AU2021103499A4 - An automated plantation health monitoring system and a method thereof - Google Patents
An automated plantation health monitoring system and a method thereof Download PDFInfo
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- AU2021103499A4 AU2021103499A4 AU2021103499A AU2021103499A AU2021103499A4 AU 2021103499 A4 AU2021103499 A4 AU 2021103499A4 AU 2021103499 A AU2021103499 A AU 2021103499A AU 2021103499 A AU2021103499 A AU 2021103499A AU 2021103499 A4 AU2021103499 A4 AU 2021103499A4
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- plantation
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- unit
- trap
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000036541 health Effects 0.000 title claims abstract description 17
- 238000012544 monitoring process Methods 0.000 title claims abstract description 13
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 42
- 241001465754 Metazoa Species 0.000 claims abstract description 24
- 238000004891 communication Methods 0.000 claims abstract description 8
- 238000012545 processing Methods 0.000 claims abstract description 7
- 201000010099 disease Diseases 0.000 claims description 18
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 18
- 208000024891 symptom Diseases 0.000 claims description 11
- 230000004044 response Effects 0.000 claims description 2
- 238000011161 development Methods 0.000 claims 1
- 241000196324 Embryophyta Species 0.000 description 9
- 230000006378 damage Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 235000013399 edible fruits Nutrition 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- 240000007124 Brassica oleracea Species 0.000 description 2
- 235000003899 Brassica oleracea var acephala Nutrition 0.000 description 2
- 235000011299 Brassica oleracea var botrytis Nutrition 0.000 description 2
- 240000003259 Brassica oleracea var. botrytis Species 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 208000014674 injury Diseases 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 231100000331 toxic Toxicity 0.000 description 2
- 230000002588 toxic effect Effects 0.000 description 2
- 235000013311 vegetables Nutrition 0.000 description 2
- 241000271566 Aves Species 0.000 description 1
- 235000011331 Brassica Nutrition 0.000 description 1
- 241000219198 Brassica Species 0.000 description 1
- 235000011301 Brassica oleracea var capitata Nutrition 0.000 description 1
- 235000017647 Brassica oleracea var italica Nutrition 0.000 description 1
- 235000001169 Brassica oleracea var oleracea Nutrition 0.000 description 1
- 235000012905 Brassica oleracea var viridis Nutrition 0.000 description 1
- 241000282472 Canis lupus familiaris Species 0.000 description 1
- 241000272201 Columbiformes Species 0.000 description 1
- 241000256259 Noctuidae Species 0.000 description 1
- 241000283973 Oryctolagus cuniculus Species 0.000 description 1
- 241000255969 Pieris brassicae Species 0.000 description 1
- 241000500437 Plutella xylostella Species 0.000 description 1
- 241000700159 Rattus Species 0.000 description 1
- 241000283984 Rodentia Species 0.000 description 1
- 241000255901 Tortricidae Species 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 238000007429 general method Methods 0.000 description 1
- 239000002917 insecticide Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000575 pesticide Substances 0.000 description 1
- 239000003090 pesticide formulation Substances 0.000 description 1
- 230000004962 physiological condition Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004579 scanning voltage microscopy Methods 0.000 description 1
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Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G13/00—Protecting plants
- A01G13/10—Devices for affording protection against animals, birds or other pests
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M1/00—Stationary means for catching or killing insects
- A01M1/02—Stationary means for catching or killing insects with devices or substances, e.g. food, pheronones attracting the insects
- A01M1/04—Attracting insects by using illumination or colours
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30188—Vegetation; Agriculture
Abstract
The present invention discloses an automated plantation health monitoring system, and a method
thereof. The system is a cloud computing based centralized server platform which includes a
plurality of various interfaces, devices, units, and sensors, one or more processors processing the
5 data retrieved from the devices, the units, and the sensors, communicating with each other
remotely. The devices, and the units may include LED trap units, rain proof covers, image
capturing unit, memory, communication, animal or pest capturing, weather prediction. The
sensors may include sensors associated with detecting physiological parameters associated with
the health of the plantation.
10
1/2
Image capturing unit 100
(102)
Memory
(104) Processor Warning Unit
(110) (112)
Trap Lamps (106)
Weather prediction
sensors (106A)
Display Unit
Animal/Pest capturing (114)
unit (106B)
Sensors
(106)
Figure 1
Description
1/2
Image capturing unit 100
(102)
Memory
(104) Processor Warning Unit
(110) (112) Trap Lamps (106)
Weather prediction sensors (106A) Display Unit Animal/Pest capturing (114) unit (106B)
Sensors
(106)
Figure 1
TECHNICAL FIELD The present disclosure generally relates to the field of agriculture. More specifically, the present disclosure relates to an automated plantation health monitoring system and a method thereof.
There is always a gradual global evolution as per the natural law of evolution. Such an evolution brings just not the physical changes in the living organisms, but also genetic changes therein. These genetic changes may be due to natural evolution, life style, surroundings, unexpected trauma, injuries or pandemic. For example, existing crops, and plants are undergoing drastic genetic changes such that their life term and resistance to various diseases and pests have decreased. Major examples of the pests may include such as fruit borers bugs, leaf rollers, etc. Such pests may cause wilting, nibbling on the leaves, dry leaves, etc. The diseases such as blight, crown gall, basal rot, etc. can cause physiological changes in the plants. Such pests and diseases have been increased to a large extent and are highly resistant to pesticide formulations. For example, green caterpillars of large cabbage white butterfly and the small diamond-back cabbage moth damage the leaves of the Brassica family including broccoli, cabbage, kale, cauliflower etc. Cutworms may damage the stem of young seedlings of the plants.
Such pests and diseases can be controlled by very highly toxic and strong pesticides or insecticides. Such toxic formulations when sprayed on the plantation may ingress outer covering of fruits or grains or vegetables or plants, thereby causing further harm to human and animal consumption. In addition, such formulations may release certain chemicals to the environment while fogging, thereby harming the surroundings. Hence, the alternative way may be to control or stop the pests and diseases in the first instance of appearance of any causative symptoms thereof in the plantation, and removing them before such symptoms spreading to the entire plant. However, it may be difficult to watch each and every plant symptoms especially on large agricultural land.
There are many general methods available to detect such pests and diseases depending on size of pests, and type of diseases. One of the traditional methods is naked eye examination which is difficult on large areas. Hence, many automated methods involving various techniques such as Artificial Intelligence, Machine learning, SVM, etc. developed to ease the pest and disease detection of various crops. However, such methods involve training of models to automate the pest detection, which have limited tendency to detect all kinds of pests and diseases. Such methods further may be confined to a particular area. Moreover, such methods detect only one parameter not the other parameters like humidity levels, pH, sunlight as such additional parameters may also play a role in enriching proper growth of the plantation. For example, if a particular crop in a field is getting inadequate sunlight, such inadequacy is not recognized by the conventional automated methods. Hence, such a crop requiring dislocation therefrom may remain devoid of proper attention and adequate sunlight, and hence may die sooner. When large amount of such crops die due to number of factors or may not be enriched, may actually lead to economic losses to farmers and Government. Further, such methods and/or techniques lead to only the detection of pests not capturing thereof.
Therefore in light of the foregoing discussion, there remains a need for developing automated systems and methods which not only detect pests and diseases but also monitor overall health of the plantation and capturing the pests.
SUMMARY In one aspect of the present invention, an automated plantation health monitoring system is disclosed. The system includes a plurality of plantation; a non volatile memory having a repository of physiological parameters associated with the plantation. An image capturing unit is configured to capture images of the plantation. A plurality of trap lamps is equipped with rainproof covers, and animal pests capturing unit defined below the trap lamps. At least one microprocessor is configured to process the data retrieved from the image capturing unit, the trap lamps, the server for comparison with that of the data stored in the memory. A warning unit is configured to generate signals for a first symptom of pests and diseases appearing in the plantation, and the animal pests captured in the trap lamps. A cloud computing centralized server includes one or more microprocessors located remotely in an area of the plantation and the trap lamps. The system also includes a display unit for displaying the updates of the health of the plantation. In another aspect of the present invention, an automatic method for monitoring health of the plantation is disclosed. The method includes receiving information belonging to a particular plantation, data of number of animal pests captured in animals capturing unit, and weather prediction information from sensors disposed on rain proof covers disposed on LED trap covers. The method further involves processing the retrieved information and the data for further comparison with that of the information stored in the memory. The method finally includes generating warning signals for a first symptom of pests and diseases appearing in the plantation, and the animal pests captured in the trap lamps, followed by display on a display unit.
BRIEF DESCRIPTION OF THE DRAWINGS Other objects, features, and advantages of the embodiment will be apparent from the following description when read with reference to the accompanying drawings. In the drawings, wherein like reference numerals denote corresponding parts throughout the several views. Preferred embodiments of the present invention are herein further described, by way of non limiting example only, with reference to the accompanying drawings, in which: Figure 1 illustrates a schematic view of an automated plantation health monitoring system, in accordance with an illustrative embodiment of a present invention; and Figure 2 illustrates a flowchart depicting an automated plantation health monitoring system, in accordance with another illustrative embodiment of the present invention.
MODES FOR CARRYING OUT THE PREFERRED EMBODIMENTS The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein. Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" and "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. As used herein, the singular forms "a", "an", "the" include plural referents unless the context clearly dictates otherwise. Further, the terms "like", "as such", "for example", "including" are meant to introduce examples which further clarify more general subject matter, and should be contemplated for the persons skilled in the art to understand the subject matter. Although this invention has been described in conjunction with the exemplary embodiments' below, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art. Accordingly, the exemplary embodiments of the invention as set forth above are intended to be illustrative and not limiting. Various changes may be made without departing from the spirit and scope of the invention. The present invention discloses an automated plantation health monitoring system which involves communication among a plurality of interfaces, units, devices, sensors associated with diseases, pests, and symptoms thereof arising in the plantation, animal pests damaging the plantation, and weather affecting the plantation. As shown in Figure 1, n automated plantation health monitoring system (100) is disclosed. The system includes one or more devices, units, interfaces, sensors communicating with each other in a cloud computing centralized server platform. The system includes a plurality of plantation. Such plantation may be grown on any size of a land ranging from a small quarry to a medium sized to large sized agricultural land. The plantation may include such but is not limited to crops, grains, plants, fruits, vegetables, non edible crops, and so on. The system (100) includes an image capturing unit (102) for capturing images of the plantation. The image capturing unit (102) may include such as but are not limited to a camera configured to capture images in all the dimensions such that the images are captured from all the directs of the plantation. The camera may also be configured to rotate. The camera may also be configured to capture streaming and non-streaming video of the plantation. The camera may also be equipped with gyrosensors. The system (100) includes a non volatile memory (104) having a repository of physiological parameters associated with the plantation. The physiological parameters may be associated with health parameters of the plantation. The parameters may includes such as but are not limited to color intensity, appearance of any spots, shape, active, inactive, drooping, height, pH, adequate sunlight, moisture, appearance of any pests. The memory (104) is a complete repository of all the parameters associated with all the plants grown in the particular plantation. The memory (104) may be expandable to include information for more plants.
As shown in Figure 1, the system (100) includes a plurality of trap lamps (106). The lamps (106) may be disposed at particular inches from each other as per the requirement and size of the land. The lamps (106) may be equipped with rainproof covers. Such covers may be defined as canopy having weather prediction sensor (106A). Such sensors (106A) may predict weather and likelihood of rain and hail. The sensors (106A) may be configured to actuate the canopy as there is a likelihood of rain and hail. Hence, the weather prediction sensors are in communication with the rainproof covers to actuate in response to prediction of rain, and hail.
The lamps (106) may further be configured to illuminate the land during the night time. Further, the lamps (106) may have animal/pests capturing unit (106B) disposed underneath thereof. The animal/pests capturing unit (106B) may be configured to capture any rodents like rats, mouse, etc., and animals like dogs, rabbits or birds like pigeons etc. in case such animal pests come in close proximity to the plantation. The system (100) also includes a plurality of sensors (108) in the plantation which may include such as but are not limited to sense various parameters selected from one or more or combinations of pH, water/moisture, humidity in air, weather, etc.
In some embodiments, the system (100) includes at least one microprocessor (110) processing the data retrieved from various interfaces, sensors, devices, units and so on. The data can be retrieved from the image capturing unit (102), the trap lamps (106), and sensors (108). The data represents live physiological conditions of the environment, and the plantation. The data retrieved by the cloud computing based server undergoes comparison with that of the data stored in the memory (104).
The system (100) also includes a warning unit (112) at the server location. The warning unit (112) is configured to generate signals for a first symptom of pests and diseases appearing in the plantation. The warning unit (112) also indicates animal pests captured in the trap lamps, and keep on generating such signals until removal of such captured animals or pests from the plantation. The warning signals can be in the form of a text message or alarm or voice message, etc. The system (100) includes a cloud computing centralized server including one or more microprocessors located remotely in an area of the plantation and the trap lamps. All such interfaces, devices, units, and sensors communicate with each other on the centralized cloud computing platform via a communication unit. The communication unit may involve a wireless network through a plurality of medium such as Bluetooth, Wi-fi, Zigbee, 4G, 5G, and so on. The warning signals can be displayed on the display unit (114). The display unit (114) may include such as mobile phone, smart phone, Pager, laptop, monitor etc. The display unit (114) may be configured to receive the updates from the warning unit (112) periodically or instantly as per requirements of a user.
As shown in Figure 2, the present invention discloses an automatic method (200) for monitoring health of the plantation. The method (200) includes a number of steps; sequence thereof may be exemplary in nature to understand the present invention. The method (200) includes receiving information belonging to a particular plantation at step 202. The method (200) includes receiving data of number of animal pests captured in animals capturing unit at step 204. The method (200) includes receiving weather prediction information from sensors disposed on rain proof covers disposed on LED trap covers at step 206, followed by processing the retrieved information and the data for further comparison with that of the information stored in the memory at step 208. The method (200) includes generating warning signals for a first symptom of pests and diseases appearing in the plantation, and the animal pests captured in the trap lamps at step 210.
Many modifications will be apparent to those skilled in the art without departing from the scope of the present invention as hereinbefore described with reference to the accompanying drawings.
The reference in this specification to any prior art publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgement or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of Endeavour to which this specification relates. The foregoing descriptions of exemplary embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to best explain the principles of the disclosure and its practical application, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions, substitutions of equivalents are contemplated as circumstance may suggest or render expedient but is intended to cover the application or implementation without departing from the spirit or scope of the claims of the present disclosure.
Claims (9)
1. An automated plantation health monitoring system comprising: a plurality of plantation; a non volatile memory having a repository of physiological parameters associated with the plantation; an image capturing unit for capturing images of the plantation; a plurality of trap lamps equipped with rainproof covers, and animal pests capturing unit defined below the trap lamps; at least one microprocessor processing the data retrieved from the image capturing unit, the trap lamps, the server for comparison with that of the data stored in the memory; a warning unit generating signals for a first symptom of pests and diseases appearing in the plantation, and the animal pests captured in the trap lamps; a cloud computing centralized server including one or more microprocessors located remotely in an area of the plantation and the trap lamps; and a display unit.
2. The system of claim 1, wherein the system further comprising a weather prediction sensors in communication with the rainproof covers to actuate in response to prediction of rain, and hail.
3. The system of claim 1, wherein the system further comprising a plurality of sensors to sense physiological changes instantly and reporting to the warning system.
4. The system of claim 1, wherein instant live updates display on the display system.
5. The system of claim 1, wherein the system comprising a communication unit to enable communication between a plurality of interfaces associated with a plurality of units, sensors, devices of the system.
6. An automatic method for monitoring health of the plantation, the method comprising: receiving information belonging to a particular plantation; receiving data of number of animal pests captured in animals capturing unit; receiving weather prediction information from sensors disposed on rain proof covers disposed on LED trap covers; processing the retrieved information and the data for further comparison with that of the information stored in the memory; and generating warning signals for a first symptom of pests and diseases appearing in the plantation, and the animal pests captured in the trap lamps.
7. The method of claim 6, wherein information includes physiological parameters selected from one or more of appearance, shape, development of any symptoms associated with pests and diseases on leaf, root, stem, and overall plant.
8. The method of claim 6, wherein the live updates display on a display unit.
9. The method of claim 6, wherein a plurality of servers, devices, and units communicating with each other through a cloud computing centralized server.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021103499A AU2021103499A4 (en) | 2021-06-21 | 2021-06-21 | An automated plantation health monitoring system and a method thereof |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021103499A AU2021103499A4 (en) | 2021-06-21 | 2021-06-21 | An automated plantation health monitoring system and a method thereof |
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| AU2021103499A4 true AU2021103499A4 (en) | 2021-08-19 |
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| AU2021103499A Ceased AU2021103499A4 (en) | 2021-06-21 | 2021-06-21 | An automated plantation health monitoring system and a method thereof |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114698596A (en) * | 2021-12-27 | 2022-07-05 | 山东农业工程学院 | Escape-preventing method for locusta migratoria culture |
-
2021
- 2021-06-21 AU AU2021103499A patent/AU2021103499A4/en not_active Ceased
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114698596A (en) * | 2021-12-27 | 2022-07-05 | 山东农业工程学院 | Escape-preventing method for locusta migratoria culture |
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Owner name: SINGH, P.K. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: SINGH, R.S. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: SOMASHEKAR, D. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: SINGH, S. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: GAUR, P. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: SINGH, A. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: PALIWAL, S. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: SINGH, V. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT Owner name: SHARMA, S. Free format text: FORMER NAME(S): SINGH, PANKAJ; SINGH, RAVI; SOMASHEKAR, D.R.; SINGH, SANJEEV; GAUR, PRERNA; SINGH, ABHISHEK; PALIWAL, SANJAY; SINGH, VIVEK; SHARMA, SHAASWAT |
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