Description:
BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a system for early disease detection and monitoring productivity and particularly to a system for monitoring productivity of small-scale mango fields.
Description of Related Art
[002] Fruit mango is cultivated for diverse raw products. Global efforts towards sustainable development of fruits such as mango involve adopting practices resilient to challenges. However, mango trees face various fungal diseases affecting leaves, flowers, stems, twigs, and fruit. In scenarios, such, early disease detection is crucial for minimizing economic and environmental losses. Additionally, early disease detection could be implemented by integrating real-time technologies, computer vision, and deep learning leading to enhancement in early detection in mangoes.
[003] CN110473194A discloses a ‘Fruit surface defect detection method based on more image block Threshold Segmentation Algorithms’. The disclosed art is limited to the implementation of different processing methods based on type of the image techniques such as visual light imaging, multi-optical spectrum imaging, high light spectrum image, and laser backscatter. All the processing methods increase complexity and make it hard to implement on a smaller scale.
[004] However, that is the commercial unavailability of systems and methods that can wirelessly communicate disease information, effective strategies, and treatments to users.
[005] There is thus a need for an improved and advanced system for monitoring productivity of small-scale mango fields that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for monitoring productivity of small-scale mango fields. The system comprising: vision nodes integrated with cameras for capturing images of mango trees in real-time. The system further comprising: an edge-based gateway equipped with Internet of Things (IoT) and Long Range (LoRa) connectivity for establishing a communication with the vision nodes. The system further comprising: a processor in communication with the vision nodes through the edge-based gateway. The processor is configured to: receive an image data of the mango trees from the vision nodes through the edge-based gateway; analyze the image data for estimating parameters selected from a count of the mangoes on each of the mango trees, a tree identification, a volume of the mangoes, a density of the mangoes, a growth stage of the mangoes, a color of the mangoes, a shape of the mangoes, a size of the mangoes, or a combination thereof; and detect an anomaly based on the estimated parameters by utilizing a deep learning model with an assistance of a co-processor.
[007] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for monitoring productivity of small-scale mango fields.
[008] Next, embodiments of the present application may provide a system for monitoring productivity of small-scale mango fields that engage Internet of Things (IoT) and Long Range (LoRa) communication to empower communication for the exchange of real-time updates and suggestions to the user.
[009] Next, embodiments of the present application may provide a system for monitoring productivity of small-scale mango fields that use edge-based solutions to enhance the prediction of disease through visuals in an efficient manner.
[0010] Next, embodiments of the present application may provide a system for monitoring productivity that is cost-effective and easy to use.
[0011] Next, embodiments of the present application may provide a system for monitoring productivity of small-scale mango fields that is feasible for implementation on a small scale.
[0012] These and other advantages will be apparent from the present application of the embodiments described herein.
[0013] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0015] FIG. 1A illustrates a system for monitoring productivity of small-scale mango fields, according to an embodiment of the present invention;
[0016] FIG. 1B illustrates vision nodes of the system for monitoring productivity of small-scale mango fields, according to an embodiment of the present invention;
[0017] FIG. 1C illustrates an edge-based gateway of the system for monitoring productivity of small-scale mango fields, according to an embodiment of the present invention;
[0018] FIG. 2 illustrates a block diagram of a processor of the system for monitoring productivity of small-scale mango fields, according to an embodiment of the present invention; and
[0019] FIG. 3 depicts a flowchart of a method for monitoring productivity of small-scale mango fields, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1A illustrates a system 100 for monitoring productivity of small-scale mango fields, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be configured for monitoring the productivity of the small-scale mango fields. The system 100 may employ a deep learning and a Long Range (LoRa) communication with an edge-based networking for real-time and early detection of diseases in mango trees of the small-scale mango fields, in an embodiment of the present invention.
[0025] According to embodiments of the present invention, the system 100 may be installed in locations such as, but not limited to, a farmland, an agricultural land, and so forth. Embodiments of the present invention are intended to include or otherwise cover any location for installation of the system 100, including known, related art, and/or later developed technologies.
[0026] According to embodiments of the present invention, the system 100 may comprise vision nodes 102a-102n (hereinafter referred individually to as the vision node 102, and plurally to as the vision nodes 102), cameras 104a-104n (hereinafter referred individually to as the camera 104 and plurally to as the cameras 104), a dataset 106, a co-processor 108, a solar panel 110, a first computing unit 112, a first display unit 114, a first communication unit 116, an edge-based gateway 118, a second communication unit 120, a second computing unit 122, a second display unit 124, a battery 126, a processor 128, a cloud server 130, a user device 132, and a computer application 134.
[0027] In an embodiment of the present invention, the vision nodes 102 may be installed at multiple locations in the small-scale mango fields. The vision nodes 102 may further be placed in such an arrangement that all the trees in the small-scale mango fields may be under monitoring by any of the vision nodes 102, in an embodiment of the present invention. In an embodiment of the present invention, the vision nodes 102 may be placed at a distance of 2 kilometers (km) apart.
[0028] In an embodiment of the present invention, the vision nodes 102 may be integrated with the cameras 104 for capturing images of the mango trees in the small-scale mango fields in a real-time. In an embodiment of the present invention, the vision nodes 102 may further be explained in conjunction with FIG. 1B.
[0029] In an embodiment of the present invention, the cameras 104 may be adapted for capturing images of the mango trees in real-time. According to other embodiments of the present invention, a resolution for the images of the mango trees using the cameras 104 may be in a range from 320 pixels by 240 pixels to 1920 pixels by 1080 pixels. Embodiments of the present invention are intended to include or otherwise cover any resolution for the images of the mango trees captured by the cameras 104, including known, related art, and/or later developed technologies.
[0030] In an embodiment of the present invention, the cameras 104 may be installed in an integrated fashion with the vision nodes 102. The installation of the cameras 104 may be, such that each of the vision nodes 102 may have a corresponding camera 104, in an embodiment of the present invention.
[0031] According to embodiments of the present invention, the cameras 104 may be, but not limited to, telephoto cameras, wide-angle cameras, color balancer cameras, night vision cameras, and so forth. In a preferred embodiment of the present invention, the cameras 104 may be high-definition (HD) cameras capable of capturing images in 360 degrees (°). Embodiments of the present invention are intended to include or otherwise cover any type of the cameras 104, including known, related art, and/or later developed technologies.
[0032] In an embodiment of the present invention, the edge-based gateway 118 may be equipped with Internet of Things (IoT) and Long Range (LoRa) connectivity for establishing a communication with the vision nodes 102. In an embodiment of the present invention, the edge-based gateway 118 may further be explained in conjunction with FIG. 1C.
[0033] In an embodiment of the present invention, the processor 128 may be in communication with the vision nodes 102 through the edge-based gateway 118. The processor 128 may further be configured to execute computer-executable instructions to generate an output relating to the system 100. According to embodiments of the present invention, the processor 128 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processor 128 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the processor 128 may further be explained in conjunction with FIG. 2.
[0034] In an embodiment of the present invention, the cloud server 130 may be configured to store the captured images of the mango trees and an image data corresponding to the captured images of the mango trees. The image data corresponding to the captured images of the mango trees may be extracted by the first computing unit 112 (As explained in FIG. 1B), in an embodiment of the present invention.
[0035] According to embodiments of the present invention, the cloud server 130 may further be internally populated with a database for example, but not limited to, a distributed database, a personal database, an end-user database, a commercial database, a Structured Query Language (SQL) database, a non-SQL database, an operational database, a relational database, an object-oriented database, a graph database, and so forth. In a preferred embodiment of the present invention, the cloud server 130 may be a cloud database. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud server 130 including known, related art, and/or later developed technologies.
[0036] Further, the cloud server 130 may be stored in the cloud database, in an embodiment of the present invention. In an embodiment of the present invention, the cloud server 130 may be remotely located. In an exemplary embodiment of the present invention, the cloud server 130 may be a public cloud server. In another exemplary embodiment of the present invention, the cloud server 130 may be a private cloud server. In yet another embodiment of the present invention, the cloud server 130 may be a dedicated cloud server. According to embodiments of the present invention, the cloud server 130 may be, but not limited to, a Microsoft Azure cloud server, an Amazon AWS cloud server, a Google Compute Engine (GEC) cloud server, an Amazon Elastic Compute Cloud (EC2) cloud server, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud server 130 including known, related art, and/or later developed technologies.
[0037] In an embodiment of the present invention, the user device 132 may be a device used by a user. The user device 132 may enable the user real-time monitoring of the mango trees and further to view the health status of the mango trees from any remote location, in an embodiment of the present invention.
[0038] The user device 132 may be configured to view parameters estimated by the processor 128, in an embodiment of the present invention. According to embodiments of the present invention, the parameters estimated by the processor 128 may be, but not limited to, a count of the mangoes on each of the mango trees, a tree identification, a volume of the mangoes, a density of the mangoes, a growth stage of the mangoes, a color of the mangoes, a shape of the mangoes, a size of the mangoes, and so forth. Embodiments of the present invention are intended to include or otherwise cover any parameters that may be estimated by the processor 128, including known, related art, and/or later developed technologies.
[0039] The user device 132 may be, but not limited to, a personal computer, a consumer device, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the user device 132 including known, related art, and/or later developed technologies.
[0040] In an embodiment of the present invention, the personal computer may be, but not limited to, a desktop, a server, a laptop, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the personal computer including known, related art, and/or later developed technologies.
[0041] Further, in an embodiment of the present invention, the consumer device may be, but not limited to, a tablet, a mobile phone, a notebook, a netbook, a smartphone, a wearable device, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the consumer device including known, related art, and/or later developed technologies.
[0042] According to an embodiment of the present invention, the user device 132 may comprise software applications such as, but not limited to, a product selling application, a weather application, an appointment application, and the like. In a preferred embodiment of the present invention, the user device 132 may comprise the computer application 134 which may be a computer-readable program installed in the user device 132 for executing functions associated with the system 100. Further, the estimated parameters may be visualized on the computer application 134, in an embodiment of the present invention. In an embodiment of the present invention, the computer application 134 may be installed on the user device 132.
[0043] FIG. 1B illustrates the vision nodes 102 of the system 100, according to an embodiment of the present invention. According to embodiments of the present invention, the vision nodes 102 may comprise the dataset 106, the co-processor 108, the solar panel 110, the first computing unit 112, the first display unit 114, and the first communication unit 116.
[0044] In an embodiment of the present invention, the dataset 106 may be adapted for training the deep learning model. The dataset 106 may comprise images of various mango leaf and fruit diseases corresponding to the mango trees, in an embodiment of the present invention. In an embodiment of the present invention, the deep learning model may be embedded within the vision nodes 102 for anomaly detection based on vision-based data analysis. The anomaly detection based on the vision-based data analysis may be performed with an assistance of the co-processor 108.
[0045] In an embodiment of the present invention, the solar panel 110 may be adapted to harvest electrical energy from solar energy. The harvested electrical energy may further be supplied for the operation of the vision nodes 102, in an embodiment of the present invention. In an embodiment of the present invention, the harvested electrical energy may further be supplied to the co-processor 108, the first computing unit 112, the first display unit 114, and the first communication unit 116.
[0046] In an embodiment of the present invention, the first computing unit 112 may be configured to extract the image data corresponding to the captured images of the mango trees. According to embodiments of the present invention, the first computing unit 112 may be, but not limited to, the Programmable Logic Control (PLC) unit, the microprocessor, the development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the first computing unit 112 including known, related art, and/or later developed technologies.
[0047] In an embodiment of the present invention, the first display unit 114 may be configured to display the captured images of the mango trees. According to embodiments of the present invention, the first display unit 114 may be, but not limited to, a Light Emitting Diode (LED) display, an Organic Light Emitting Diode (OLED) display, and so forth. In a preferred embodiment of the present invention, the first display unit 114 may be a Liquid Crystal Display (LCD). Further, the first display unit 114 may feature a backlight that may be turned on and/or turned off based on a requirement. Embodiments of the present invention are intended to include or otherwise cover any type of the first display unit 114 including known, related art, and/or later developed technologies.
[0048] In an embodiment of the present invention, the first communication unit 116 may be adapted to establish a communicative link between the vision nodes 102 and the second communication unit 120 of the edge-based gateway 118. The first communication unit 116 may receive the image data corresponding to the captured images of the mango trees from the first computing unit 112, in an embodiment of the present invention. Further, the first communication unit 116 may transmit the image data corresponding to the captured images of the mango trees to the second communication unit 120 of the edge-based gateway 118, in an embodiment of the present invention.
[0049] According to embodiments of the present invention, the first communication unit 116 may be, but not limited to, a Wi-Fi communication unit, a Bluetooth communication unit, a millimeter waves communication unit, an Ultra-High Frequency (UHF) communication unit, and so forth. In a preferred embodiment of the present invention, the first communication unit 116 may be a Long Range (LoRa) communication module. Embodiments of the present invention are intended to include or otherwise cover any type of the first communication unit 116, including known, related art, and/or later developed technologies.
[0050] FIG. 1C illustrates the edge-based gateway 118 of the system 100, according to an embodiment of the present invention. According to embodiments of the present invention, the edge-based gateway 118 may comprise the second communication unit 120, the second computing unit 122, the second display unit 124, and the battery 126.
[0051] In an embodiment of the present invention, the second communication unit 120 may be adapted to establish the communicative link between the edge-based gateway 118 and the first communication unit 116 of the vision nodes 102. The second communication unit 120 may receive the image data corresponding to the captured images of the mango trees from the first communication unit 116 of the vision nodes 102, in an embodiment of the present invention. Further, the second communication unit 120 may transmit the received image data corresponding to the images of the mango trees to the processor 128 to the second computing unit 122, in an embodiment of the present invention.
[0052] According to embodiments of the present invention, the second communication unit 120 may be, but not limited to the Bluetooth communication unit, the millimeter waves communication unit, the Ultra-High Frequency (UHF) communication unit, and so forth. In a preferred embodiment of the present invention, the first communication unit 116 may be the Wi-Fi communication unit. Embodiments of the present invention are intended to include or otherwise cover any type of the second communication unit 120, including known, related art, and/or later developed technologies.
[0053] In an embodiment of the present invention, the second computing unit 122 may be connected to the second communication unit 120. The second computing unit 122 may be configured to display the received image data corresponding to the images of the mango trees on the second display unit 124, in an embodiment of the present invention. According to embodiments of the present invention, the second computing unit 122 may be, but not limited to, the Programmable Logic Control (PLC) unit, the microprocessor, the development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the second computing unit 122 including known, related art, and/or later developed technologies.
[0054] – In an embodiment of the present invention, the second display unit 124 may be configured to display the received image data corresponding to the images of the mango trees. According to embodiments of the present invention, the second display unit 124 may be, but not limited to, the Liquid Crystal Display (LCD), the Light Emitting Diode (LED) display, the Organic Light Emitting Diode (OLED) display, and so forth. Further, the second display unit 124 may feature the backlight that may be turned on and/or turned off based on a requirement. Embodiments of the present invention are intended to include or otherwise cover any type of the second display unit 124 including known, related art, and/or later developed technologies.
[0055] In an embodiment of the present invention, the battery 126 may be configured to supply operational power to the edge-based gateway 118. The battery 126 may further supply operational power to the second communication unit 120, the second computing unit 122, and the second display unit 124, in an embodiment of the present invention. In an embodiment of the present invention, the battery 126 may be rechargeable. In another embodiment of the present invention, the battery 126 may be non-rechargeable. According to embodiments of the present invention, the battery 126 for power supply may be of any composition such as, but not limited to, a Nickel-Cadmium battery, a Nickel-Metal Hydride battery, a Zinc-Carbon battery, a Lithium-Ion battery, and so forth. Embodiments of the present invention are intended to include or otherwise cover any composition of the battery 126, including known, related art, and/or later developed technologies.
[0056] FIG. 2 illustrates a block diagram of the processor 128 of the system 100, according to an embodiment of the present invention. The processing may comprise the computer-executable instructions in form of programming modules such as a data receiving module 200, a data analysis module 202, and an anomaly detection module 204.
[0057] In an embodiment of the present invention, the data receiving module 200 may be configured to receive the image data of the mango trees from the vision nodes 102 through the edge-based gateway 118. According to embodiments of the present invention, the image data of the mango trees may be, but not limited to, the captured images, a meta information of the captured image, a processed format of the captured images, and so forth. Embodiments of the present invention are intended to include or otherwise cover any image data of the mango trees, including known, related art, and/or later developed technologies.
[0058] Upon receipt of the image data of the mango trees from the vision nodes 102, the data receiving module 200 may transmit the image data to the data analysis module 202.
[0059] In an embodiment of the present invention, the data analysis module 202 may be activated upon receipt of the image data from the data receiving module 200. The data analysis module 202 may analyze the image data for estimating the parameters, in an embodiment of the present invention. According to embodiments of the present invention, the parameters may be, but not limited to, the count of the mangoes on each of the mango trees, the tree identification, the volume of the mangoes, the density of the mangoes, the growth stage of the mangoes, the color of the mangoes, the shape of the mangoes, the size of the mangoes, and so forth. Embodiments of the present invention are intended to include or otherwise cover any parameters that may be estimated from the analysis of the image data, including known, related art, and/or later developed technologies.
[0060] Further, the data analysis module 202 may be configured to visualize the parameters on the user device 132, through the computer application 134. Upon estimating the parameters, the data analysis module 202 may transmit an activation signal to the anomaly detection module 204.
[0061] In an embodiment of the present invention, the anomaly detection module 204 may be activated upon receipt of the activation signal from the data analysis module 202. The anomaly detection module 204 may be configured to detect an anomaly based on the estimated parameters by utilizing the deep learning model with the assistance of the co-processor 108, in an embodiment of the present invention.
[0062] FIG. 3 depicts a flowchart of a method 300 for monitoring the productivity of the small-scale mango fields using the system 100, according to an embodiment of the present invention.
[0063] At step 302, the system 100 may receive the image data of the mango trees from the vision nodes 102 through the edge-based gateway 118.
[0064] At step 304, the system 100 may analyze the image data for estimating parameters.
[0065] At step 306, the system 100 may detect the anomaly based on the estimated parameters by utilizing the deep learning model with the assistance of the co-processor 108.
[0066] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0067] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for monitoring the productivity of small-scale mango fields, the system (100) comprising:
vision nodes (102a-102n) integrated with cameras (104a-104n) for capturing images of mango trees in real-time;
an edge-based gateway (118) equipped with Internet of Things (IoT) and Long Range (LoRa) connectivity for establishing a communication with the vision nodes (102a-102n); and
a processor (128) in communication with the vision nodes (102a-102n) through the edge-based gateway (118), characterized in that the processor (128) is configured to:
receive an image data of the mango trees from the vision nodes (102a-102n) through the edge-based gateway (118);
analyze the image data for estimating parameters selected from a count of the mangoes on each of the mango trees, a tree identification, a volume of the mangoes, a density of the mangoes, a growth stage of the mangoes, a color of the mangoes, a shape of the mangoes, a size of the mangoes, or a combination thereof; and
detect an anomaly based on the estimated parameters by utilizing a deep learning model with an assistance of a co-processor (108).
2. The system (100) as claimed in claim 1, wherein the processor (128) is configured to visualize the parameters on a user device (132), through a computer application (134).
3. The system (100) as claimed in claim 1, wherein the deep learning model is embedded within the vision nodes (102a-102n) for anomaly detection based on vision-based data analysis.
4. The system (100) as claimed in claim 1, wherein the deep learning model is trained using a dataset (106) of various mango leaf and fruit diseases.
5. The system (100) as claimed in claim 1, wherein the cameras (104a-104n) are a high-definition (HD) camera capable of capturing images in 360 degrees (°).
6. The system (100) as claimed in claim 1, wherein the vision nodes (102a-102n) are powered using a solar panel (110).
7. The system (100) as claimed in claim 1, wherein the vision nodes (102a-102n) comprise a first display unit (114), wherein the first display unit (114) is a colored Liquid Crystal Display (LCD).
8. The system (100) as claimed in claim 1, comprising a cloud server (130) to store the captured images of the mango trees and the image data corresponding to the captured images of the mango trees.
9. The system (100) as claimed in claim 1, wherein the edge-based gateway (118) is powered using a battery (126).
10. The system (100) as claimed in claim 1, wherein the edge-based gateway (118) comprises a display unit.
Date: December 11, 2023
Place: Noida

Dr. Keerti Gupta
Agent for the Applicant
(IN/PA-1529)