AU2021101375A4 - IoT Based Agri Robotic Hands for Plucking Under Ground Crops & Automatic Spraying - Google Patents

Abstract

The invention is an automated fruit harvester which includes cameras, sensors and robotic arms which position themselves adjacent to a fruit to be harvested. Prof. Ramesh Chandra Panda Dr. Sailesh Iyer Dr. Manish Sakhlecha Dr. Ratnesh Tiwari Dr. Taranjeet Sachdev Mr. Mayank Sharma Parv Nayak Prashant Singh Smita Jaiswal Dr. Love Kumar Dr. Sunil S. Harakannanavar K. Aravindhan TOTAL NO OF SHEET: 03 NO OF FIG:07 FIG.1: Water container FIG.2: Brushless Motor Controller DC 12-36V 500W PWM Driver Board.

Description

Prof. Ramesh Chandra Panda Dr. Sailesh Iyer Dr. Manish Sakhlecha Dr. Ratnesh Tiwari Dr. Taranjeet Sachdev Mr. Mayank Sharma Parv Nayak Prashant Singh Smita Jaiswal Dr. Love Kumar Dr. Sunil S. Harakannanavar K. Aravindhan

TOTAL NO OF SHEET: 03 NO OF FIG:07

FIG.1: Water container

FIG.2: Brushless Motor Controller DC 12-36V 500W PWM Driver Board.

Australian Government IP Australia Innovation Patent Australia Patent Title: IoT Based Agri Robotic Hands for Plucking Under Ground Crops

& Automatic Spraying

Name and address of patentees(s): Jolly Masih Head Techno Managerial Research, Prestige Institute of, Engineering Management and Research, Indore Madhya Pradesh India. K. Aravindhan Assistant Professor, Department of Computer Science and Engineering, SNS College of Engineering, Coimbatore-641107 (Under Anna University) Prof. Ramesh Chandra Panda Dean, Research & Development Cell, Synergy Institute of Engineering & Technology Dhenkanal, Odisha,759001. Dr. Sailesh Iyer Professor, CSE, Rai University,Ahmedabad, India. Dr. Manish Sakhlecha ICFAI University, Kamal Ghat, Agartala, Tripura, India 799210.

Dr. Ratnesh Tiwari Associate prof. and HOD, Dept. of Physics, Bhilai Institute of technology Raipur 493661 India. Dr. Taranjeet Sachdev Shri Shankaracharya Institute of professional management and Technology, Raipur, C.G., India. Mr. Mayank Sharma Shri Shankaracharya Institute of professional management and technology, Raipur, C.G., India.

Parv Nayak Ph.D. Research Scholar (Processing and Food Engineering) Department of Agricultural Processing and Food Engineering, College of Agricultural Engineering and Technology, Odisha University of Agriculture and Technology, Bhubaneshwar, 751003, India. Prashant Singh Ph. D Research Scholar, Division of agricultural engineering (soil and water conservation), Indian council of agricultural research - Indian agriicultural research institute, New Delhi, Pincode - 110012, India. Smita Jaiswal Ph. D Research Scholar, discipline of water science and Technology, ICAR- Indian agricultural research institute, New Delhi , Pincode- 110012,India. Dr. Love Kumar Junior Research Fellow Water Technology Centre, ICAR - Indian agriicultural research institute, New Delhi, Pincode 110012, India. Dr. Sunil S. Harakannanavar S/o S. F. Harakannanavar, Post: Devagiri, TQ & Dist: Haveri -581110,Karnataka, India. Complete Specification: Australian Government.

FIELD OF THE INVENTION Our Invention is related to a IoT based Agri robotic hands for plucking underground crops & automatic spraying

BACKGROUND OF THE INVENTION

Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. Modern agriculture is involved in informatics, sensors, and navigation technologies in crop production systems. The field operations are the key factors that are quite labor-intensive either due to their complexity or because of their association with sensitive plants/edible product interaction, Major technological advancements in agriculture have drastically transformed several processes, both in crop and livestock production systems, during the last decades.

These advancements are mainly related to the minimization of operational and production costs, reduction of environmental impact and optimization of the overall production cycle. Focusing on crop production, a systematic review on research and commercial agricultural robotics used in crop field operations a series of optimization models and software tools have been developed so far on field-operation level. This progress, in parallel with the technological advancements and equipment in field machinery, has provided radical solutions to several challenges that modern farmers face.

In crop production systems, one of the most significant issues is connected to human labor-intensive operations. These are, mainly, field tasks (such as sensitive fruits harvesting and intra-row weed control) that are more difficult to be executed by traditional field machinery, and human workers are employed. This has brought the increased need for autonomous tractors and robotic platforms to be used in the crop field operations, currently developed at research stage.

Crop harvesting seems ripe for automation. It's physically taxing and highly repetitive - the kind of labour that's often most effectively targeted in the robot revolution as in factories, manufacturing, mining, logistics processing. Picking crops also requires manual dexterity and a delicate touch. Many fruits bruise easily in the heat, and leafy vegetables are easily torn. And most robots just aren't advanced enough to handle that level of precision. But AGTECH companies in the private sector and robotics departments in academia continue their efforts to clear that hurdle.

This innovative patent covers both scientific and commercial agricultural robotic systems applied in various field operations such as weeding, seeding, disease detection, crop scouting, spraying and harvesting for different farming environments (arable, greenhouses, vineyards and orchards). The systems were related to harvesting and weeding, and the disease detection and seeding robots. The optimization and further development of agricultural robotics are vital, and should be evolved by producing faster processing algorithms, better communication between the robotic platforms and the gadgets, and advanced sensing systems.

The use of robots to automate tasks performed by people is increasing. Robots provide several important benefits over human labor including improved efficiency, less expense, more consistent and higher quality work performed, and the ability to perform hazardous work without endangering people. Individually and collectively, these benefits help businesses increase margins and profits, which is essential for maintaining competitiveness.

Agriculture is one industry with traditionally low profit margins and high manual labor costs. In particular, harvesting can be expensive. For some crops, such as tree fruit, harvesting labor represents the growers' single largest expense, up to 50% of total crop cost. Increasing labor costs and labor shortages threaten the economic viability of many farms. Therefore, replacing manual labor with robots would be extremely beneficial for harvesting. Additional benefits could be obtained through automating other tasks currently done manually such as pruning, culling, thinning, spraying, weeding, measuring and managing of agricultural crops.

GPS controlled automated tractors and combines already operate in wheat and other grain fields. Automated harvesters exist that can blindly harvest fruit by causing the fruit to drop from a plant into a collection device. For example, Korvan Industries, Inc. makes equipment than shakes oranges, grapes, raspberries, blueberries, etc. off plants. These harvesting approaches have wide scale applicability, but are not applicable to the harvesting of all crops.

For example, while oranges may be harvested en mass by shaking the tree, this method only works for the fruit that will be processed. Shaking cannot be used for picking oranges sold as fresh, i.e. table fruit. The violent nature of this harvesting technique can bruise the fruit and tear the skin, which is both unappealing to the consumer and causes the fruit to rot quickly.

Thus, whole tree harvesting approaches comprising "shaking," are inappropriate for picking fresh fruits and vegetables such as apples, pears, tomatoes and cucumbers that are to be sold as whole fruit. A different approach is required, one in which each piece of fruit is picked individually.

People have attempted to develop mechanical pickers to pick whole fruits for years. For example, Pellenc, a French manufacturer, built a prototype orange picker, but abandoned the project. One common failure mode for these picking systems was that they could not locate fruit located on the inside of the tree that cannot be seen from outside the canopy. To date, no equipment exists that can pick fresh fruits and vegetables efficiently enough to compete with human labor in cost or yield. Furthermore, machines have been used in an attempt to hedge grape vines.

Hedging grape vines provides a rough cut to the vines that blindly shapes the vines. The final pruning of the canes on the grape vines is non-trivial and is best performed using a holistic view of the grape vine and planning before pruning is attempted. To date, no known machines are configured to intelligently perform the final pruning of grape vines. Known final pruning methods utilize humans operating pruning devices by hand. In addition, there are no known systems that scout and pre-plan harvesting, pruning, culling or other agricultural functions. Similarly, to harvesting and pruning, automating other tasks such as thinning, spraying, culling, weeding, measuring and managing of agricultural crops can lower costs and increase consistency and quality.

A farmer's main inventory is the crop in the field. Managing that inventory requires knowledge about that inventory such as the count, size, color, etc. of the crop on each tree, bush, or vine. To date, farmers estimate these parameters from relatively small samples taken by manual observation that are prone to errors when projecting parameters of the entire crop. Because of the time, cost, and effort required to do these estimates, farmers often do not even perform these estimates.

Satellite imagery has recently enabled macro-level estimates of some of these crop parameters such as tree vigor, crop ripeness by color, or the presence of certain diseases. While this is useful information, it does not provide data at the individual tree/bush/vine level. For at least the reasons detailed in this section, there is a need for an agricultural robot system and method.

OBJECTIVES OF THE INVENTION 1. The objective of the invention is to a fitted with a MY1020Z 600W 36V 480RPM DC Motor (GB) to operate the plucking robotic hand.

2. The other objective of the invention is to a Brushless Motor Controller DC 12 36V 500W PWM Driver Board is fitted with the innovation to control or directorate the vehicle 3. The other objective of the invention is to a Su-kam 100 Watt 12 V Solar Panel Polycrystalline is fitted with the innovation to make itself sustainable one 4. The other objective of the invention is to a water container is used to store water and helpful for automatic water irrigation. 5. The other objective of the invention is to a MKGW1 BLE Gateway is attached with this innovation and helpful for long range Wi-Fi connection. 6. The other objective of the invention is to a Protable Automatic Watering Irrigation Module with Soil Moisture Detection is fitted for sustainable water irrigation 7. The other objective of the invention is to a 60AH Solar Battery and 100 Watt 12V Solar Inverter is fitted with the innovation to make it renewable assisted. 8. The other objective of the invention is to a portable automatic watering irrigation module with Soil Moisture Detection is ffitted for automatic water irrigation purpose

SUMMARY OF THE INVENTION Working Principle:

IoT based Agri robotic hands for plucking underground crops & automatic spraying is a robotic assisted portable vegetable harvesting cum automatic water spraying device effective for 12 kilometers working range. This innovation is fitted with a MY1020Z 600W 36V 480RPM DC Motor which is controlled by Brushless Motor Controller DC 12-36V 500W PWM Driver Board.

A water container covered by 100 Watt 12 V solar panel polycrystalline to make this system sustainable one. MKGW1 BLE gateway which detecting bluetooth device wireless signals attached with portable automatic watering irrigation module with soil moisture detection which detect soil moisture conditioning. 60AH Solar Battery and 100 Watt 12V solar inverter is attached with 100 Watt 12 V solar panel polycrystalline to convert the DC to AC system requirement.

The invention enables an agricultural robot system and method of harvesting, pruning, thinning, spraying, culling, weeding, measuring and managing of agricultural crops. One approach for automated harvesting of fresh fruits and vegetables, pruning of vines, culling fruit, thinning of growth or fruit buds, selective spraying and or fertilizing, weeding, measuring and managing of agricultural resources is to use a robot comprising a machine-vision system containing cameras such as rugged solid state digital cameras.

The cameras may be utilized to identify and locate the fruit on each tree, points on a vine to prune, weeds around plants. In addition, the cameras may be utilized in measuring agricultural parameters or otherwise aid in managing agricultural resources. Autonomous robot(s) or semi-autonomous robot(s) coupled with a tractor, trailer, boom or any combination thereof comprise embodiments of the invention. In one embodiment of the invention a robot moves through a field and uses its vision system and other sensors to "map" the field to determine plant locations, the number and size of fruit on the plants and the approximate positions of the fruit on each plant.

In addition, a map can contain the location of branches to be pruned, fruit to be culled, buds to be thinned, etc. at the individual tree/bush/vine level as well as for the entire field. Maps can also contain other data such as tree vigor, pest infestation, state of hydration, etc. that is associated with each plant in the field. A robot employed in these embodiments may additionally comprise a GPS sensor or other external navigational aids to simplify the mapping process.

The function of taking data which is used to create the map(s) may also be called scouting. In this case, if a robot performs primarily this function, it may be called a Scout robot. If the function is performed on a robot as part of a more complex series of functions, then this function may be called the Scouting function or Scouting part of the robot. The following terms may be used interchangeably and their usage is not meant to limit the intent of the specific design feature: plants, vines and trees; fruits and vegetables; and fields, orchards and groves.

Once the map(s) are prepared, the robot or another robot or server can create an action plan that the robot or another robot can then implement generally by moving and using articulated arms or other task-specific actuators, such as a selective sprayer to implement an agricultural function under the direction of a processor system. An action plan may comprise operations and data specifying picking, pruning, thinning, spraying, culling, measuring, or any other agricultural function or combination thereof. The vision system may be coupled with a picking system or other task specific actuators to capture data from various locations in and around each plant when performing the picking or desired agricultural function.

In one embodiment of the invention, an agricultural robot gathers data and then determines an action plan in advance of picking, pruning, thinning, spraying, or culling a tree or a vine. This may be done if the map is finished before the robot is scheduled to harvest or prune, or if the action plan algorithm selected requires significant computational time and cannot be implemented in "real time" by the robot as it is picking, pruning or culling plants in a field.

If the algorithm selected is less computationally intense, the robot may generate the map and calculate the plan as it is harvesting, culling, thinning, spraying or pruning for example. When picking, the system harvests according to the selected picking plan. The robot may also plan a cull, so that apple trees for example may be culled in order to ensure that the apples that are not culled that remain on the tree mature and become larger than if all of the apples on a tree were allowed to mature. Any combination of picking, pruning, culling, thinning, spraying, weeding, measuring or any other agricultural activity may form part of the action plan.

In one embodiment of the invention, the robot does not perform any mechanical task. Its sole function is the collection of data from the field to enable the farmer to more efficiently plan and manage his crop. The robot may be called upon to collect data multiple times during a growing season with multiple or different sensor sets attached. This data may be used to predict future crop performance in order to optimize factors such as fertilizer input, zones to pick, timing of maturity, etc., which will result in improved profitability.

An agricultural robot may comprise zero or more actuators or articulating arms coupled with a self-propelled automated platform or coupled with a tractor, trailer or boom. An arm may be configured or coupled with an implement configured to pick, prune, cull, thin, spray, weed, take samples or perform any other agricultural task that is desired. Each arm may include one or more cameras and/or an embedded processor to accurately locate and reach each piece of fruit/vegetable, and an end effector which provides further action.

The end effector may be a mechanical hand that grabs and picks fruit, or may contain some mechanical cutting or thinning device, some type of spraying mechanism, or any other device or implement to perform an agricultural function or observation or measurement. The end effector may also contain a mechanism to cut or snip the fruit from the stem rather than just pulling it free.

The system may comprise two or more different style arms incorporated into the robot in order to reach the fruit on different parts of the tree or to perform different agricultural functions independent of the other arm or dependent upon the other arm, e.g., one arm may be configured to move branches so that another arm may be allowed to pick or cull fruit for example. The robot may pull or carry loading bins, into which it may load the picked fruit. In addition, the robot may work with bins that are handled by a separate means not attached to the robot.

Harvest bins may comprise any device that is capable of holding picked fruit such as a basket, a bushel, a box, a bucket or any other agricultural fruit repository. Bins may be left in the field or transported to the robot one at a time. Packaging may be performed at the robot or at any other location utilizing an embodiment of the robot or any other machine to which the robot may transport agricultural items. In an alternative embodiment of this invention, the end effector(s) is/are not mounted on an articulating arm (such as directly to the robot's frame or on a non-articulating arm).

Alternate embodiments of an agricultural robot may comprise semi-autonomous robot(s) that may be coupled with a tractor, boom or trailer for example coupled with an extension link to allow for movement along or about the axis of tractor travel at a velocity other than that of the tractor. Robots are mounted on a tractor, boom or trailer in one or more embodiments of the invention which eliminates or minimizes the drive mechanisms in the robots used in autonomous self-propelled platforms. Robots that are not self-propelled are generally smaller and cheaper. In addition, most farms have tractors that may be augmented with robots, allowing for easy adoption of robots while minimizing capital expenditures. Some farms may require a driver to physically move robots for safety or other concerns. One or more embodiments utilize a scout and harvester mounted on a tractor.

Alternatively, or in combination a trailer comprising a scout and/or harvester may be coupled with a tractor. A boom may also be utilized as a mount point for a scout and/or harvester alone or in combination with a tractor and/or trailer, and the boom may be mounted in the front of the tractor, sides of the tractor, rear of the trailer or sides of the trailer. A power source such as a generator may be mounted on the tractor, boom or trailer and may make use of the tractors power-take-off unit. The power source may be utilized in powering any robots coupled with the tractor, trailer or boom. Embodiments of the robot may obtain power from the tractor's hydraulic system as well.

BRIEF DESCRIPTION OF THE DIAGRAM FIG.1: Water container FIG.2: Brushless Motor Controller DC 12-36V 500W PWM Driver Board. Fig.3: BLDC Motor works Explore here Fig.4: BLDC Motor FIG.5: Solar Plate. FIG.6: Water container FIG.7: Tool Kit

DESCRIPTION OF THE INVENTION

Embodiments of the invention enable an agricultural robot system and method of harvesting, pruning, thinning, spraying culling, weeding, measuring and managing of agricultural crops. One approach for automated harvesting of fresh fruits and vegetables, pruning of vines, culling fruit, weeding, measuring and managing of agricultural resources, etc. is to use a robot comprising a machine-vision system containing cameras such as rugged solid-state digital cameras. The cameras may be utilized to identify and locate the fruit on each tree, points on a vine to prune, weeds around plants. In addition, the cameras may be utilized in measuring agricultural parameters or otherwise aid in managing agricultural resources.

The cameras may be coupled with a picking system or other implement to allow views all around and even inside the plant when performing the picking or desired agricultural function. Autonomous robot(s) or semi-autonomous robot(s) coupled with a tractor, trailer, boom or any combination thereof comprise embodiments of the invention. In one embodiment of the invention a robot moves through a field first to "map" the field to determine plant locations, the number and size of fruit on the plants and the approximate positions of the fruit on each plant. Alternatively, a robot may map the cordons and canes of grape vines. In such a case, the map would consist of the location of each cordon, cane, and sucker as well as the location and orientation of buds on each cane.

The function of the map is to allow the robot to make intelligent decisions and perform tasks based on what the vision system or other attached sensors detect along with rules or algorithms in the robot's software.

For instance, the robot may choose to pick only fruit meeting a certain size criteria and may optimize the picking order for those fruit. Or the robot may use the map of a grape vine along with rules embodied in its software to prune the vine to the 8 best canes per cordon with 2 buds left on each of those canes. Alternatively, or in addition, the map may be used for other purposes other than functional decisions by the robot. For example, data from the map may be used by the grower to track crop performance and make intelligent decisions about when to harvest or when to prune. Other embodiments gather data applicable to thinning, spraying culling, weeding and crop management.

A robot employed in these embodiments may comprise a GPS or other sensor to simplify the mapping process. Once the map is complete for a field, the robot or another robot or server can create an action plan that the robot or another robot can then implement generally by moving and using actuators that may be mounted on articulated arms. These task specific tools enable the robot to implement an agricultural function under the direction of a processor system.

An action plan may comprise operations and data specifying picking, pruning, thinning, spraying, culling, measuring, or any other agricultural function or combination thereof. Pre-mapping and preplanning picking allows for efficient picking and pre-mapping and preplanning for pruning allows for effective pruning. A map may also enable improved farming by providing data even if that data is not acted on by another functional robot.

In the following exemplary description numerous specific details are set forth in order to provide a more thorough understanding of embodiments of the invention. It will be apparent, however, to an artisan of ordinary skill that the present invention may be practiced without incorporating all aspects of the specific details described herein. Any mathematical references made herein are approximations that can in some instances be varied to any degree that enables the invention to accomplish the function for which it is designed.

In other instances, specific features, quantities, or measurements well-known to those of ordinary skill in the art have not been described in detail so as not to obscure the invention. Readers should note that although examples of the invention are set forth herein, the claims, and the full scope of any equivalents, are what define the metes and bounds of the invention. Agricultural elements as used herein pertain to fruit, vegetables, branches, plants or trees, or any other item found in an agricultural field.

Pre-mapping enables efficient picking. A map can be created either just before harvesting or earlier in the growing season. While navigating through the grove and mapping, a scout robot can gather other useful information including the condition, size, quantity, health and ripeness of the fruit, individual trees and the orchards as a whole.

In another embodiment, the scout robot can be equipped with a variety of sensors, including but not limited to cameras, hydration sensors, spectral sensors or filters to sense changes in coloration of the leaves, bark or fruit. Using a semi-autonomous or autonomous robot to carry these sensors may allow the farmer to collect data more frequently, more thoroughly, and/or in a more cost-effective manner than manually deploying sensors or using a costly network of fixed sensors.

Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-of digital farming-based sensors for the creation of virtual farms were highlighted as some of the gateways. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information.

For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. 1. Water container 2. MY1020Z 600W 36V 480RPM DC Motor (GB) 3. Robotic Link joints

4. Robotic link 5. Wheels 6. End effector 7. Aurdino Brushless Motor Controller DC 12-36V 500W PWM Driver Board Robotic Hand 8. Long range lora-wifi (MKGW1 BLE Gateway) 9. Su-kam 100 Watt 12 V Solar Panel Polycrystalline MY1020Z 600W 36V 480RPM DC Motor (GB)

1. Model: MY 1020Z2. 2. Supply Voltage: 36V. 3. Output Power: 600W. 4. Speed: 480RPM. 5. Full Load Current: 21.40 Amp. 6. Torque: 1.80 N.m. 7. Efficiency: 78%.

This motor should work with some Xtreme or Razor electric scooter, mini bike or go kart using a 600W motor. Should also work with any other brand with similar specs in the motor.

Model MY 1020Z2.600W Operating Power 600 W Operating Voltage (VDC) 36 Rated Current(A) 2.5 Rated Speed (RPM) Base: 3200 Rated Speed After Reduction (RPM) 480 Rated Torque (Kg-Cm) 18.35 Gearbox Yes Weight (Kg) 5.438 Efficiency >78%. Application Small and Medium-size E-Tricycle Gear Ratio 6.67:1 Cable Length (Meter) 0.5 Shipment Weight 6.1 kg Shipment Dimensions 19 x 10 x 16 cm

Brushless Motor Controller DC 12-36V 500W PWM Driver Board

1. Drive plate type: JYQDV6.3E2 2. Voltage range: 12V-36V. 3. Peak current: 20A. 4. PWM speed control: PWM frequency 1-20KHZ. 5. Duty cycle: 0-100% 6. Drive power: 500W (related working voltage).

This Brushless Motor Controller DC 12-36V 500W PWM Driver Board is used for the 3-phase brushless sensorless motor, but not suit for all 3-phase brushless senseless motors directly. If the driving effect is not good (such as starting jitter, reversing, the motor no-load working current is too large, the speed is not stable, the efficiency is low, and can't start-up with a load.) Customers can adjust the resistance and capacitance of the driver board according to the actual situation to achieve the best driving effect. To know more about how BLDC Motor works Explore here Specifications and Features:

1. Protection: Overload protection, locked-rotor protection, over-current protection. 2. Analog voltage speed regulation: 0-5V 3. Operating temperature: -40 ~ +85°C 4. Drive power s 500W (related working voltage). 5. Size: 62 x 42.5 x 17 mm. 6. Weight: 26 gm. 7. Speed control function: voltage speed regulation or single chip. 8. Microcomputer control PWM speed regulation.

Voltage Range(V) 12-36 Current handling Capacity(A) 15 Power handling Capacity(W) 500 Length (mm) 63 Width (mm) 42.5 Height (mm) 17 Weight (gm) 30 Shipment Weight 0.105 kg Shipment Dimensions 10x6x2cm

100 Watt 12 V Solar Panel Polycrystalline

Type of Product: Polycrystalline Solar Panel Sub Brand: Solar L V Panel Rated Power Range: 60-100 W Watt: 100 W

Operation:

Solar energy is a renewable form of energy which does not emit any harmful carbon radiations in the environment. It is this very aspect of solar energy that is causing a huge stir in the electrical sector wherein everyone is moving towards a greener form of lighting solutions. The Solar panels are made from solar cells i.e. Poly crystalline/Mono-crystalline. When sun rays fall on these cells they store the energy which later is converted into electrical energy by the solar inverter connected to the solar panels.

Features of 100 W Polycrystalline Solar Panel:

The Solar panels are made up of Poly-crystalline cells connected in series. It is powered by TEDLER, crane glass and is provided with EVA lamination. The high performance of the Solar panels can be attributed to the Black sheet which ensures robustness and high-performance. The solar cells are laminated between the UV resistant polymer i.e. EVA and the high transmission toughened glass surface. The terminal box is made from Nylon which ensures that output connections are not hampered by unfavorable weather conditions. It has a Pm Temperature Coefficient (%/K) of -0.4 and a NOCT- Nominal operating cell temp. (Celsius): 45.

Features

1. Outdoor test for energy yield 2. Electroluminescence 3. IR Thermography 4. Reliability test for a junction box 5. Damp heat test 6. Thermal cycling humidity freeze 7. Hot-spot test 8. Low Irradiance test 9. STC & NOCT performance 10. UV- Outdoor exposure 11. Dry Hipot 12. Insulation Resistance 13. Wet Leakage Current 14. Bypass Diode Thermal Test

Module Size Round Mounting Hole Dimension (mm) 6.9 Mounting Hole (X-Axis) (mm) 630 Mounting Hole (Y-Axis) (Distance b/2 2 holes) (mm) 503 Electrical Parameters Voc(V) 21.4 Isc (A) 6.3 PMax (W) 100 Vpm (V) 17.7 Ipm (A) 5.7 Eff.(%) 14.93

Tolerance (±mm) ±5 Verify the Bill of Material Yes Electrical Tolerance (± %) 3 RF ID Yes MKGW1 BLE Gateway 1. Detecting Bluetooth device wireless signal 2. Gateway working states will be displayed by LED 3. 300 bluetooth device can be scanned per second 4. Supporting MQTT(TCP/SSL), HTTP/HTTPS 5. Internet transmission protocol 6. Firmware can be upgraded by OTA and USB 7. Users will easy to get the data from iBeacon 8. Eddystone, ble Sensor (humidity&temperature and accelerometer) and other bluetooth device, uploading this information to the server or IOT cloud platform.

Main Features Bluetooth Standard: BLE5.0 (nRF52832) Power supply: PoE (48V) , DC (9V) and Micro USB (5V) Protocol: MQTT (TCP/Isl and HTTP/HTTPS Network: Ethernet and WIFI User can modify the gateway parameters on the web UL. The WIFI data rate of MKGW1 can be the Max 150Mbps

Frequency Bands 433/47OMHz,868/915MHZ

Lora WAN@-Based Protocol V1.0.2

WiFi Standard IEEE 802.11b/g/n

Input Voltage 12VDC/POE 48V

Working Mode Half-duplex

Channel 8

Max Tx Power 30dBm

Max Rx Sensitivity -141dBm

Backhaul Connectivity Ethernet/WiFi

Dimension 120mm x 120mmX22.5mm

Portable Automatic Watering Irrigation Module with Soil Moisture Detection

100% brand new and high quality Made of high quality material, durable and practical to use DIY Set Plant Automatic Watering Adjustable Soil Moisture Detection Irrigation System

Type: Voltage Regulator

100 Watt 12V Solar Inverter

Operating Voltage 12 V Voltage at max 18.2 V power Open circuit voltage (Voc) 22.OV Current at max power (Imax)

Product Specification Operating Voltage 12 V Voltage at max power 18.2 V (Vmax) Open circuit voltage (Voc) 22.OV Current at max power (Imax) 5.5 Amps Short circuit current (Isc) 5.9 Amps Dimensions (LxWxT) 103 x 67 x 3.4 cm No. of Cells 60 Model Name Solar Panel 150W /12V

Output power 100 Watts PV Panel Type Poly Crystalline Manufacturer Warranty 1 year on manufacturing defects Performance Warranty 25 Years Minimum Order Quantity 1 Unit

AH Solar Battery Solar batteries are C10 rated Tubular Technology based solar battery that has deep cycle design. This battery is design to provide longer backup when charged from solar power. Complete specification regarding 60 Ah Luminous solar batteries is below:

Particulars Description

Brand Luminous

Model LPT1260H

Rating 60 AH

Volt 12 Volt

Battery Type Lead Acid

WE CLAIMS

1. Our Invention "IoT Based Agri Robotic Hands for Plucking Under Ground Crops

& Automatic Spraying" is an agriculture regarded as biggest human industry. In Indian GDP agriculture sector share is 20.5 percent. Agriculture is quickly becoming an exciting high-tech industry, drawing new professionals, new companies and new investors. The UN estimates the world population will rise from 7.3 billion todays to 9.7 billion in 2050. The world will need a lot more food, and farmers will face serious pressure to keep up with demand. Almost 70 percent of the India's population depends on the agriculture sector and in rural population almost 80 percent depends on agriculture sector. Sensing technology engaged in high reliability, adaptability, recalibration, information processing, data fusion, validation and integration of novel high performance sensors specifically aims to monitor agricultural and environmental parameters which leads to more feasible agricultural productivity. Smart control, smart sensing.

2. According to claims# the innovation is to fitted with a MY1020Z 600W 36V 480RPM DC Motor (GB) to operate the plucking robotic hand.

3. According to claim,2# the innovation is to a Brushless Motor Controller DC 12-36V 500W PWM Driver Board is fitted with the innovation to control or directorate the vehicle 4. According to claim,2,3# the innovation is to a Su-kam 100 Watt 12 V Solar Panel Polycrystalline is fitted with the innovation to make itself sustainable one 5. According to claim,2# the innovation is to a water container is used to store water and helpful for automatic water irrigation. 6. According to claim,2,4# the innovation is to MKGW1 BLE Gateway is attached with this innovation and helpful for long range Wi-Fi connection. 7. According to claim,3,4# the innovation is to a Protable Automatic Watering Irrigation Module with Soil Moisture Detection is fitted for sustainable water irrigation 8. According to claim,2,3,4# the innovation is to a 60AH Solar Battery and 100 Watt 12V Solar Inverter is fitted with the innovation to make it renewable assisted. 9. According to claim,2,3,4,5# the innovation is to a portable automatic watering irrigation module with Soil Moisture Detection is ffitted for automatic water irrigation purpose

FOR Prof. Ramesh Chandra Panda Dr. Sailesh Iyer Dr. Manish Sakhlecha Dr. Ratnesh Tiwari Dr. Taranjeet Sachdev Mr. Mayank Sharma Parv Nayak Prashant Singh Smita Jaiswal Dr. Love Kumar Dr. Sunil S. Harakannanavar K. Aravindhan 16 Mar 2021

TOTAL NO OF SHEET: 03 NO OF FIG:07 2021101375

FIG.1: Water container

FIG.2: Brushless Motor Controller DC 12-36V 500W PWM Driver Board.

FOR Prof. Ramesh Chandra Panda Dr. Sailesh Iyer Dr. Manish Sakhlecha Dr. Ratnesh Tiwari Dr. Taranjeet Sachdev Mr. Mayank Sharma Parv Nayak Prashant Singh Smita Jaiswal Dr. Love Kumar Dr. Sunil S. Harakannanavar K. Aravindhan 16 Mar 2021

TOTAL NO OF SHEET: 03 NO OF FIG:07 2021101375

Fig.3: BLDC Motor works Explore here

Fig.4: BLDC Motor

FOR Prof. Ramesh Chandra Panda Dr. Sailesh Iyer Dr. Manish Sakhlecha Dr. Ratnesh Tiwari Dr. Taranjeet Sachdev Mr. Mayank Sharma Parv Nayak Prashant Singh Smita Jaiswal Dr. Love Kumar Dr. Sunil S. Harakannanavar K. Aravindhan 16 Mar 2021

TOTAL NO OF SHEET: 03 NO OF FIG:07 2021101375

FIG.5: Solar Plate.

FIG.6: Water container

FIG.7: Tool Kit