SMART AND SUSTAINABLE PLANT SPECIES ESTIMATOR FOR ORGANIC AND LOCALIZED AIR FILTERING

Abstract

ABSTRACT “SMART AND SUSTAINABLE PLANT SPECIES ESTIMATOR FOR ORGANIC AND LOCALIZED AIR FILTERING” The present invention provides smart and sustainable plant species estimator for organic and localized air filtering. The AeroGlan system integrates IoT-based sensors to monitor air pollution levels in localized regions. The system predicts and recommends plant species capable of absorbing specific pollutants, using machine learning algorithms. These plant species are chosen based on their Air Pollution Tolerance Index (ATPI), ensuring they are suitable for improving local air quality sustainably and cost-effectively. The predictive pipeline, based on Random Forest models, achieves high accuracy in selecting plants most effective for specific pollutants. The system can be scaled from household use to urban planning. Figure 1

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of air filtering using plant species, and more particularly, the present invention relates to the smart and sustainable plant species estimator for organic and localized air filtering.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] As air pollution increases, serious health risks arise, particularly in urban areas, where the quality of air varies significantly across regions. A number of artificial air purification methods exist, but they are expensive, inaccessible, and cannot adapt to the changes in local air pollution. It remains a major problem in urban environments to underutilize plants that filter air naturally. So our invention predicts the most suitable tree available according to the local environment conditions to tackle this problem of air pollution naturally.
[0004] Currently, air purification is addressed through artificial devices such as industrial air purifiers or household appliances, which are costly and involve maintenance. Again, natural air filters can barely be utilized in urban green spaces because a lack of scientific methodologies restrains the selection of appropriate plant species for given pollutants. Also, the proposed local device can be used at a personal level according to the locality.
[0005] In light of the foregoing, there is a need for Smart and sustainable plant species estimator for organic and localized air filtering that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0007] The principal object of the present invention is to overcome the disadvantages of the prior art by providing smart and sustainable plant species estimator for organic and localized air filtering.
[0008] Another object of the present invention is to provide smart and sustainable plant species estimator for organic and localized air filtering that relies on plants, which are generally cheaper, have less maintenance, and are more eco-friendly than their artificial purification counterparts.
[0009] Another object of the present invention is to provide smart and sustainable plant species estimator for organic and localized air filtering that is applied from a single-family house up to a wide metropolitan area.
[0010] Another object of the present invention is to provide smart and sustainable plant species estimator for organic and localized air filtering, wherein the plants clean the air continuously during a very long period in an absolutely eco-friendly process, with no energy consumption or maintenance required.
[0011] Another object of the present invention is to provide smart and sustainable plant species estimator for organic and localized air filtering that incorporates recommendations for plants according to a particular regional pollution level.
[0012] Another object of the present invention is to provide smart and sustainable plant species estimator for organic and localized air filtering, wherein model has achieved a mean precision of 0.95, recall of 0.92, and f-score of 0.94, with reliable and accurate plant selection.
[0013] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0014] The present invention relates to smart and sustainable plant species estimator for organic and localized air filtering.
[0015] The AeroGlan system integrates IoT-based sensors to monitor air pollution levels in localized regions. The system predicts and recommends plant species capable of absorbing specific pollutants, using machine learning algorithms. These plant species are chosen based on their Air Pollution Tolerance Index (ATPI), ensuring they are suitable for improving local air quality sustainably and cost-effectively. The predictive pipeline, based on Random Forest models, achieves high accuracy in selecting plants most effective for specific pollutants. The system can be scaled from household use to urban planning.
[0016] Figure 1 is an attempt to show how pollutants are being mapped to the plants which have the capacity or ability to absorb them. It represents the top down approach how raw data is collected from source which are air pollutants and also how respective plants are selected based on certain criteria and the various stages and filtering process after which data mapping is finally ready to be used in building the Machine Learning model.
[0017] Figure 2 represents the workflow of the model, the raw data is first collected through the IoT device consisting of a range of different gas sensors. Different gases are identified and the concentration of those contributing to the pollution in the surroundings is segregated and then passed for further processing, tabulation, and dataset creation. After all these discussed stages finally, the predicted result can be displayed to the user through a custom designed user interface which can be accessed via mobile devices, tablets as well as PC's and laptops.
[0018] As depicted in Figure.3 the device can be classified into two main hardware based modules: IOT module and computation device. The IOT device is responsible for air sample collection (different pollutant concentration) and the computing device which can be tablet, smartphone, Pc or laptop which through web based or mobile application compute the results and then display it to the users. Here the final results displayed to the user will be the current air quality level of the room, the most suitable plant to be placed in the room based on the readings and the detailed description about the plant with a picture for easy identification of the plant along with its common and scientific name.
[0019] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0020] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0021] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0022] Figure 1. Feature Mapping among AQI, ATPI and Plants Selection.
[0023] Figure 2. Workflow diagram of AeroGlan Model.
[0024] Figure 3. Representative Construct of the Device being proposed.
DETAILED DESCRIPTION OF THE INVENTION
[0025] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0026] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0027] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0028] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0029] The present invention relates to smart and sustainable plant species estimator for organic and localized air filtering. The AeroGlan system integrates IoT-based sensors to monitor air pollution levels in localized regions. The system predicts and recommends plant species capable of absorbing specific pollutants, using machine learning algorithms. These plant species are chosen based on their Air Pollution Tolerance Index (ATPI), ensuring they are suitable for improving local air quality sustainably and cost-effectively. The predictive pipeline, based on Random Forest models, achieves high accuracy in selecting plants most effective for specific pollutants. The system can be scaled from household use to urban planning.
[0030] Figure 1 is an attempt to show how pollutants are being mapped to the plants which have the capacity or ability to absorb them. It represents the top down approach how raw data is collected from source which are air pollutants and also how respective plants are selected based on certain criteria and the various stages and filtering process after which data mapping is finally ready to be used in building the Machine Learning model.
[0031] Figure 2 represents the workflow of the model; the raw data is first collected through the IoT device consisting of a range of different gas sensors. Different gases are identified and the concentration of those contributing to the pollution in the surroundings is segregated and then passed for further processing, tabulation, and dataset creation. After all these discussed stages finally, the predicted result can be displayed to the user through a custom designed user interface which can be accessed via mobile devices, tablets as well as PC's and laptops.
[0032] As depicted in Figure 3 the device can be classified into two main hardware based modules: IOT module and computation device. The IOT device is responsible for air sample collection (different pollutant concentration) and the computing device which can be tablet, smartphone, Pc or laptop which through web based or mobile application compute the results and then display it to the users. Here the final results displayed to the user will be the current air quality level of the room, the most suitable plant to be placed in the room based on the readings and the detailed description about the plant with a picture for easy identification of the plant along with its common and scientific name.
[0033] The advantages of using aeroglan are as follows:
- Cost-Effectiveness: It relies on plants, which are generally cheaper, have less maintenance, and are more eco-friendly than their artificial purification counterparts.
- Scalability: The system may be applied from a single-family house up to a wide metropolitan area.
- Sustainability: The plants clean the air continuously during a very long period in an absolutely eco-friendly process, with no energy consumption or maintenance required.
- Personalization: The system should incorporate recommendations for plants according to a particular regional pollution level.
- Performance: Our system with our model has achieved a mean precision of 0.95, recall of 0.92, and f-score of 0.94, with reliable and accurate plant selection.
[0034] A table comparing AeroGlan with existing air purifiers:
Feature AeroGlan Conventional Air Purifiers
Cost Low High
Maintenance Minimal High
Scalability High (from household to city level) Limited to individual use
Sustainability High Low (energy consumption)
[0035] Immediate applications include urban air quality management, household air purification using indoor plants as well as city planning, and green space management.
[0036] Future Applications can be like partnerships with city designers that would help integrate the model into broader city planning initiatives aimed at improving air quality, increasing green spaces, and promoting sustainability.
[0037] Partnerships can be made with urban planners to design green spaces (parks, streetscapes, and community areas) that are optimized based on AeroGlan's tree species recommendations. Implement green infrastructure projects, such as green belts, urban forests, and tree planting along city streets, with the help of city designers and planning departments.
[0038] Environmental protection agencies or local government offices are responsible for air quality management. Engage with agencies that focus on reducing urban air pollution to deploy the model in pilot projects aimed at assessing the impact of specific tree species on pollution reduction.
[0039] Participate in urban redevelopment initiatives with municipal governments, especially in high-pollution areas. AeroGlan could guide tree-planting efforts in these redevelopment projects to help revitalize neighborhoods while simultaneously improving air quality.
[0040] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims. , Claims:CLAIMS
We Claim:
1) A smart and sustainable plant species estimator for organic and localized air filtering, the system comprises:
- an IoT-based sensor module configured to monitor air pollution levels in a localized region by detecting various pollutants using gas sensors;
- a machine learning-based predictive pipeline that uses data collected by the IoT sensor module to predict and recommend plant species capable of absorbing specific pollutants;
- a Random Forest model that processes the data from the sensors to select plant species based on their Air Pollution Tolerance Index (ATPI) for optimizing air quality improvement.
2) The system as claimed in claim 1, wherein the plant species recommendations are personalized according to the regional pollution level, ensuring that the most effective plant species are selected for the specific air quality conditions of the area.
3) The system as claimed in claim 1, wherein the system further comprising:
- a computation device that receives the data from the IoT sensor module, processes the pollution data, and computes the recommended plant species for air filtration;
- a user interface accessible via mobile devices, tablets, PCs, or laptops, through which the user can view the current air quality level and recommended plant species, including detailed information about the plant and its common and scientific names.
4) The system as claimed in claim 1, wherein the system's predictive pipeline includes a data collection stage, a data processing stage, a tabulation stage, and a dataset creation stage, which collectively enable the generation of accurate plant species recommendations for air pollution reduction.

Documents