Description:FIELD OF THE INVENTION
This invention relates to a system of hybrid agricultural robot for farmer assistance.
BACKGROUND OF THE INVENTION
India is surrounded by issues like pests, scarcity of water, and animal infestation that are adversely impacting crop production. The human behind the trigger is still a crucial factor, even with fantastic technology at one hand. We suggest a low-cost system to monitor fields using cameras, by keeping an eye on the pests and estimating soil moisture levels. It also integrates a robot controlled by the system that uses computer vision to identify and assess crop health.
CN114158543B The invention belongs to the technical field of agricultural robots, and particularly relates to a crawler-type hillside orchard inter-row inter-plant hybrid weeding robot, comprising a traveling mechanical movement, an identifying apparatus, an inter-row weeding device, an inter-plant weeding device, and a control module, wherein the control module controls the traveling mechanical movement, the identifying apparatus, the inter-row weeding device, and the inter-plant weeding device to act in cooperation. The weeds between the rows and between the plants can be cleaned at the same time in synchronization. Therefore, the weeding efficiency can be greatly improved. The weed implement can make selective adjustment or cleaning according to the row spacing, weed height, etc., so the weeding can be cleaner. The crawler-type travel mechanism is adopted so that the walking performance is multi-terrain walking in mountainous areas, practicality and application scope are stronger and wider. The camera subassembly and laser radar subassembly are adopted for visual observation and information acquisition, and subsequent cooperation control is given to the robot to walk and complete work by itself. Barrier distance operation, non-interference operation, and automation are relatively high, and the control mode is changing.
RESEARCH GAP: Continuous Operation: The robot can run non-stop, providing 24/7 monitoring and guarding of the crops.
US9382003B2 Today, modern farming is conducted by tens of hectares per hour ground equipment that weighs a few tons or aircraft with different capacities. Automated farming may well employ small, nimble, lightweight, and energy-efficient drones to be used for exactly the same job as traditional farm equipment—albeit on a plant-by-plant basis, thus enabling some new methods of farming. The "aerial farm robots" are unmanned aerial vehicles (UAVs) combined with interchangeable implements and reservoirs. High-precision positioning and vision technology, hereinafter referred to as precision control or autonomous operation, are connotation recognition areas mainly used for agricultural farming in which an integrated latitude system is included. The aerial farm robots can accurately fly and fit together to create a bridge connection with one another using an end effector, which makes them able to work in conjunction as part of the global automated farming system that is running entirely on its own, only notifying farmers or other stakeholders when any of these control/refill/recharge/communication subsystems break down from daily animal husbandry tasks.
RESEARCH GAP: Improved Productivity: The system, by ensuring the right circumstances for growth and taking immediate action at any red flag signs so that it can benefit in increasing the yield of a crop which will help farmers to be more productive.
US9852644B2 Modern agriculture is carried out by heavy ground machinery or airplanes, which weigh many tones and cover hundreds of hectares per hour uniformly. Instead, we can fly small agile light energy-efficient flying robotic devices and have them do the same work on every plant even if it has to be done one at a time or in new ways of farming. A hybrid airship/drone benefits from passive lift (e.g. a gas balloon) as well as active lift (e.g., propellers). Hybrid Airship-Drone: Can be less expensive, more stable in flight, and require less maintenance than other aerial vehicles, e.g., quadcopters. But hybrid airship-drones might also be scaled up and hence have extra inertia that has to be shaken off for launch, deceleration, or turns.
RESEARCH GAP: Automatic Reports: The system automatically creates reports and alerts that allow the farmers to see what is going on, with no extra effort.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Hybrid Agricultural Robot For Farmer Assistance.
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.
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.
This invention develops an autonomous robotic system which will traverse in agricultural fields for monitoring purposes as well as data collection. The information can be utilized for precision agriculture. The robotic system will be outfitted with a high-resolution camera and an array of sensors to enable tasks such as yield estimation, pest detection, and soil moisture assessment. The robot will process image data using TensorFlow and the YOLOv5 model with cutting-edge computer vision techniques to be able to recognize and offer a health assessment of plants. All of the data collected will be sent to a central system which farmers can tap into through an easy-to-use interface in order that they may better manage their lands for maximum results.
This system is going to change the conventional farming practices and can add tech-ingredients for more effectiveness, less labour costs, and an increase of farm production in a huge manner. That is why using technology to automate this process can help farmers make strategic decisions rather than manual labour. The project also reflects a sustainability aspect by providing water and pesticides in the best of its use which leads to eco-friendly farming.
The initialization process incorporates the motors, sensors, and camera, among other components, once the robot's power system is turned on. With every startup, the robot must always perform a self-test that checks the hardware and software for problems so that necessary corrections are made.
The robot can work in either Manual Control, where the farmer manually orders and drives the robot by a remote interface to do some specific tasks, or Automated Control, where predefined patrol routes will be followed and routine monitoring implemented with AI algorithms for autonomous navigation. Obstacle detection in front of the robot is detected, and avoidance algorithms are used to safely navigate around them. If no obstacles are detected, it continues on its planned route.
The following modules to activate are the data collectors. A high-definition camera captures high-detailed images and videos of plants to assess plant health and pest identification. Soil moisture is detected through a humidity sensor placed at different points, which comes in handy in administering irrigation. At the same time, a pest detector utilizes computer vision with YOLOv5 for automatic detection and classification of potential hazards. Processes accurate data gathered with TensorFlow and YOLOv5 for pest occurrence, plant health, and soil hydration level. It will re-record the needed data to make the required corrections in the presence of inaccuracies. The captured data goes through image processing and data analysis before transmission to a central monitoring location.
The farmer is connected in real-time to the central system, with the possibility to receive images and sensor readings of the field drone either through Wi-Fi or Bluetooth. Plant health data, soil moisture, and test results for detecting pests will appear on a dashboard that gives an overview of conditions in the field. This information will then be used to raise alerts with tips to the individual farmer on when to irrigate and when pests are expected to infest. The farmer's feedback is utilized to revise the system. This means that it may change the robot's path and improve the algorithms for data analysis. On the contrary, if feedback is not received, then the system awaits further input; this thereby assures that a decision made by a farmer to affect an action based on the robot sensor data was finally approved.
The robotic platform is a wheeled or tracked, rugged, all-weather mobile unit designed for moving on different terrains. It incorporates basic units such as sensors, cameras, and processing units, thereby guaranteeing stability and robustness in agricultural operation environments. It has integrated sensors like soil moisture to determine water content, temperature, humidity sensors to monitor the environment, and advanced imaging pest detection sensors to detect harmful insects. The system utilizes TensorFlow and YOLOv5 for running the detection part on a live stream from the camera in real-time; more precisely, TensorFlow presents the deep learning framework within which YOLOv5 identifies plants and pests in real-time. The powerful on-board computers manage power to process all the sensor data, images, and computer vision algorithms for analysis of soil moisture levels and compile findings into actionable insights. The robot's communication module is Wi-Fi- and Bluetooth-enabled, bringing real-time data transmission capabilities to a central system for continuous monitoring and immediate response to issues in the field. A general view of the dashboard is offered through a web-based or mobile application where all the data can be visualized, alerts issued, and finally, where the customer interacts with the robot. The rich interface ensures easy access and interpretation of information. The manual and automatic modes of operation offered by the control system enable farmers to manipulate movement remotely, make the robot follow a pre-defined path, or use AI-driven navigation features for autonomous movement.
System initialization is the first step: when the robot switches power on, it initializes all systems, starts the operating system, sensors, and cameras, and checks that all components are OK. Manual Operation Mode is the state of operation in which the farmer or operator has direct control over the movement and action functions of the robot using a remote control or any similar interface to perform specific works that need human judgment or troubleshooting. There can be some predefined routes or AI algorithms following which the robot uses GPS and other systems to ensure that movements are precise. It continuously checks the surroundings for obstacles and takes precautions to avoid them if they appear, so navigation will be safe and not lead to the destruction of crops. This way, the robot collects data through its sensors and cameras when moving across the field: images of plants, soil moisture, temperature, and detecting pests. It then verifies the information validity: for invalid or incomplete information, it will try to recollect. For the valid data, it proceeds through several advanced algorithms, including TensorFlow and YOLOv5, to analyze the images, identify soil moisture, and locate pests. It then forwards this processed data to a central system using a communication module that represents plant status, soil, and pest details in real-time. The interface was developed to be user-friendly and accessible to the users. According to the data analyzed, the actionable insights and feedback to the farmer involve watering suggestions, pest control, and other recommendations that are necessary for the best health of the crop. The system then waits for farmer feedback and processes this information for better data collection and analysis in the future, ensuring that it is in continuous improvement with input from the user. This is the completion of the ongoing cycle this robot is performing; following this, the system can be prepared to perform another cycle, or it can be put on a stand-by till the commencement of the next operation.
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: System Architecture
Figure 2: Agribot and Power management system.
Figure 3: Detailed architecture with full functionality.
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.
This invention develops an autonomous robotic system which will traverse in agricultural fields for monitoring purposes as well as data collection. The information can be utilized for precision agriculture. The robotic system will be outfitted with a high-resolution camera and an array of sensors to enable tasks such as yield estimation, pest detection, and soil moisture assessment. The robot will process image data using TensorFlow and the YOLOv5 model with cutting-edge computer vision techniques to be able to recognize and offer a health assessment of plants. All of the data collected will be sent to a central system which farmers can tap into through an easy-to-use interface in order that they may better manage their lands for maximum results.
This system is going to change the conventional farming practices and can add tech-ingredients for more effectiveness, less labour costs, and an increase of farm production in a huge manner. That is why using technology to automate this process can help farmers make strategic decisions rather than manual labour. The project also reflects a sustainability aspect by providing water and pesticides in the best of its use which leads to eco-friendly farming.
The initialization process incorporates the motors, sensors, and camera, among other components, once the robot's power system is turned on. With every startup, the robot must always perform a self-test that checks the hardware and software for problems so that necessary corrections are made.
The robot can work in either Manual Control, where the farmer manually orders and drives the robot by a remote interface to do some specific tasks, or Automated Control, where predefined patrol routes will be followed and routine monitoring implemented with AI algorithms for autonomous navigation. Obstacle detection in front of the robot is detected, and avoidance algorithms are used to safely navigate around them. If no obstacles are detected, it continues on its planned route.
The following modules to activate are the data collectors. A high-definition camera captures high-detailed images and videos of plants to assess plant health and pest identification. Soil moisture is detected through a humidity sensor placed at different points, which comes in handy in administering irrigation. At the same time, a pest detector utilizes computer vision with YOLOv5 for automatic detection and classification of potential hazards. Processes accurate data gathered with TensorFlow and YOLOv5 for pest occurrence, plant health, and soil hydration level. It will re-record the needed data to make the required corrections in the presence of inaccuracies. The captured data goes through image processing and data analysis before transmission to a central monitoring location.
The farmer is connected in real-time to the central system, with the possibility to receive images and sensor readings of the field drone either through Wi-Fi or Bluetooth. Plant health data, soil moisture, and test results for detecting pests will appear on a dashboard that gives an overview of conditions in the field. This information will then be used to raise alerts with tips to the individual farmer on when to irrigate and when pests are expected to infest. The farmer's feedback is utilized to revise the system. This means that it may change the robot's path and improve the algorithms for data analysis. On the contrary, if feedback is not received, then the system awaits further input; this thereby assures that a decision made by a farmer to affect an action based on the robot sensor data was finally approved.
The robotic platform is a wheeled or tracked, rugged, all-weather mobile unit designed for moving on different terrains. It incorporates basic units such as sensors, cameras, and processing units, thereby guaranteeing stability and robustness in agricultural operation environments. It has integrated sensors like soil moisture to determine water content, temperature, humidity sensors to monitor the environment, and advanced imaging pest detection sensors to detect harmful insects. The system utilizes TensorFlow and YOLOv5 for running the detection part on a live stream from the camera in real-time; more precisely, TensorFlow presents the deep learning framework within which YOLOv5 identifies plants and pests in real-time. The powerful on-board computers manage power to process all the sensor data, images, and computer vision algorithms for analysis of soil moisture levels and compile findings into actionable insights. The robot's communication module is Wi-Fi- and Bluetooth-enabled, bringing real-time data transmission capabilities to a central system for continuous monitoring and immediate response to issues in the field. A general view of the dashboard is offered through a web-based or mobile application where all the data can be visualized, alerts issued, and finally, where the customer interacts with the robot. The rich interface ensures easy access and interpretation of information. The manual and automatic modes of operation offered by the control system enable farmers to manipulate movement remotely, make the robot follow a pre-defined path, or use AI-driven navigation features for autonomous movement.
System initialization is the first step: when the robot switches power on, it initializes all systems, starts the operating system, sensors, and cameras, and checks that all components are OK. Manual Operation Mode is the state of operation in which the farmer or operator has direct control over the movement and action functions of the robot using a remote control or any similar interface to perform specific works that need human judgment or troubleshooting. There can be some predefined routes or AI algorithms following which the robot uses GPS and other systems to ensure that movements are precise. It continuously checks the surroundings for obstacles and takes precautions to avoid them if they appear, so navigation will be safe and not lead to the destruction of crops. This way, the robot collects data through its sensors and cameras when moving across the field: images of plants, soil moisture, temperature, and detecting pests. It then verifies the information validity: for invalid or incomplete information, it will try to recollect. For the valid data, it proceeds through several advanced algorithms, including TensorFlow and YOLOv5, to analyze the images, identify soil moisture, and locate pests. It then forwards this processed data to a central system using a communication module that represents plant status, soil, and pest details in real-time. The interface was developed to be user-friendly and accessible to the users. According to the data analyzed, the actionable insights and feedback to the farmer involve watering suggestions, pest control, and other recommendations that are necessary for the best health of the crop. The system then waits for farmer feedback and processes this information for better data collection and analysis in the future, ensuring that it is in continuous improvement with input from the user. This is the completion of the ongoing cycle this robot is performing; following this, the system can be prepared to perform another cycle, or it can be put on a stand-by till the commencement of the next operation.
A Hybrid agricultural robot for Farmer assistance comprises a plurality of Agribot (A1, A2, A-N), Problem Collector (2), Lora Network (3), Tensorflow (4), Yolo5 (5), Camera (6), Raspberry Pi (7), Remote Controller (8), Microcontroller (9), Lidar Sensor (10), Motor Driver (11), Motor (12), DC included Moter (13), XBee (14) and Power Supply (15) wherein the camera obtains plant images for health monitoring and pest identification, while a humidity sensor facilitates irrigation.
In another embodiment the system has integrated sensors like soil moisture to determine water content, temperature, humidity sensors to monitor the environment, and advanced imaging pest detection sensors to detect harmful insects.
In another embodiment the robot will process image data using TensorFlow and the YOLOv5 model with cutting-edge computer vision techniques to be able to recognize and offer a health assessment of plants.
In another embodiment the processed data forward to the central system using a wifi module that represents plant status, soil, and pest details in real-time.
In another embodiment A general view of the dashboard is offered through the web-based or mobile application where all the data can be visualized, alerts issued, and finally, where the customer interacts with the robot.
In another embodiment GPS continuously checks the surroundings for obstacles and takes precautions to avoid them if they appear, so navigation will be safe and not lead to the destruction of crops.
ADVANTAGES OF THE INVENTION
Increased Efficiency: Automation of crop and soil conditions monitoring minimizes the labor force and time it requires in the case of farmers. Automation enables them to focus on other essential aspects, such as planning and controlling the general functioning of the farm, increasing crop yield, and ensuring sound farming practices.
Early Pest Detection: Advanced computer vision of the robot allows early detection of pests. Early detection by the device makes it possible to give intervention at the right time before much damage is caused to the crop in question. Early intervention concerning pests would make it easier for farmers to manage their crops and, therefore, keep healthy yields.
Precise Monitoring: The sensors on the robot give detailed and precise information about soil moisture and plant health. This accurate information helps farmers know what to do to be in a state of growing conditions. Knowing the exact state of their fields will help make an informed decision regarding watering, fertilizing, and other essential aspects of crop management.
Cost-Effective: The system enables a reduction in operational costs and an increase in the profitability of farmers by decreasing the number of unskilled workers and improving resource utilization. Such efficiency allows the farmer to put resources into proper use and have time for other critical activities.
Real-Time Data: Real-time data transfer and continuous monitoring allow a farmer to make decisions based on information received promptly. In this way, it saves a problem from going for the worst, ensuring timely interventions with optimal conditions for crop growth.
, Claims:1. A Hybrid agricultural robot for Farmer assistance comprises a plurality of Agribot (A1, A2, A-N), Problem Collector (2), Lora Network (3), Tensorflow (4), Yolo5 (5), Camera (6), Raspberry Pi (7), Remote Controller (8), Microcontroller (9), Lidar Sensor (10), Motor Driver (11), Motor (12), DC included Moter (13), XBee (14) and Power Supply (15) wherein the camera obtains plant images for health monitoring and pest identification, while a humidity sensor facilitates irrigation.
2. The system as claimed in claim 1, wherein the system has integrated sensors like soil moisture to determine water content, temperature, humidity sensors to monitor the environment, and advanced imaging pest detection sensors to detect harmful insects.
3. The system as claimed in claim 1, wherein the robot will process image data using TensorFlow and the YOLOv5 model with cutting-edge computer vision techniques to be able to recognize and offer a health assessment of plants.
4. The system as claimed in claim 1, wherein the processed data forward to the central system using a wifi module that represents plant status, soil, and pest details in real-time.
5. The system as claimed in claim 1, wherein A general view of the dashboard is offered through the web-based or mobile application where all the data can be visualized, alerts issued, and finally, where the customer interacts with the robot.
6. The system as claimed in claim 1, wherein GPS continuously checks the surroundings for

obstacles and takes precautions to avoid them if they appear, so navigation will be safe and not lead to the destruction of crops.