Description:FIELD OF THE INVENTION
The present invention relates to an artificial intelligence (AI)-based disease detection and management system for leafy crops grown in greenhouses. Specifically, the invention integrates AI-driven image recognition, IoT-enabled environmental monitoring, and predictive analytics to detect, predict, and prevent diseases such as powdery mildew and bacterial leaf spots in controlled greenhouse environments.
BACKGROUND OF THE INVENTION
Leafy crop diseases in greenhouses cause significant yield losses, reduce crop quality, and increase dependency on pesticides. The controlled high-humidity greenhouse environment accelerates the spread of diseases like powdery mildew and bacterial leaf spots. Manual inspection methods are time-consuming, prone to error, and often lead to inefficient pesticide use. A real-time, data-driven AI system is needed to predict and manage disease outbreaks effectively.
1. Known Solutions:
Remote Sensing Systems: Satellite or drone-based disease monitoring systems.
IoT Sensor Networks: Environmental monitoring systems using sensors for humidity, temperature, and CO2.
Mobile Diagnostic Apps: Tools like Plantix for disease identification through images.
2. Limitations:
High cost, complexity, and dependency on external connectivity.
Inaccuracy in distinguishing overlapping symptoms.
Insufficient scalability for small greenhouses.
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.
The present invention provides an AI-based system for real-time detection and management of leafy crop diseases in greenhouse environments. The system integrates image recognition, IoT-enabled environmental monitoring, and predictive analytics to identify early disease symptoms and regulate greenhouse conditions accordingly.
The AI-driven image recognition module continuously captures images of leafy crops using high-resolution cameras. These images are processed using convolutional neural networks (CNNs) to detect disease symptoms, distinguishing between overlapping and visually similar symptoms for accurate classification. Simultaneously, IoT-based environmental sensors collect real-time data on temperature, humidity, CO2 levels, and other relevant parameters. The collected data is analyzed using predictive modeling techniques to forecast potential disease outbreaks based on environmental trends.
Upon detecting disease symptoms or identifying risk factors through predictive modeling, the system triggers automated greenhouse controls. These controls regulate temperature and humidity levels to create an unfavorable environment for disease proliferation. A mobile application provides real-time alerts, disease diagnosis, treatment recommendations, and remote control functionalities, enabling farmers to take immediate action and optimize their pesticide application.
By integrating AI, IoT, and predictive analytics, the invention ensures a proactive approach to greenhouse disease management. This minimizes crop losses, optimizes pesticide use, and enhances sustainability in controlled agricultural environments.
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 proposed system uses AI-driven image recognition and IoT-based environmental monitoring to detect and predict diseases in leafy greenhouse crops. Key features include:
1. Disease detection using image recognition.
2. Predictive analytics for disease outbreaks using environmental data.
3. Automated greenhouse controls to regulate temperature and humidity.
4. Mobile app for diagnosis, treatment recommendations, and farmer interaction.
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 proposed system uses AI-driven image recognition and IoT-based environmental monitoring to detect and predict diseases in leafy greenhouse crops. Key features include:
1. Disease detection using image recognition.
2. Predictive analytics for disease outbreaks using environmental data.
3. Automated greenhouse controls to regulate temperature and humidity.
4. Mobile app for diagnosis, treatment recommendations, and farmer interaction.
The project introduces a novel approach by integrating AI-based image recognition with predictive modeling to revolutionize greenhouse management. It automates real-time controls to prevent diseases by leveraging continuous learning models that enhance accuracy over time. This innovation ensures accessibility for both small and large-scale farmers through user-friendly mobile platforms, democratizing advanced agricultural technology for broader adoption.
The proposed solution offers several key advantages over existing methods. It provides more accurate and real-time disease detection, enabling growers to identify and address issues at an earlier stage. This proactive approach to disease management reduces reliance on reactive measures, minimizing crop losses and ensuring healthier yields. Additionally, it supports cost-efficient and targeted pesticide application, reducing chemical usage and environmental impact. The solution is highly scalable and adaptable, making it suitable for various greenhouse conditions and crop types, ultimately enhancing efficiency and sustainability in modern agriculture.
The proposed AI-driven disease detection and management system comprises multiple interconnected components, including an image recognition module, environmental sensors, a predictive analytics engine, an automated greenhouse control system, and a mobile application.
The image recognition module consists of high-resolution cameras installed within the greenhouse, positioned strategically to capture images of the entire crop canopy. These images are continuously processed using AI-driven algorithms based on convolutional neural networks (CNNs) trained on a dataset of diseased and healthy crop images. The AI model detects anomalies such as discoloration, lesion patterns, and fungal growth indicative of powdery mildew, bacterial leaf spots, and other greenhouse diseases. The classification results are then stored in a database and used for trend analysis.
Simultaneously, an IoT-based environmental monitoring system collects real-time data using an array of sensors measuring temperature, humidity, CO2 levels, and light intensity. These sensors wirelessly transmit data to a cloud-based server, where it is analyzed in conjunction with image recognition results. The predictive analytics engine processes historical and real-time environmental data to identify conditions conducive to disease outbreaks. Machine learning algorithms continuously refine predictive models by correlating past disease occurrences with environmental conditions, enhancing forecasting accuracy over time.
Upon detection of disease symptoms or prediction of an outbreak, the system interfaces with the automated greenhouse control unit. This unit consists of actuators controlling ventilation, humidifiers, heaters, and irrigation systems. Based on AI-driven recommendations, the control unit adjusts greenhouse conditions dynamically, reducing humidity and increasing airflow when high moisture levels are detected, thereby mitigating disease spread.
A mobile application serves as the user interface for farmers and greenhouse operators. The app provides real-time notifications on detected diseases, environmental anomalies, and recommended corrective actions. Farmers can view detailed disease reports, access historical data, and remotely adjust greenhouse parameters. The app also suggests targeted pesticide application based on disease severity, optimizing resource use and minimizing chemical exposure.
The system is designed for scalability, allowing its deployment across small-scale greenhouses and large commercial farms. The modular nature of the system ensures compatibility with existing greenhouse infrastructure, enabling easy integration with other smart farming technologies.
 
, Claims:1. An AI-based system for disease detection and management in leafy crop greenhouses, comprising an image recognition module, IoT-based environmental sensors, a predictive analytics engine, an automated greenhouse control system, and a mobile application.
2. The system as claimed in claim 1, wherein the image recognition module employs convolutional neural networks (CNNs) to detect disease symptoms based on crop images captured by high-resolution cameras.
3. The system as claimed in claim 2, wherein the CNN-based model distinguishes between visually similar disease symptoms to improve accuracy in disease classification.
4. The system as claimed in claim 1, wherein the IoT-based environmental sensors collect real-time data on temperature, humidity, CO2 levels, and light intensity to identify disease-prone conditions.
5. The system as claimed in claim 1, wherein the predictive analytics engine utilizes machine learning algorithms to forecast potential disease outbreaks based on historical and real-time environmental data.
6. The system as claimed in claim 1, wherein the automated greenhouse control system dynamically adjusts ventilation, humidity levels, and irrigation to mitigate disease spread based on AI-driven recommendations.
7. The system as claimed in claim 1, wherein the mobile application provides real-time alerts, disease diagnosis, treatment recommendations, and remote-control functionalities for greenhouse operators.
8. The system as claimed in claim 7, wherein the mobile application enables farmers to track historical disease trends and optimize pesticide application based on AI-based risk assessments.
9. The system as claimed in claim 1, wherein the integration of AI-based image recognition and IoT sensor data enhances precision disease management in greenhouse environments.
10. The system as claimed in claim 1, wherein continuous learning models refine disease detection and predictive accuracy over time, improving proactive disease management strategies.