wherein the user interface employs graphical representations including charts, heatmaps, and graphs to convey environmental data.
The system (100) of claim 1, wherein the plurality of sensors (102) includes at least one sensor selected from the group consisting of a temperature sensor, a humidity sensor, a CO2 sensor, a VOC sensor, and a particulate matter sensor, each sensor being calibrated to detect its respective air quality parameter within predetermined accuracy ranges.
The system (100) of claim 1, wherein the data processing module (106) further includes a data augmentation feature that synthesizes additional training data for the machine learning model (110) to improve detection of rare air quality events.
The system (100) of claim 1, wherein the machine learning model (110) is based on at least one of the following technique: decision trees, support vector machines, neural networks, convolutional neural networks, recurrent neural networks, long short-term memory networks, or ensemble methods.
The system (100) of claim 7, wherein the feature extraction module (108) utilizes dimensionality reduction techniques to improve computational efficiency and model performance.
The system (100) of claim 1, wherein the alerting mechanism (112) is configured to initiate automatic responses, including activating air filtration or ventilation systems, upon detection of air quality issues or fungi presence.
A method (200) for detecting fungi, the method (200) comprising:
collecting data indicative of air quality parameters, including at least temperature, humidity, carbon dioxide levels, volatile organic compounds, and particulate matter, using a plurality of sensors (102);
wirelessly transmitting the collected data to a central hub (104);
preprocessing the collected data at a data processing module (106), wherein preprocessing includes cleaning, normalizing, smoothing, and interpolating the collected data;
extracting, at a feature extraction module (108), temporal and statistical features from the preprocessed data and performing correlation analysis on the extracted features;
training a machine learning model (110) using the extracted features to identify patterns indicative of fungi presence and other air quality issues;
detecting, via the trained machine learning model (110), the patterns indicative of fungi presence and other air quality issues; and
issuing notifications based on the detected patterns using an alerting mechanism (112).
The method (200) of claim 9, wherein collecting data further comprises calibrating the sensors (102) to specific sensitivities for each air quality parameter to improve accuracy.

IOT-BASED SMART FUNGI DETECTION SYSTEM