AUTOMATED WEED CONTROL SYSTEM FOR PRECISION AGRICULTURE

Abstract

This invention introduces an automated weed control system for precision agriculture, utilizing AI, machine vision, and laser technology to target and remove weeds selectively. By reducing herbicide reliance, enhancing crop health, and promoting sustainable farming practices, the system addresses environmental and operational challenges in modern agriculture. Equipped with drones and weed-detection robots, the system provides a comprehensive solution for efficient, data-driven weed management.

Specification

Description:FIELD OF THE INVENTION
This invention relates to precision agriculture and automated weed management technology, focusing on an advanced system that integrates artificial intelligence (AI), machine vision, and automated machinery to selectively target and control weeds. The system aims to reduce reliance on herbicides, minimize environmental impact, and improve operational efficiency in weed management practices.
BACKGROUND OF THE INVENTION
Traditional weed control methods rely heavily on herbicides, which, while effective, pose significant environmental concerns. Herbicides can harm non-target plants, beneficial insects, and soil health, contributing to environmental degradation and biodiversity loss. Additionally, the use of herbicides leads to resistance in some weed species, diminishing long-term effectiveness and requiring increased chemical use. Labor shortages and the high costs associated with manual weeding further challenge sustainable weed management. This invention addresses these issues by introducing an automated weed control system that uses AI and machine learning to detect and manage weeds precisely. By selectively targeting weeds through technologies like laser weeders, weed-detection robots, and drones, the system reduces herbicide usage, improves crop health, and supports sustainable agricultural 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 an automated weed control system that utilizes artificial intelligence and machine vision to detect and differentiate between weeds and crops accurately. This system employs sensors, advanced cameras, and GPS technology to navigate fields autonomously, scanning for unwanted vegetation and applying targeted weed control measures only where needed. The system includes laser weeding capabilities, reducing chemical usage, and a data collection feature for monitoring weed populations and informing future agricultural practices. Designed to be effective across diverse crop types and farming environments, the automated weed control system promotes sustainability, lowers operational costs, and enhances crop yield by maintaining a weed-free growing environment.
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: ILLUSTRATES THE AI-DRIVEN MACHINE VISION SETUP, SHOWING THE SENSORS AND CAMERA SYSTEM USED FOR WEED AND CROP DIFFERENTIATION.
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 automated weed control system integrates artificial intelligence with precision agriculture tools to address the challenges of sustainable weed management. The system's core components include machine vision technology, AI algorithms, laser weeders, robotic weed-detection units, and drones, each contributing to the accurate detection and management of weeds. Using imagery from agriculture fields, machine learning algorithms analyze the visual data to distinguish between crop plants and weeds. The object detection framework enables the system to identify various weed species occupying the same space as crops, allowing for selective targeting.
The weed-detection robots are equipped with advanced sensors and cameras that continuously monitor crop rows, identifying weeds based on shape, size, and color. These robots navigate autonomously through the field, relying on GPS and sensor-based guidance to map weed locations and apply laser weeding techniques precisely where necessary. Laser weeders eliminate the need for herbicides by targeting the weed's growth points with high-intensity lasers, effectively preventing further growth without impacting the surrounding crops.
Drones provide an additional layer of weed management by capturing aerial images and mapping weed infestation zones. This high-level perspective allows for comprehensive field monitoring, identifying large infestations and areas requiring immediate attention. The collected data on weed populations and health metrics is stored and analyzed to improve future weed management strategies, enabling proactive and data-driven agricultural practices.
This automated system significantly reduces herbicide reliance, enhances crop yields, and promotes soil health by targeting only weed-infested areas. The integration of real-time data collection allows farmers to adapt weed control practices according to the infestation level and crop requirements, promoting a more efficient and environmentally friendly approach to precision agriculture.
, Claims:1. An automated weed control system for precision agriculture, integrating AI, machine vision, and autonomous machinery to detect and manage weeds effectively.
2. The system as claimed in Claim 1, wherein the machine vision technology identifies weeds based on size, shape, and color differences from crops, enabling precise targeting.
3. The system as claimed in Claim 1, wherein laser weeders are incorporated to eliminate weeds selectively, reducing the need for herbicides and supporting sustainable agriculture.
4. The system as claimed in Claim 1, wherein weed-detection robots autonomously navigate fields using GPS, identifying and targeting weeds without damaging crops.
5. The system as claimed in Claim 1, wherein drones are used for aerial monitoring, mapping weed infestations across large agricultural areas to support targeted intervention.
6. A method of automated weed management as claimed in Claim 1, involving the use of AI and machine vision to provide selective weed removal, enhance crop yield, and reduce environmental impact.
7. The system as claimed in Claim 1, wherein it supports data collection on weed distribution and crop health, enabling data-driven decision-making for future weed management.

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