Description:FIELD OF THE INVENTION
This invention relates to Monitoring of Radical Length Under Germinator Through Internet of Things.
BACKGROUND OF THE INVENTION
At present time germinators are available and it is used for testing of seeds viability and germination percent under specific temperature and it requires 16 to 35 days during this period monitoring and measurement of radicals are needed. According to agricultural scientist if a radical is emerged 2cm then it is said that it will form a healthy plant.
US11647700B2 One variation of a method for monitoring growth of plants within a facility includes: aggregating global ambient data recorded by a suite of fixed sensors, arranged proximal a grow area within the facility, at a first frequency during a grow period; extracting interim outcomes of a set of plants, occupying a module in the grow area, from module-level images recorded by a mover at a second frequency less than the first frequency while interfacing with the module during the period of time; dispatching the mover to autonomously deliver the module to a transfer station; extracting interim outcomes of the set of plants from plant-level images recorded by the transfer station while sequentially transferring plants out of the module at the conclusion of the grow period; and deriving relationships between ambient conditions, interim outcomes, and final outcomes from a corpus of plant records associated with plants grown in the facility.

RESEARCH GAP: Our system monitors the newly emerging radicals from the seeds.
AU2007254360B2 A detector is supported adjacent the seed cells within a seed meter housing and provides an indication of the presence or absence of seeds in the cells. A processor receives the seed presence indications and provides an operator readout to facilitate adjustments to the seed meter system to maintain a single seed per seed cell at the given seed population. In one embodiment, the detector is a video camera, and the readout includes a slow-motion rendering of the seed cells downstream of a seed singulator In another embodiment, a light source is located on one side of the seed cells, and a light detector is located on the opposite side of the seed cell to determine presence or absence of material in the cell.
RESEARCH GAP: Our system monitors the newly emerging radicals from the seeds.
US10721859B2 An example machinery includes an automated crop management motorized vehicle having an intelligent, modularized image sensor (e.g. camera or video) system that is portable to other crop management vehicles such as a combine, planter or a tillage machine. The image sensor system includes a framework having a bank of procedures for monitoring and control of navigation, spray application, weeding, seeding, machine configuration, in real time as the machines go through a crop field throughout a crop cycle. One example implementation includes electronic circuits, with more than one set mounted on a platform that facilitates moving the setup to other agricultural machines. The framework captures, preserves and corrects the captured images for real time analysis and response, and for spray management to improve crop yield that is correlated with the machine settings and crop management practices.
RESEARCH GAP: Our system monitors the newly emerging radicals from the seeds.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Monitoring of Radical Length Under Germinator Through Internet of Things.
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.
Present invention discloses a Monitoring of Radical Length Under Germinator Through Internet of Things comprises a multiple vision nodes (200) equipped with cameras (320) and volumetric sensors (310); a controlling unit (330) with AI models; a WiFi module (154); a cloud server (152); and a web/mobile app interface (153); wherein the volumetric sensor is used to estimate the length of emerged radicle in petri dishes; and the microcontroller process the image data through computing system.
In another embodiment, the volumetric sensor is calibrated to accurately measure the length of emerged radicles in petri dishes of varying sizes and shapes.
In another embodiment, the microcontroller is configured to process image data using image segmentation algorithms to identify the boundaries of germinating seeds and their corresponding radicles.
In another embodiment, the AI models are trained on a dataset of seed images with annotated radicle lengths and other relevant features, such as seed type, germination conditions, and environmental factors.
In another embodiment, the AI models are capable of estimating radicle length with a high degree of accuracy and precision.
In another embodiment, the web/mobile app interface provides real-time updates on radicle length measurements, allowing users to monitor the germination process in real-time.
In another embodiment, the cloud server stores historical data on radicle length measurements, germination rates, and environmental conditions, enabling users to analyze trends and patterns over time.
In another embodiment, the system is configured to send alerts to users when radicle length measurements fall outside of predefined thresholds, indicating potential issues with the germination process.
In another embodiment, the system is integrated with a temperature and humidity control system to maintain optimal germination conditions.
In another embodiment, the system is capable of operating autonomously without requiring constant human intervention.
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.
Fig. 1 Comprises of vision node, node and germination chamber. This is the overview of our system fitted with germinator. This figure is showing how our system works and there are n number of vision node is fitted to capture radicle germination length precisely. All the measured data can be seen on the dashboard of web or mob app through internet. Because of internet anyone can see the fetched data from anywhere. This system is powered externally. It uses the image processing method and through volumetric sensor to estimate the length of emerged radicle in petri dishes
Fig. 2 Comprises of power source, controlling unit, wi-fi module, Web app, AI Module. This showing that after processing of image data through controlling unit assisted with AI module passes to the cloud server via internet with the help of Wi-Fi to the web or mob app. Then can be seen on dashboard of mob/web app. All the components are powered by external power source. The past data will be recorded and user can see it whenever it requires through internet.
Fig. 3 showing architecture of vision node 1, 2, n, which is comprises of camera, volumetric sensor and power source. This node is responsible for capturing real-time visual data of seed germination under the germination chamber.
Fig. 4 comprises of Vision Node 1, Vision Node 2, Vision Node n, Controlling Unit, AI Model, Wi-Fi Module, Cloud Server, Power Source and Web App. In this architecture we have shown the components that are used. In this system we have used vision nodes 1, 2, n, so that all the seed bed cubes could be covered easily, these vision nodes are powered through external power source same that of germinator power source. These vision nodes are connected to the controlling unit in which is AI Models has introduced to estimate/measure the germination percentage and measure the length of newly emerged radicals from seeds at a particular and specific temperature. And all the system’s data could communicate through Wi-Fi to the cloud server and from cloud server to the web App which could be easily accessed from anywhere in the world.
Fig. 5 is showing the dashboard of mob/web app which is showing data of emerged seed radicals estimated length through vision nodes. This data comprises of date, time and length (mm) and all these information are stored in cloud server.
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: Overview Of System Fitted with Germinator
FIGURE 2 Image Processing Unit
FIGURE 3: System Architecture
FIGURE 4: Architecture of Vision Node 1, 2, N
FIGURE 5: Dashboard of Mob/Web App
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.
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.
Fig. 1 Comprises of vision node, node and germination chamber. This is the overview of our system fitted with germinator. This figure is showing how our system works and there are n number of vision node is fitted to capture radicle germination length precisely. All the measured data can be seen on the dashboard of web or mob app through internet. Because of internet anyone can see the fetched data from anywhere. This system is powered externally. It uses the image processing method and through volumetric sensor to estimate the length of emerged radicle in petri dishes
Fig. 2 Comprises of power source, controlling unit, wi-fi module, Web app, AI Module. This showing that after processing of image data through controlling unit assisted with AI module passes to the cloud server via internet with the help of Wi-Fi to the web or mob app. Then can be seen on dashboard of mob/web app. All the components are powered by external power source. The past data will be recorded and user can see it whenever it requires through internet.
Fig. 3 showing architecture of vision node 1, 2, n, which is comprises of camera, volumetric sensor and power source. this node is responsible for capturing real-time visual data of seed germination under the germination chamber.
Fig. 4 comprises of Vision Node 1, Vision Node 2, Vision Node n, Controlling Unit, AI Model, Wi-Fi Module, Cloud Server, Power Source and Web App. In this architecture we have shown the components that are used. In this system we have used vision nodes 1, 2, n, so that all the seed bed cubes could be covered easily, these vision nodes are powered through external power source same that of germinator power source. These vision nodes are connected to the controlling unit in which is AI Models has introduced to estimate/measure the germination percentage and measure the length of newly emerged radicals from seeds at a particular and specific temperature. And all the system’s data could communicate through Wi-Fi to the cloud server and from cloud server to the web App which could be easily accessed from anywhere in the world.
Fig. 5 is showing the dashboard of mob/web app which is showing data of emerged seed radicals estimated length through vision nodes. This data comprises of date, time and length (mm) and all this information are stored in cloud server.
Present system consists of multiple vision nodes, a germination chamber, and a cloud-based interface. The vision nodes are strategically placed to capture real-time visual data of seed germination within the chamber. The measured data is then transmitted to a web or mobile app via the internet, allowing for remote access and analysis.
The system's components include a power source, a controlling unit, a WiFi module, a web/mobile app, and an AI module. The vision nodes capture image data, which is processed by the controlling unit with the assistance of AI models. The processed data is then transmitted to the cloud server via WiFi and subsequently displayed on the web/mobile app dashboard.
Each vision node is equipped with a camera, a volumetric sensor, and a power source. The camera captures visual data of the seed germination process, while the volumetric sensor provides additional data for more accurate measurements.
The system utilizes multiple vision nodes to cover all seed bed cubes. These nodes are connected to a controlling unit, which houses AI models capable of estimating germination percentage and measuring radicle length. The system's data is transmitted via WiFi to the cloud server and then made accessible through a web app.
The web/mobile app dashboard displays data on the estimated length of emerged seed radicles, including date, time, and length measurements. This information is stored in the cloud server for future reference.
A Monitoring of Radical Length Under Germinator Through Internet of Things comprises a multiple vision nodes (200) equipped with cameras (320) and volumetric sensors (310); a controlling unit (330) with AI models; a WiFi module (154); a cloud server (152); and a web/mobile app interface (153);
wherein the volumetric sensor is used to estimate the length of emerged radicle in petri dishes; and the microcontroller process the image data through computing system.
In another embodiment the volumetric sensor is calibrated to accurately measure the length of emerged radicles in petri dishes of varying sizes and shapes.
In another embodiment the microcontroller is configured to process image data using image segmentation algorithms to identify the boundaries of germinating seeds and their corresponding radicles.
The system as claimed in claim 1, wherein the AI models are trained on a dataset of seed images with annotated radicle lengths and other relevant features, such as seed type, germination conditions, and environmental factors.
In another embodiment the AI models are capable of estimating radicle length with a high degree of accuracy and precision.
In another embodiment the web/mobile app interface provides real-time updates on radicle length measurements, allowing users to monitor the germination process in real-time.
In another embodiment the cloud server stores historical data on radicle length measurements, germination rates, and environmental conditions, enabling users to analyze trends and patterns over time.
In another embodiment the system is configured to send alerts to users when radicle length measurements fall outside of predefined thresholds, indicating potential issues with the germination process.
In another embodiment the system is integrated with a temperature and humidity control system to maintain optimal germination conditions.
In another embodiment the system is capable of operating autonomously without requiring constant human intervention.
ADVANTAGES OF THE INVENTION
1. It will provide monitoring in real time without being visible.
2. No mistakes in the finished product since there was no disruption to the seeds' germination in the germinator.
3. Simple external interruption-free observation of seeds in the germinator whenever needed.
4. Estimation or measuring the percentage of emerged/germinated radicles and their length also from the total number of seeds in the germinator from outside only with the help of computer.
, Claims:1. A Monitoring of Radical Length Under Germinator Through Internet of Things comprises a
multiple vision nodes (200) equipped with cameras (320) and volumetric sensors (310); a controlling unit (330) with AI models; a WiFi module (154); a cloud server (152); and a web/mobile app interface (153);
wherein the volumetric sensor is used to estimate the length of emerged radicle in petri dishes; and the microcontroller process the image data through computing system.
2. The system as claimed in claim 1, wherein the volumetric sensor is calibrated to accurately measure the length of emerged radicles in petri dishes of varying sizes and shapes.
3. The system as claimed in claim 1, wherein the microcontroller is configured to process image data using image segmentation algorithms to identify the boundaries of germinating seeds and their corresponding radicles.
4. The system as claimed in claim 1, wherein the AI models are trained on a dataset of seed images with annotated radicle lengths and other relevant features, such as seed type, germination conditions, and environmental factors.
5. The system as claimed in claim 1, wherein the AI models are capable of estimating radicle length with a high degree of accuracy and precision.
6. The system as claimed in claim 1, wherein the web/mobile app interface provides real-time updates on radicle length measurements, allowing users to monitor the germination process in real-time.
7. The system as claimed in claim 1, wherein the cloud server stores historical data on radicle length measurements, germination rates, and environmental conditions, enabling users to analyze trends and patterns over time.
8. The system as claimed in claim 1, wherein the system is configured to send alerts to users when radicle length measurements fall outside of predefined thresholds, indicating potential issues with the germination process.
9. The system as claimed in claim 1, wherein the system is integrated with a temperature and humidity control system to maintain optimal germination conditions.
10. The system as claimed in claim 1, wherein the system is capable of operating autonomously without requiring constant human intervention.