This invention relates to Vision and Edge Enabled System for Sedimentation Level Detection in Fish Ponds.
Background of the Invention
CN104090541B discloses an aquatic product breeding environment monitoring system and a management cloud platform. The monitoring system comprises a monitoring module, a control module and an alarm module. The monitoring module is used for collecting environment parameters of aquatic product breeding. The control module is used for judging whether the environment parameters exceed the set upper limit or the set lower limit of the environment parameters or not, and generating an alarm signal if the environment parameters exceed the set upper limit or the set lower limit of the environment parameters. The alarm module is used for displaying alarm information to aquatic product breeding administrative staff. The breeding environment monitoring system can be used for conducting real-time online monitoring on fresh water breeding environments and seawater breeding environments, collecting important parameters for the aquatic product growth environment in real time, and conducting real-time monitoring through network communication; meanwhile, by means of the aquatic product breeding management cloud platform, technicians and government management staff can conveniently conduct monitoring and technical guidance, and therefore the aims of intensive breeding and accurate and scientific breeding are achieved, and the aquatic product breeding management cloud platform can be widely used for breeding aquatic products such as fish, shrimps, crabs, sea cucumbers, abalones, pearls and shells.
Research Gap: In this invention, environment monitoring is for aquatic product breeding. No sediment level is carried out. Long range transmission of data is lacking. Real-time detection of changes in fish pond is need to be carried.
CN204907563U provides a pair of pond water quality monitoring system, include, microprocessor, camera, controller, quality of water sensor and wireless transceiver module, camera, controller, quality of water sensor and the equal electricity of wireless transceiver module are connected with microprocessor, and the quality of water sensor includes temperature sensor, pH value sensor, turbidity detector, biochemical oxygen demand apparatus and chemical oxygen demand apparatus, and the controller electricity is connected with the oxygenerator. The beneficial effects of the utility model are that: the construction cost is low, can carry out long -range and real time monitoring, and oxygen content in can the automatic balance pond reduces the human cost.
Research Gap: This invention is limited to monitoring of normal water pond. Vision based system is not implemented for sediment detection. Cloud based framework is lacking.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. Discloses a Vision and Edge Enabled System for Sedimentation Level Detection in Fish Ponds comprises vision-based mote (10, 11, 12), local supervising authority (20) and central gateway (30), ML based computing unit (50), LoRa module (51), display unit (52), power supply (53), HD camera module (54), LoRa module (81, 92), Alarm module (94), and ESP 8266 Wi-Fi module (82). The obtained results from the vision-based mote (10, 11, 12) are transmitted to the cloud server through local supervising authority (20) and central gateway (30). A local supervising authority (20) is placed in the proposed architecture for supervising the data receiving from the different vision-based mote (10, 11, 12). The local supervising authority identifies the identity of the specific vision-based mote through the identification number that is allotted to it during programming.
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.
Sediment problems in fish farms may be a total hassle for aquaculture farmers. Fish pond sediment has accumulated due to an increase in fish output in recent years. This sediment builds up over time, reducing the depth of ponds and the amount of living space accessible to fish, as well as causing dissolved oxygen levels to drop. As a result, sediment removal from freshwater ponds is essential for pond upkeep and, as a result, for cost-effective fish production. Other undesirable consequences could arise depending on what's in the accumulating sediment. The release of harmful elements such as hydrogen sulphside and nitrites into the water can drastically impair fish production if the silt is high in these components. If the silt contains significant quantities of organic matter, the higher demand for oxygen from that material may deplete oxygen levels in the water, further lowering fish production. Removing excess silt from fish ponds is one technique to control and maintain not just fish pond volume but also water quality that is most suitable to fish farming. Because fish pond sediment is high in nutrients and organic matter, it could be used as a fertilizer in agricultural production, nursery pot culture, and other similar applications. It does, however, include components that degrade quickly, emitting unpleasant odors and posing a harm to the environment; as a result, it must be controlled and handled in an environmentally sound and sustainable manner. There is a need to monitor the real time sedimentation and management techniques of fish pond sediment which create an existing aquaculture system, with a focus on organic aquaculture, as well as the possibility for nutrient recovery via bioconversion processes to organic fertilisers.
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.
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 Sedimentation level estimation in water pond
Figure 2 Vision based mote
Figure 3 Local supervising authority
Figure 4 Gateway
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.
These and other advantages of the present subject matter would be described in greater detail with reference to the following figures. 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 present invention, a system is proposed that is capable of estimating the sediment levels through vision enabled device. The vision enabled architecture is proposed for real time estimation of sediment level in the fish pond as shown in Figure. 1. Vision-based devices are based on edge computing, and the images received by the camera module are processed and analyzed on the edge itself using pre-trained models for machine learning. Processing and analyzing of the real-time sensor data at the edge assists to overcome the latency and processing time to obtain the sediment levels in the fish pond.
The obtained results from the vision-based mote (10, 11, 12) are transmitted to the cloud server through local supervising authority (20) and central gateway (30). A local supervising authority (20) is placed in the proposed architecture for supervising the data receiving from the different vision-based mote (10, 11, 12). This local supervising authority identifies the identity of the specific vision-based mote through the identification number that is allotted to it during programming. The central gateway (30) empower to communicate the output data of vision-based mote to the cloud server over internet. The user can receive the updates on the mobile application and web application that are connected to the cloud server
Vision based mote (10, 11, 12) assist the user to estimate the sediment level in the fish pond. HD camera module (54) provides the raw image data to the ML based computing unit (50) (Figure.2). This computing unit analyzes the changes in the image data of fish pond and concludes the sediment level. The display unit (52) visualizes the sediment level and status of data transmission. LoRa module (51) act as bi-directional communication for transmitting the data and receiving the request from the user through mobile application/web application (cloud server). A external power supply and solar based power supply are embedded in vision based mote (10, 11, 12) for supplying better power supply to it.
Local supervising authority (20) is used for supervising all the connected vison-based mote (10, 11, 12). LoRa module (92) interfaced to computing unit of local supervising authority enables to receive and transmit the data over long range as shown in figure 3. Even it has storage capability to store the data received from the vision-based mote (10, 11, 12). Alarm module (94) is also connected to generate the alarm in case of heavy sediment level in fish pond. An external power supply (93) is used for powering the unit.

Figure 4 illustrates the gateway where it connects vision-based mote (10, 11, 12) and cloud server. The gateway (30) supports communication on multiple distinct network. LoRa module (81) act as receiver and ESP 8266 Wi-Fi module (82) act as transmitter. The information received through LoRa module is logged on the cloud server through ESP 8266 Wi-Fi module, as cloud server accepts the data in the form of internet protocol (IP) packets. An external power supply (53) is used for powering the unit.

We Claim:

1. A Vision and Edge Enabled System for Sedimentation Level Detection in Fish Ponds comprises vision-based mote (10, 11, 12), local supervising authority (20) and central gateway (30), ML based computing unit (50), LoRa module (51), display unit (52), power supply (53), HD camera module (54), LoRa module (81, 92), Alarm module (94), and ESP 8266 Wi-Fi module (82).
2. The system as claimed in claim 1, wherein the obtained results from the vision-based mote (10, 11, 12) are transmitted to the cloud server through local supervising authority (20) and central gateway (30).
3. The system as claimed in claim 1, wherein a local supervising authority (20) is placed in the proposed architecture for supervising the data receiving from the different vision-based mote (10, 11, 12).
4. The system as claimed in claim 1, wherein the local supervising authority identifies the identity of the specific vision-based mote through the identification number that is allotted to it during programming.
5. The system as claimed in claim 1, wherein the central gateway (30) empower to communicate the output data of vision-based mote to the cloud server over internet; and the user receives the updates on the mobile application and web application that are connected to the cloud server.
6. The system as claimed in claim 1, wherein Vision based mote (10, 11, 12) assist the user to estimate the sediment level in the fish pond; and HD camera module (54) provides the raw image data to the ML based computing unit (50); and the computing unit analyzes the changes in the image data of fish pond and concludes the sediment level; and the display unit (52) visualizes the sediment level and status of data transmission.
7. The system as claimed in claim 1, wherein LoRa module (51) act as bi-directional communication for transmitting the data and receiving the request from the user through mobile application/web application (cloud server).
8. The system as claimed in claim 1, wherein an external power supply and solar based power supply are embedded in vision-based mote (10, 11, 12) for supplying better power supply to it; and Local supervising authority (20) is used for supervising all the connected vison-based mote (10, 11, 12); and LoRa module (92) interfaced to computing unit of local supervising authority enables to receive and transmit the data over long range.
9. The system as claimed in claim 1, wherein alarm module (94) is also connected to generate the alarm in case of heavy sediment level in fish pond; and an external power supply (93) is used for powering the unit.
10. The system as claimed in claim 1, wherein the gateway where it connects vision-based mote (10, 11, 12) and cloud server. The gateway (30) supports communication on multiple distinct network; and LoRa module (81) act as receiver and ESP 8266 Wi-Fi module (82) act as transmitter; wherein the information received through LoRa module is logged on the cloud server through ESP 8266 Wi-Fi module, as cloud server accepts the data in the form of internet protocol (IP) packets. An external power supply (53) is used for powering the unit.