This invention relates to a system of Chlorophyll estimation for crop monitoring system using LoRa.
BACKGROUND OF THE INVENTION
USA7746452B2 Methods for determining chlorophyll content comprise providing a sample, subjecting the sample to light at a first wavelength and detecting a first wavelength response, subjecting the sample to light at a second wavelength and detecting a second wavelength response, and calculating a chlorophyll content of the sample based on at least the first wavelength response and the second wavelength response. Optional approaches include detecting the nitrogen content and/or water content of the sample. Associated apparatus for determining chlorophyll content, which may comprise a handheld device, is also disclosed.
Research Gap: In this invention the wireless transmission of the Chlorophyll content is not explored. Here the estimation of chlorophyll has been carried out in a small area or in a selected plant. In this invention the real time chlorophyll is not explored. As well no explanation of the alarming system.
WO2016106215A1 A system for measuring chlorophyll concentration in a leaf sample includes a leaf-holding illuminator device with a main body containing a power source, a plurality of switchable light sources emitting light at different spectra (e.g., red and white light from a broadband light source), and a cap detachably secured to the main body using one or more fastening means. The leaf sample is interposed between the main body and the cap and held in place during imaging. The system includes a mobile electronic device having a camera configured to capture an image of the leaf illuminated by the plurality of switchable light sources, the mobile electronic device having wireless connectivity to a network and an application contained therein configured to transfer the images to a remote sever or computer via the network for data processing. A final chlorophyll index value is calculated based on the transferred images.
Research Gap: Automatic chlorophyll estimation in real time is not explored. Estimation of chlorophyll has been carried out of a single leaf based on selected plant. This device is only for Chlorophyll estimation and long-range transmission of the data is not included.
CNN101556245B belongs to the technical field of photoelectric measurement, relating to a chlorophyll measurement method based on RGB digital signals. The method comprises the steps of: measuring a sample by utilizing a chlorophyll meter with stable white light source LED so as to obtain frequency signals representing light intensity information of transmitted light of red light , green light and blue light; measuring the chlorophyll content of the sample by utilizing a spectrophotometer method; inputting the measurement result into a computer, establishing a multiple linear regression equation of the frequency signals of light intensity information of red light, green light and blue light and the chlorophyll content; configurating parameters to the RGB chlorophyll meter according to the multiple linear regression equation so as to obtain an embedded model for measuring chlorophyll content by the RGB chlorophyll meter; and measuring the leaves of a plant to be tested so as to obtain the frequency signals of light intensity information of red light, green light and blue light, and calculating the chlorophyll content by the embedded model of the chlorophyll meter. The method has the characteristics of simple and stable lighting source, relatively accurate measured chlorophyll content and the like.
Research Gap: Machine learning model is limitedly applied on the RGB and chlorophyll meter. Use of Wireless transmission technology is not incorporated.
SUMMARY OF THE INVENTION
Chlorophyll estimation is a critical parameter for examine the health of the crop. Existing technology to estimate the chlorophyll is based on image processing which is time consuming and costly effort. Real time monitoring of chlorophyll is highly required as it has capability to provide direct input. Moreover, it is required to communicate the current situation the crop in the remote areas to the concern authorities.
Discloses herein a system of Chlorophyll estimation for crop monitoring system using LoRa comprises Field Site comprising Controller unit (10), LoRa Module (11), Camera Module (12), Temperature Sensor (13), Rainfall Sensor (14), Humidity Sensor (15); edge site comprising computing unit (20), pretrained ML model (22), LoRa Module (23), Solar panel based power supply (24); Regional Unit comprising controller unit (30), wifi module (31), LoRa Module (32), Display unit (33), External power supply (34); connected to cloud server (40) with user to access information.
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:
Figure 1. Architecture for edge computing-based Chlorophyll estimation of crop
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.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
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.
In this invention we have proposed a system that is capable of estimating the chlorophyll through vision mode. Figure. 1 illustrates the proposed system architecture, where it consist of three distinct unit that is having different functions. Three unit are field site, edge unit and regional site. Field site is primary unit that monitors the chlorophyll level of crop through camera module. Here the camera module and other sensors are interfaced to the controller unit. Disturbance in imaging from airflow through vision mode will be illuminated by advanced algorithms. The captured fixed image of the crop field will be used further analysis. The chlorophyll level of the crop are communicated to the edge unit for further processing through LoRa module. Edge unit is the unit, where the received camera module data from field site is analysed with pre-trained model that connected to the computing unit. Based on analyzation, the edge unit provides the information regarding degree of degradation of the crop. Moreover, this relevant information is communicated to the regional site through LoRa module. At regional site, the officers are able to estimate the condition of the crop without going to the crop site that are located in remote area. The Wi-Fi module in the regional unit, logs the data on the cloud server through internet and this data can be accessed from anywhere. Field site and edge unit are powered with the solar panel based power supply. The regional unit are powered with external power supply.
Best method of working:
Discloses herein a system of Chlorophyll estimation for crop monitoring system using LoRa comprises Field Site comprising Controller unit (10), LoRa Module (11), Camera Module (12), Temperature Sensor (13), Rainfall Sensor (14), Humidity Sensor (15); edge site comprising computing unit (20), pretrained ML model (22), LoRa Module (23), Solar panel based power supply (24); Regional Unit comprising controller unit (30), wifi module (31), LoRa Module (32), Display unit (33), External power supply (34); connected to cloud server (40) with user to access information.
Field site is primary unit that monitors the chlorophyll level of crop through camera module; in which the camera module and other sensors are interfaced to the controller unit; and the captured fixed image of the crop field is used further analysis.
The chlorophyll level of the crop is communicated to the edge unit for further processing through LoRa module.
Edge unit is the unit, where the received camera module data from field site is analysed with pre-trained model that connected to the computing unit; and based on analyzation, the edge unit provides the information regarding degree of degradation of the crop; this relevant information is communicated to the regional site through LoRa module.
At regional site, the users are able to estimate the condition of the crop without going to the crop site that are located in remote area.
The Wi-Fi module in the regional unit, logs the data on the cloud server through internet and this data can be accessed from anywhere.
Field site and edge unit are powered with the solar panel-based power supply; and the regional unit are powered with external power supply.
ADVANTAGES OF THE INVENTION:
1. Real time crop health monitoring of large area is possible as the required information is fetched to the concerned authorities in time.
2. Edge computing and machine learning enable device is easy to analyse the Realtime chlorophyll estimation and their degradation.
3. Alarming of the damage or degraded crop type will be possible which can trigger for the appropriate planning to workout.
4. As a whole this technique will helpful to save the time and money of the agricultural industries and farmers. As well as this will enhance the productivity.
Novel Features of the Invention:
1. Edge computing and machine learning enable device for real time chlorophyll estimation.
2. Long range technique for crop health monitoring through chlorophyll estimation.
3. Real-time vision enabled system for chlorophyll estimation

We claim:

1. A system of Chlorophyll estimation for crop monitoring system using LoRa comprises Field Site comprising Controller unit (10), LoRa Module (11), Camera Module (12), Temperature Sensor (13), Rainfall Sensor (14), Humidity Sensor (15); edge site comprising computing unit (20), pretrained ML model (22), LoRa Module (23), Solar panel based power supply (24); Regional Unit comprising controller unit (30), wifi module (31), LoRa Module (32), Display unit (33), External power supply (34); connected to cloud server (40) with user to access information.
2. The system as claimed in claim 1, wherein Field site is primary unit that monitors the chlorophyll level of crop through camera module; in which the camera module and other sensors are interfaced to the controller unit; and the captured fixed image of the crop field is used further analysis.
3. The system as claimed in claim 1, wherein the chlorophyll level of the crop are communicated to the edge unit for further processing through LoRa module.
4. The system as claimed in claim 1, wherein edge unit is the unit, where the received camera module data from field site is analysed with pre-trained model that connected to the computing unit; and based on analyzation, the edge unit provides the information regarding degree of degradation of the crop; this relevant information is communicated to the regional site through LoRa module.
5. The system as claimed in claim 1, wherein at regional site, the users are able to estimate the condition of the crop without going to the crop site that are located in remote area.
6. The system as claimed in claim 1, wherein the Wi-Fi module in the regional unit, logs the data on the cloud server through internet and this data can be accessed from anywhere.
7. The system as claimed in claim 1, wherein Field site and edge unit are powered with the solar panel-based power supply; and the regional unit are powered with external power supply.