Description:FIELD OF THE INVENTION
This invention relates to AI-Powered Pest Detection with Autonomous Drones
BACKGROUND OF THE INVENTION
Pest infestations are responsible for up to 40% of crop losses worldwide every year and are a serious threat to food security, as well as causing a huge financial loss for farmers. Conventional approaches to pest control, including manual scouting and blanket spraying of broad-spectrum insecticides, are time-consuming, expensive, and environmentally toxic.
Scouting can be error-prone, labor-intensive, and fail to detect early stages of infestations. Fixed cameras provide poor coverage and are disadvantaged by big or oddly shaped fields, while satellite imagery is not real-time responsive and subject to weather effects. Existing pesticide spraying systems based on drones also fail to use AI-based detection, resulting in wasteful chemical use and more environmental pollution.
With the absence of an automated, real-time precision-based pest detection mechanism, farmers are not able to control infestations to their liking. As a result, they tend to use too many pesticides, experience lower yields on their crops, and lose money. An urgent need is for an intelligent, autonomous early pest detection, detection of infested areas, and pesticide application system where the latter is actually necessary. This would introduce higher efficiency and increase agricultural sustainability
Pest Capture and storage device and Pest insecticide device (JP6274430B2), Pest abatement utilizing an aerial drone (US20170231213A1), Pest management system (CN117545352A)
The presently available solutions are shortfall in terms of:
 Accuracy in Pest Detection: Current systems tend to misdiagnose crop diseases as pest infestations, causing inaccurate identifications.
 Real-time monitoring: Most solutions do not have real-time detection, and therefore, they take time to identify and act upon infestations.
 Targeted intervention: Drones and pesticide sprayers usually take predefined routes, spraying chemicals randomly instead of targeting infected areas.
 Data Processing & insights: Existing technologies do not offer predictive analytics, which would be able to predict future infestations from past trends in data.
 Automation: The majority of existing solutions are manually operated, and therefore large-scale pest monitoring is time-consuming and inefficient.
 Environmental impact: Pesticide overuse, due to inaccuracy, causes water pollution and increased pesticide resistance in pests as well as soil erosion.
 These limitations underscore the need to create an AI-based autonomous drone system that will deliver real-time, precise pest detection and targeted pesticide application for more effective and sustainable crop management.
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 block diagram for proposed AI-Powered Pest Detection with Autonomous Drones innovation illustrated in Fig. 1. The system consists of Drone, AI-based Pest Detection, continuous monitoring system etc.
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 block diagram for proposed AI-Powered Pest Detection with Autonomous Drones innovation illustrated in Fig. 1. The system consists of Drone, AI-based Pest Detection, continuous monitoring system etc.
The AI driven pest detection system uses drones, artificial intelligence (AI), and data analysis to Mechanize agro-pest monitoring and management. The system starts by dispatching autonomous Dornes with high-definition cameras, multispectral sensors, and thermal cameras to survey Agricultural farms. The drones record real-time videos and images, which catch useful information To identify the early signs of pest infestation. The gathered data is analyzed by an AI-powered Pest detection and identification system that employ algorithms of computer vision and deep learning to discern the images and detect pests. discriminates the pest insects from the beneficial insects depending on characteristics, so only the harmful pests are targeted. Classification increases detection and minimizes chances of unnecessary use of pesticides.
After the pest identification, the system shifts towards decision making and selective intervention.
Depending on severity and nature of the infestation, AI system suggests treatment protocols, whether it is mere monitoring in weak cases or treatment by pesticide usage for severe infestation. AI correctly determines points for treatment where only the target sites receive the application of the pesticides, diminishing the chemical expenditure. In precision spraying and green action stage, intelligence spraying drones or self-guided sprayer are utilized to spray pesticides with great precision. It decreases chemical waste, conserves beneficial insects and prevents overuse of pesticides; thus, it is a more environmentally friendly alternative.
Predictive analytics are further utilized to scan historic pest data, weather data, and environmental data and project future infestation. Based on historical infestations, the AI can provide optimal planting time recommendations and best spraying times of pesticides, helping farmers avoid future pest issues. Moreover, it creates real-time reports and suggestions, giving farmers full information on the activity of the pests, level of infestation, and what action should be taken. Such reports are accessed through mobile apps or web-based dashboards, allowing farmers to make quick and informed decisions.
Last but not least, the system ensures ongoing improvement and monitoring as AI model and drones update their databases with newer information continuously. This enables the AI to continually sharpen its ability to detect pests with time, hence making the system more precise and effective. By constantly monitoring fields, the system ensures ongoing long-term pest control, thus promoting crop yield and minimizing loss. This approach optimized through AI ensures better agriculture practice, reduced cost, minimum environmental footprint, and sustainable pest control for the future.
NOVELTY:
This is a statement of what is new, and not a business case.
The innovation of the proposed AI-driven pest detection system harmonizes autonomous drones, deep learning-driven pest identification, precision-targeted pesticide application, and predictive analytics to provide real-time, automated, and sustainable pest control. The innovative solution reduces chemical usage and optimizes long-term agricultural productivity
ADVANTAGES OF THE INVENTION
Real-time and automatic monitoring: In contrast to traditional pest management systems that involve manual scouting, this system utilizes autonomous drones that scout out fields and detect infestation in real time, thus conserving labor while gaining efficiency.
AI-Based Accurate Pest Identification: Conventional approaches often involve blanket area pesticide spraying, leading to the unnecessary use of chemicals. The system employs deep learning models to accurately identify specific pests, whereby only pest that are harmful are targeted while leaving useful organisms unharmed.
Precision Pesticide Application: Rather than adopting widespread spraying, which increases
environmental and health risks, this system offers precision spraying by AI-Controlled drones,
minimizing wastage of pesticides and reducing soil and water contamination
Predictive analytics for display: Unlike existing solutions that only react to infestations once they have spread, system utilizes historical pest information, climate, and conditions
Information to forecast probable outbreaks, allowing for preemptive action.
Continuous Learning and Flexibility: Existing methods are static in nature, whereas this system
constantly enhances detection accuracy by learning from new data, becoming more efficient and flexible over time.
Sustainability and Eco-Friendliness: In contrast to conventional, pesticide- based pest control,
this solution reduces the use of pesticides, promoting soil health, pollinator conservation, and
sustainable agriculture.
Data-Driven Decision Making: Farmers are provided with real-time reports and actionable
information via mobile Apps or dashboards, allowing data-driven decision-making instead of
guesswork or sporadic inspections.
Long-Term Cost-Effectiveness: Although investment in drones and AI will be higher compared to conventional manual pest control systems in the beginning, lower usage of pesticides, lower labor expense, nd higher yields balance this out in the long term.
, Claims:1. An AI driven pest detection system, comprising: uses drones, artificial intelligence (AI), data analysis, high-definition cameras, multispectral sensors, and thermal cameras.
2. The system as claimed in claim 1, wherein the system generates real-time reports and treatment suggestions accessible via a mobile application or web-based dashboard.
3. The system as claimed in claim 1, wherein all modules are integrated to function as a continuous feedback loop, enabling real-time updates, learning, and adaptive pest control strategies.
4. The system as claimed in claim 1, wherein the drones operate autonomously based on GPS coordinates, predefined flight paths, and adaptive routing based on detected threats.
5. The system as claimed in claim 1, wherein the decision-making module determines a course of action from a set comprising: no action, continued monitoring, or targeted pesticide application.
6. The system as claimed in claim 1, wherein the artificial intelligence engine identifies pests by analyzing patterns in color, shape, movement, and thermal signatures.
7. The system as claimed in claim 1, wherein the predictive analytics module provides planting time and pesticide application recommendations based on predictive infestation patterns.