Description:[0001] 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.
[0002] Nano materials are usually considered to be materials with at least one external dimension that measures 100 nano meters or less or with internal structures measuring 100nm or less. They may be in the form of particles, tubes, rods or fibers. Nano technology for the management of crops is used as an essential technology for enhancing crop productivity.
[0003] A number of different types of nanomaterial property analysis systems that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles The emergence and rapid spread of multidrug-resistant bacteria strains are a public health concern. This emergence is caused by the overuse and misuse of antibiotics leading to the evolution of antibiotic-resistant strains. Nanoparticles (NPs) are objects with all three external dimensions in the nanoscale that varies from 1 to 100 nm. Research on NPs with enhanced antimicrobial activity as alternatives to antibiotics has grown due to the increased incidence of nosocomial and community acquired infections caused by pathogens. Machine learning (ML) tools have been used in the field of nano informatics with promising results. As a consequence of evident achievements on a wide range of predictive tasks, ML techniques are attracting significant interest across a variety of stakeholders. In this article, we present an ML tool that successfully predicts the antibacterial capacity of NPs while the model’s validation demonstrates encouraging results (R2 = 0.78). The data were compiled after a literature review of 60 articles and consist of key physio-chemical (p-chem) properties and experimental conditions (exposure variables and bacterial clustering) from in vitro studies. Following data homogenization and pre-processing, we trained various regression algorithms and we validated them using diverse performance metrics. Finally, an important attribute evaluation, which ranks the attributes that are most important in predicting the outcome, was performed. The attribute importance revealed that NP core size, the exposure dose, and the species of bacterium are key variables in predicting the antibacterial effect of NPs. This tool assists various stakeholders and scientists in predicting the antibacterial effects of NPs based on their p-chem properties and diverse exposure settings. This concept also aids the safe-by-design paradigm by incorporating functionality tools.
[0005] Review on Nanoparticles and Nanostructured Materials: Bioimaging, Biosensing, Drug Delivery, Tissue Engineering, Antimicrobial, and Agro-Food Applications In the last few decades, the vast potential of nanomaterials for biomedical and healthcare applications has been extensively investigated. Several case studies demonstrated that nanomaterials can offer solutions to the current challenges of raw materials in the biomedical and healthcare fields. This review describes the different nanoparticles and nanostructured material synthesis approaches and presents some emerging biomedical, healthcare, and Agro-food applications. This review focuses on various nanomaterial types (e.g., spherical, nanorods, nanotubes, nanosheets, nanofibers, core-shell, and mesoporous) that can be synthesized from different raw materials and their emerging applications in bioimaging, biosensing, drug delivery, tissue engineering, antimicrobial, and Agro-foods. Depending on their morphology (e.g., size, aspect ratio, geometry, porosity), nanomaterials can be used as formulation modifiers, moisturizers, nanofillers, additives, membranes, and films. As toxicological assessment depends on sizes and morphologies, stringent regulation is needed from the testing of efficient nanomaterials dosages. The challenges and perspectives for an industrial breakthrough of nanomaterials are related to the optimization of production and processing conditions.
[0006] The significant interest of using nano technology in agriculture includes specific application like nano fertilizers and nano pesticides to trail products and nutrients levels to increase the productivity without decontamination of soils, water and protection against several insect pest and microbial disease. The proposed invention considers the nano materials that are used for agriculture.
[0007] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0008] In the view of the foregoing disadvantages inherent in the known types of agricultural yield improvising systems now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved machine learning based approach to understand the impact of nanomaterials in improvising the agricultural yield that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0009] In the view of the foregoing disadvantages inherent in the known types of agricultural yield improving systems now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved system to study the various nanomaterials in understanding the agricultural yield by using the algorithms of machine learning which has all the advantages of the prior art and none of the disadvantages.
[0010] The Main objective of the proposed invention is to design & implement a framework of machine learning to analyse the characteristic of various nano materials. The invention aims at predicting the impact of nano materials in improving agriculture yield.
[0011] Yet another important aspect of the proposed invention is that the database of nano materials is considered for the study. The proposed invention aims at classifying the nano material’s according to their applications. The predictive unit is used to predict the agricultural yield due to use of nano materials.
[0012] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0013] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BREIF DESCRIPTION OF DRAWINGS
[0014] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of machine learning based approach to analyse the characteristics of various nano materials and their impact in improving agricultural yield, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0015] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and 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 by the appended claims and their equivalents.
[0016] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor 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 detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be 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 "a" mean "at least one” and the word “plurality” means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is 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 any additional subject matter not recited, and is not intended to exclude any 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.
[0017] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of”, "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0018] Nano technology has gained intense attention in the recent years due to its wide applications in several areas like medicine, medical drugs, catalysis, energy and materials. Sustainable agriculture is the development of nano chemical has appeared as promising agents for the plant growth fertilizers and pesticides.
[0019] In recent years the use of nano materials has been considered as an alternative solution to control pest including insects, fungi and weeds. The proposed invention focuses on analyzing the characteristic of various nano materials. The algorithms of machine learning are used to predict the efficiency of nano materials in agricultural
[0020] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0021] Figure 1 illustrates the schematic view of machine learning based approach to analyse the characteristics of various nano materials and their impact in improving agricultural yield 100. The proposed system 100 includes a Database of NM 102 which is analyzed by the machine learning unit 101 for its properties. The classification unit 103 will classify the nano materials 102 based on their applications as 104a, 104b and 104c respectively. The classified nano materials 104a, 104b and 104c are fed to crops 105 for analyzing the impact of nano materials on yield of agricultural lands. The prediction unit 106 will predict the efficiency of nano material in improvising crop yield and results of prediction are stored on display unit 107.
[0022] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. Machine Learning based approach to analyse the Characteristics of Various Nano Materials and their impact in Improving Agricultural Yield comprises of
Machine learning unit;
Display unit;
Predictive unit and
Classification unit.
2. Machine Learning based approach to analyse the Characteristics of Various Nano Materials and their impact in Improving Agricultural Yield, according to claim 1, includes a machine learning unit, wherein the machine learning unit will predict the impact of various nanomaterials in improving the agricultural yield.
3. Machine Learning based approach to analyse the Characteristics of Various Nano Materials and their impact in Improving Agricultural Yield, according to claim 1, includes a display unit, wherein the display unit will display the impact of various nanomaterials against crop yield.
4. Machine Learning based approach to analyse the Characteristics of Various Nano Materials and their impact in Improving Agricultural Yield, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the properties of various nanomaterials.
5. Machine Learning based approach to analyse the Characteristics of Various Nano Materials and their impact in Improving Agricultural Yield, according to claim 1, includes a classification unit, wherein the classification unit will classify the nanomaterials according to their impact on agriculture.