Description:FIELD OF THE INVENTION
This invention relates to Solar-Powered Field Cameras for Crop Disease Detection
BACKGROUND OF THE INVENTION
Plant disease affects agricultural yield drastically, and hence, result in loss and economic uncertainty among farmers. Classical disease detection utilizes field observation based on manual screening, which involves a lot of time and scope for human mistakes. Delayed detection results in the quick advancement of infections and subsequently increased pesticides consumption and more expenses. Current automation systems necessitate constant supply of power or satellite imaging that is costly and not always readily available to small-scale farmers. An economical, autonomous solution is needed to continuously monitor crops and detect diseases at early stages to facilitate timely intervention.
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.
In order to overcome the issues of timely and effective detection of crop diseases, this solution incorporates the creation of an AI-based, energy-efficient, and low-cost system for continuous monitoring of crops for early disease manifestations. The suggested system includes solar-powered field cameras positioned strategically in agricultural fields, permitting real-time image acquisition and processing.
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.
In order to overcome the issues of timely and effective detection of crop diseases, this solution incorporates the creation of an AI-based, energy-efficient, and low-cost system for continuous monitoring of crops for early disease manifestations. The suggested system includes solar-powered field cameras positioned strategically in agricultural fields, permitting real-time image acquisition and processing.
Key Features and Components:
Solar-Powered Operation:
Every camera unit has a solar panel and a rechargeable battery, which provides round-the-clock working without the need for external power sources.This provides energy efficiency and makes the system work in even remote agriculture areas where there is no electricity.
Strategic Installation of Cameras:
Cameras will be strategically placed to provide a broad field of view, maximizing crop area coverage with least hardware.AI-driven vision algorithms will augment image processing by automatically detecting disease symptoms, minimizing the requirement for manual checks.
On-Device AI Processing:
Rather than depending on internet connectivity for cloud processing, these cameras perform image processing locally with embedded AI models (e.g., deep learning-based disease detection).
This minimizes latency, reduces data transmission costs, and provides real-time disease alerts, even in regions with poor network connectivity.
Automated Disease Detection and Alerts:
The cameras use computer vision models that have been trained on big datasets of infected and healthy crops. When symptoms of disease are identified, automatic alerts (through mobile apps, SMS, or local control systems) inform farmers and agricultural officials, allowing rapid intervention.
Scalability and Affordability:
The system is made cost-effective, employing low-cost camera modules, solar panels, and AI chips.It is easily deployable at scale, and hence affordable for small-scale farmers too, as opposed to large agricultural businesses.
Uniqueness of the Solution:
• Off-Grid and Continuous Monitoring: This system does not require external power and internet connectivity like conventional disease detection techniques.
• AI-Driven Image Processing on Edge Devices: Does away with cloud reliance, providing real-time insights with low infrastructure demands.
• Low-Cost and Self-Sustaining for Remote Regions: Low-cost and self-sustaining, ideal for application in areas where traditional monitoring is not feasible.
Major Innovative Features:
• AI-Driven Disease Identification
In contrast to conventional disease identification techniques based on manual examination, this system uses AI-powered computer vision to read crop images and identify early stages of disease in real-time.The AI system is trained across varied datasets of healthy and sick crops, so it can deliver high accuracy and low false positives.
• Solar-Powered, Energy-Efficient Operation
Each of the camera units comes with solar panels and rechargeable batteries, facilitating continuous monitoring independent of an external power supply.This makes the system autonomous and perfect for deployment in off-grid and remote farm locations.
• On-Device AI Processing for Real-Time Insights
Rather than depending on internet connectivity for cloud computing, the system makes use of embedded AI models for local processing of images.This method keeps latency to a minimum, minimizes data costs for transmission, and provides instant notifications to farmers even in low or no-internet connectivity regions.
• Automated Farmer & Disease Alert Notifications
Once the system detects possible symptoms of disease, automated alerts are triggered through mobile applications, SMS, or local monitoring systems.This facilitates immediate action, which helps farmers initiate prompt action before crops are lost and disease propagation is minimized.
• Cost-Effective & Scalable Deployment
The system is developed using cost-effective hardware components, including low-cost cameras, solar panels, and AI processing units, which make it economically viable for small farmers and large farmers.The modular structure makes scalability easy, with farmers being able to scale monitoring coverage depending on the size of the field and budget.
NOVELTY:
The AI-Based Solar-Powered Crop Disease Detection System presents a revolutionary method of farm disease monitoring through the combination of solar power, AI-based image processing, and real-time automated alerts. The system provides off-grid continuous monitoring, making disease detection effective, affordable, and accessible even in far-off farming areas.
, Claims:1. A solar-powered field camera system for crop disease detection comprising:
a) at least one camera unit equipped with a solar panel and a rechargeable battery for off-grid and continuous power supply;
b) an image acquisition module configured to capture crop images from a wide field of view through strategic placement;
c) an on-device artificial intelligence (AI) processor embedded in the camera unit and configured to locally process the captured images using pretrained deep learning models for disease detection; and
d) an automated alert module configured to generate real-time notifications through mobile applications, SMS, or local systems upon detection of disease symptoms in the crop images,
e) wherein the system operates without the requirement of external electricity or continuous internet connectivity.
2. The system as claimed in claim 1, wherein the on-device AI processor is configured to process image data in real time and minimize latency and data transmission costs by eliminating the need for cloud-based computing.
3. The system as claimed in claim 1, wherein the AI processor is trained on a dataset comprising images of both healthy and diseased crops to enable high-accuracy disease identification with reduced false positives.
4. The system as claimed in claim 1, wherein the camera units are low-cost and modular, allowing scalable deployment across different farm sizes and enabling affordable use by small-scale farmers in remote locations.
5. The system as claimed in claim 1, wherein the alert module is configured to communicate disease alerts to farmers and agricultural authorities through at least one communication channel selected from: a mobile application, a short messaging service (SMS), or a local monitoring control unit.