PHENOTYPING ROBOT FOR INTELLIGENT CROP CULTIVATION (PHENO-BOT)

Abstract

This invention provides an autonomous agricultural robot, PHENO-BOT, designed to optimize crop cultivation through real-time data acquisition, machine learning, and cloud integration. Equipped with advanced sensors, processing capabilities, and autonomous navigation, PHENO-BOT offers a comprehensive solution for intelligent farming, phenotyping, and precision agriculture.

Specification

Description:FIELD OF THE INVENTION
This invention pertains to agricultural robotics and precision farming, specifically focusing on an autonomous robot designed to enhance crop phenotyping, monitoring, and management. By integrating advanced sensor systems, machine learning, and cloud connectivity, PHENO-BOT offers a robust solution to optimize crop cultivation and streamline various agricultural tasks.
BACKGROUND OF THE INVENTION
The agriculture sector is increasingly embracing automation to reduce manual labor and enhance productivity. However, current systems often lack adaptability, real-time decision-making, and the capability to handle diverse agricultural tasks autonomously. Farmers face challenges in data collection, crop analysis, and task execution due to limited access to scalable, data-driven solutions. Existing robotic systems may perform isolated tasks but do not offer a comprehensive platform that integrates data analysis, autonomous navigation, and cloud connectivity.
This invention addresses these gaps by introducing PHENO-BOT, an autonomous agricultural robot that combines phenotyping, monitoring, and management capabilities. Using advanced sensor fusion, machine learning algorithms, and cloud storage, PHENO-BOT collects real-time data, analyzes crop conditions, and autonomously performs field tasks. This robot offers farmers a streamlined, data-driven approach to optimize resource use, increase yield, and ensure sustainable farming practices.
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.
The invention provides PHENO-BOT, a fully autonomous agricultural robot that integrates sensors, machine learning, and cloud-based analytics to optimize crop cultivation. PHENO-BOT utilizes high-resolution cameras, environmental sensors, GPS, and GIS technologies for accurate data collection and navigation. Connected to AWS cloud services, it allows for remote monitoring, data storage, and real-time feedback, enabling farmers to make data-driven decisions and perform targeted interventions for optimal crop health and yield.
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
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.
The PHENO-BOT robot is designed to operate autonomously in agricultural fields, performing tasks such as crop monitoring, phenotyping, and management. Constructed from durable materials like aluminum or carbon fiber, the chassis ensures stability and mobility across varied terrains. PHENO-BOT employs wheel-based or track-based systems to facilitate movement, allowing precise navigation through agricultural landscapes.
PHENO-BOT is equipped with a comprehensive sensor suite for data acquisition. High-resolution cameras capture visual data for phenotyping and disease detection. Spectrometers analyze plant biochemical properties, while environmental sensors measure parameters such as temperature, humidity, and soil conditions. For 3D mapping and obstacle avoidance, PHENO-BOT uses Lidar or radar sensors, enhancing spatial awareness and operational safety.
The processing unit of PHENO-BOT includes a high-performance CPU or GPU, responsible for executing machine learning algorithms and real-time decision-making. Machine learning frameworks like TensorFlow and OpenCV facilitate tasks such as image processing, object detection, and plant phenotyping, allowing PHENO-BOT to provide actionable insights.
For connectivity, PHENO-BOT includes wireless communication modules that support Wi-Fi and cellular networks, enabling seamless data transmission to cloud platforms. IoT connectivity allows PHENO-BOT to interface with AWS for data storage, analysis, and remote monitoring, creating a robust platform for real-time insights. The positioning system, powered by GPS and GIS, provides accurate navigation and mapping, essential for field operations.
The robot is powered by rechargeable lithium-ion batteries and can integrate solar panels for energy-efficient, extended operation. Actuators control movement, enabling PHENO-BOT to interact autonomously with its environment. The entire structure is optimized through CAD design to ensure functionality, stability, and ease of maintenance.
PHENO-BOT operates using a custom-built operating system designed for robotics, leveraging ROS (Robot Operating System) for modular development and scalability. The programming stack includes Python and C++ for high-level and low-level control, integrating with machine learning libraries such as Scikit-learn for real-time crop analysis. Simulation tools like Gazebo allow for testing and optimization before field deployment, ensuring reliable performance.
, Claims:1. An autonomous agricultural robot, PHENO-BOT, comprising a sensor suite, processing unit, communication modules, and navigation system, designed for intelligent crop cultivation and phenotyping.
2. The robot as claimed in Claim 1, wherein high-resolution cameras and spectrometers enable real-time crop analysis, including phenotyping and disease detection.
3. The robot as claimed in Claim 1, wherein Lidar and radar sensors provide 3D mapping and obstacle detection for safe navigation through agricultural fields.
4. The robot as claimed in Claim 1, wherein the processing unit executes machine learning algorithms for data-driven decision-making in crop management.
5. The robot as claimed in Claim 1, wherein wireless communication modules and IoT connectivity facilitate cloud integration for remote monitoring and data analysis.
6. The robot as claimed in Claim 1, wherein GPS and GIS technologies enable precise navigation and mapping of agricultural landscapes.
7. The robot as claimed in Claim 1, wherein rechargeable lithium-ion batteries, optionally coupled with solar panels, provide extended operational periods for efficient fieldwork.
8. A method for crop management as claimed in Claim 1, involving real-time data acquisition, analysis, and autonomous navigation to optimize agricultural tasks.
9. The robot as claimed in Claim 1, wherein it integrates a robotics framework like ROS, facilitating modular development and scalability for future upgrades.
10. The robot as claimed in Claim 1, wherein it includes a simulation and testing capability, allowing performance optimization in a virtual environment before deployment.

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