Description:Title of The Invention
EDGE BASED SYSTEM FOR CROP SUGGESTION USING SENSORS AND DL
Field of the Invention
This invention relates to edge based system for crop suggestion using sensors and dl
Background of the Invention
US10497122B2: Various embodiments describe using a neural network to evaluate image crops in substantially real-time. In an example, a computer system performs unsupervised training of a first neural network based on unannotated image crops, followed by a supervised training of the first neural network based on annotated image crops. Once this first neural network is trained, the computer system inputs image crops generated from images to this trained network and receives composition scores therefrom. The computer system performs supervised training of a second neural network based on the images and the composition scores.
US20020133505A1: A system is provided that comprises a crop database for storing information on the crops that are appropriate for cultivation in terms of cultivation areas and cultivation seasons and a server for providing over the Internet a web site that is associated with the crop database. In response to a user's access the web site, the server transmits an input form to the user so as to allow the user to input the user's crop cultivation area and the cultivation season in the input form. The server retrieves crop information from the crop database based on the information in the filled-in input form transmitted back from the user, and to transmit retrieved crop information to the user. The system may comprise a farm tractor database for storing information on farm tractors that are appropriate for crops to be cultivated as well as information on attachments to be mounted on these farm tractors. The system includes a server for providing over the Internet a web site that is associated with the farm tractor database.
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 device presented in this patent uses a Hybrid Structure using Edge computing with Deep learning and sensors Technology to provide next crop suggestion and soil statistics on a mobile dashboard. The device presented in this patent involve the use of Edge computing and sensors technology. The device presented in this patent does not require the data on the farm tractors. The device presented in this patent uses cloud server and also QR to direct user to mobile dashboard
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.
This invention is comprising with flowchart depicting operational steps of analysis program for use with a computational device (Edge based system). This assists the farmer or user to get crop recommendation and soil condition on a mobile dashboard which is computed through the data collected from the sensors and camera of device. The cloud server interacts with the device while working of device to exchange the necessary information. The cloud then stores the result in the cloud server from which the user can access the result by scanning the QR on a mobile. A block diagram to presents the overview of the working of the connection of the device and the cloud server. The edge-based nodes are connected to different sensor which then involves the internal working of the device which involves the use of 5 sensors – pH sensor, Temperature sensor, Humidity Sensor, Moisture Sensor and NPK sensor. The data from the sensor and camera is directed to a computing unit which involves use of Deep Learning. The LoRa RF is used to connect the device with the cloud server to exchange data between them. The battery and power supply is connected to all sensors, computing unit, LoRa device, Deep learning model and camera.
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: Fig.1 is a flowchart depicting operational steps of analysis program for use with a computational device (Edge based system). This assists the farmer or user to get crop recommendation and soil condition on a mobile dashboard which is computed through the data collected from the sensors and camera of device. The cloud server interacts with the device while working of device to exchange the necessary information. The cloud then stores the result in the cloud server from which the user can access the result by scanning the QR on a mobile. Fig. 2 is a block diagram to presents the overview of the working of the connection of the device and the cloud server. The edge-based nodes are connected to different sensor which then Fig. 3 involves the internal working of the device which involves the use of 5 sensors – pH sensor, Temperature sensor, Humidity Sensor, Moisture Sensor and NPK sensor. The data from the sensor and camera is directed to a computing unit which involves use of Deep Learning. The LoRa RF is used to connect the device with the cloud server to exchange data between them. The battery and power supply is connected to all sensors, computing unit, LoRa device, Deep learning model and camera.
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.
This invention is comprising with flowchart depicting operational steps of analysis program for use with a computational device (Edge based system). This assists the farmer or user to get crop recommendation and soil condition on a mobile dashboard which is computed through the data collected from the sensors and camera of device. The cloud server interacts with the device while working of device to exchange the necessary information. The cloud then stores the result in the cloud server from which the user can access the result by scanning the QR on a mobile. A block diagram to presents the overview of the working of the connection of the device and the cloud server. The edge-based nodes are connected to different sensor which then involves the internal working of the device which involves the use of 5 sensors – pH sensor, Temperature sensor, Humidity Sensor, Moisture Sensor and NPK sensor. The data from the sensor and camera is directed to a computing unit which involves use of Deep Learning. The LoRa RF is used to connect the device with the cloud server to exchange data between them. The battery and power supply is connected to all sensors, computing unit, LoRa device, Deep learning model and camera.
ADVANTAGES OF THE INVENTION:
• This model can be used for small to large scale agriculture.
• The model can be used at any moment as per required by the user irrespective of time and region.
• Optimize crop yield and conserve resources.
• This model will help in providing real time information about which crop to grow.
• It allows access to real time soil moisture data which supports in sustainable land management practices.
• It provides long term usability
, Claims:We Claim:
1. Edge based system for crop suggestion using sensors and dl system is comprises with Edge Based Nodes (10 &11), IoT based gateway (20), Cloud Server (30), QR Code (31), Mobile dashboard (32), Computing Unit (4), pH Sensor (41), Temperature Sensor (42), Humidity Sensor (43), Moisture Sensor (43), Moisture Sensor (44), NPK sensor (45), Camera (46), Deep Learning model (47).
2. The system as claimed in claim 1, wherein systems assists the farmer or user to get crop recommendation and soil condition on a mobile dashboard which is computed through the data collected from the sensors and camera of device.
3. The system as claimed in claim 1, wherein the cloud server interacts with the device while working of device to exchange the necessary information.
4. The system as claimed in claim 1, wherein the cloud then stores the result in the cloud server from which the user can access the result by scanning the QR on a mobile.
5. The system as claimed in claim 1, wherein the edge-based nodes are connected to different sensor which involves the internal working of the device which involves the use of 5 sensors – pH sensor, Temperature sensor, Humidity Sensor, Moisture Sensor and NPK sensor.
6. The system as claimed in claim 1, wherein the data from the sensor and camera is directed to a computing unit which involves use of Deep Learning.
7. The system as claimed in claim 1, wherein the LoRa RF is used to connect the device with the cloud server to exchange data between them; and the battery and power supply is connected to all sensors, computing unit, LoRa device, Deep learning model and camera.
8. The system is claimed in claim 1, wherein cloud server interacts with the device while working of device to exchange the necessary information.
9. The system as claimed in claim 1, wherein the cloud then stores the result in the cloud server from which the user can access the result by scanning the QR on a mobile.