Description:Title of The Invention
DEEP LEARNING-BASED SYSTEM FOR MONITORING OF BIOMASS IN AGRICULTURAL CROP
Field of the Invention
This invention relates to deep learning-based system for monitoring of biomass in agricultural crop
Background of the Invention
CN111075566A: The invention discloses a biomass gas and natural gas coupling power generation device, which comprises a combined cycle power generation device and a biomass gasification device; the combined cycle power generation device comprises a gas compressor, a gas turbine combustion chamber, a gas turbine, a separator, a waste heat boiler, a steam turbine, a condenser and a water pump; the biomass gasification device comprises a biomass charging hopper, a biomass gasification furnace, a cyclone separator and a gas compressor; the invention utilizes the high-temperature flue gas at the outlet of the gas turbine as the gasifying agent for biomass gasification, provides heat for the gasification process, does not need to utilize an external heat source to provide heat for the biomass gasification process, greatly reduces the energy consumption of biomass gasification, and uses the mixed combustion of the gasified biomass gas and natural gas for combined cycle power generation, thereby realizing the high-efficiency utilization of biomass energy and improving the comprehensive utilization efficiency of energy of a natural gas power plant.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Present invention is used some devices: deep learning-based sensor nodes, LoRa (a wireless audio frequency that operates in long ranges.) based gateway, cloud server, and mobile dashboard. Internet is also been actively used in this setup.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention. This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
In this invention is used some devices: deep learning-based sensor nodes, LoRa (a wireless audio frequency that operates in long ranges.) based gateway, cloud server, and mobile dashboard. Internet is also been actively used in this setup. Deep learning-based sensor nodes are installed at certain locations around the field. These sensors are responsible to monitor the temperature, humidity, rainfall, extent of pollution, and other aspects. Afterwards, these sensors send all the information to the LoRa-based gateway using the LoRa frequency. The information is sent to the cloud server through the internet where it is stored. The cloud server then displays the information on the user’s mobile dashboard through API (Application Programming Interface). Moreover, the devices used are a co-processor, computing unit, solar-based power supply, LoRa module, and sensors (temp. sensor, humidity sensor, rainfall sensor, biomass sensor, and deep learning-based sensor). The solar-based power supply is supplying electricity to all the units of the sensor node. The deep learning sensor, biomass sensor, temp. Sensor, rainfall sensor, and humidity sensor send the information stored in them to the computing unit. At last, the computing unit sends data to the LoRa module.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
In fig. 1, have used some devices: deep learning-based sensor nodes, LoRa (a wireless audio frequency that operates in long ranges.) based gateway, cloud server, and mobile dashboard. Internet is also been actively used in this setup.
Deep learning-based sensor nodes are installed at certain locations around the field. These sensors are responsible to monitor the temperature, humidity, rainfall, extent of pollution, and other aspects. Afterwards, these sensors send all the information to the LoRa-based gateway using the LoRa frequency. The information is sent to the cloud server through the internet where it is stored. The cloud server then displays the information on the user’s mobile dashboard through API (Application Programming Interface).
In Fig. 2 the devices used are a co-processor, computing unit, solar-based power supply, LoRa module, and sensors (temp. sensor, humidity sensor, rainfall sensor, biomass sensor, and deep learning-based sensor). The solar-based power supply is supplying electricity to all the units of the sensor node. The deep learning sensor, biomass sensor, temp. Sensor, rainfall sensor, and humidity sensor send the information stored in them to the computing unit. At last, the computing unit sends data to the LoRa module.
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
It should be noted that the description merely illustrates the principles of the present subject matter. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described herein, embody the principles of the present subject matter and are included within its scope.
In this invention is used some devices: deep learning-based sensor nodes, LoRa (a wireless audio frequency that operates in long ranges.) based gateway, cloud server, and mobile dashboard. Internet is also been actively used in this setup.
Deep learning-based sensor nodes are installed at certain locations around the field. These sensors are responsible to monitor the temperature, humidity, rainfall, extent of pollution, and other aspects. Afterwards, these sensors send all the information to the LoRa-based gateway using the LoRa frequency. The information is sent to the cloud server through the internet where it is stored. The cloud server then displays the information on the user’s mobile dashboard through API (Application Programming Interface).
Moreover, the devices used are a co-processor, computing unit, solar-based power supply, LoRa module, and sensors (temp. sensor, humidity sensor, rainfall sensor, biomass sensor, and deep learning-based sensor). The solar-based power supply is supplying electricity to all the units of the sensor node. The deep learning sensor, biomass sensor, temp. Sensor, rainfall sensor, and humidity sensor send the information stored in them to the computing unit. At last, the computing unit sends data to the LoRa module.
ADVANTAGES OF THE INVENTION:
• The node uses a solar-based power supply. Hence, the power supply is a lot cheaper than other sources.
• The information is provided to the user on the mobile display. Hence, it is user-friendly.
• Biomass and natural gas are much cheaper than fossil fuels.
• A sustainable method of energy generation.
, Claims:We Claim:
1. Deep learning-based system for monitoring of biomass in agricultural crop system is comprises with deep learning-based sensor nodes (10&11), LoRa (a wireless audio frequency that operates in long ranges) based gateway (12), cloud server (13), and mobile dashboard (14); Biomass based sensor(23), Solar Based Power Supply (25), Rainfall Sensor (26), Temperature Sensor (27); and wherein Internet is also been actively used in this setup.
2. The system as claimed in claim 1, wherein which is node uses a solar-based power supply.
3. The system as claimed in claim 1, wherein These sensors are responsible to monitor the temperature, humidity, rainfall, extent of pollution, and other aspects. Afterwards, these sensors send all the information to the LoRa-based gateway using the LoRa frequency.
4. The system as claimed in claim 1, wherein the information is sent to the cloud server through the internet where it is stored.
5. The system as claimed in claim 1, wherein the cloud server then displays the information on the user’s mobile dashboard through API (Application Programming Interface).
6. The system as claimed in claim 1, wherein the solar-based power supply is supplying electricity to all the units of the sensor node.
7. The system as claimed in claim 1, wherein the deep learning sensor, biomass sensor, temp. Sensor, rainfall sensor, and humidity sensor send the information stored in them to the computing unit; and at last, the computing unit sends data to the LoRa module.