Description:FIELD OF THE INVENTION
This invention relates to Development of a Self-Adaptive AI Model for Computerized Recognition and Identification of Paddy Diseases
BACKGROUND OF THE INVENTION
A huge segment of the global population-base relies on Paddy as a primary nutritive source, but its farming is particularly susceptible to numerous infections that can greatly diminish both the harvest and standard of the harvest. Common infection identifying approaches depend greatly on manual inspection, which consumes a lot of timely and work, and is susceptible to human-based mistakes, and often leads to delayed diagnosis and treatment. While existing AI-based systems offer automated solutions, many of them lack adaptability to varying environmental conditions, new disease variants, and unseen data,
Limiting their real-world applicability.
Thus, there is a critical need to develop a Self-Adaptive AI Model that can automatically detect and diagnose Paddy diseases with high accuracy, continuously learn from new data, adapt to dynamic agricultural environments, and assist farmers and agronomists in timely decision-making to enhance crop health and productivity.
EXISTING SOLUTIONS / PRIOR ART/RELATED APPLICATIONS & PATENTS:
Known Products and Present Commercial Practice:
Several mobile applications and platforms such as Plantix, Agremo, and PlantVillage Nuru offer AI-based plant disease identification, including Paddy diseases. These systems primarily rely on image classification using pre-trained models and cloud-based diagnostics. Drone-based monitoring systems integrated with computer vision are also being used in commercial farming for large-scale disease detection. However, these products typically lack self-adaptive learning capabilities, often require consistent internet access, and are not tailored specifically for regional variations in Paddy crop diseases, limiting their accuracy and reliability in diverse farming contexts.
Presently presented explanations for Paddy disease detection rely heavily on static image-based or rule-based systems, which lack adaptability across varying conditions and crop varieties. They often fail in early-stage detection, have limited scalability for real-world deployment, and require controlled environments. Additionally, these models offer poor interpretability, reducing their practical utility for farmers. Most importantly, they do not integrate real-time or IoT data, making them less effective for dynamic, field-level diagnosis.
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 proposed invention is the development of a Self-Adaptive AI algorithm created for the automatic identification and diagnosis of Paddy infections. Unlike traditional static models, the system will incorporate self-learning and adaptive mechanisms to continuously update its knowledge base with new disease patterns, environmental variations, and unseen data.
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 invention is the development of a Self-Adaptive AI algorithm created for the automatic identification and diagnosis of Paddy infections. Unlike traditional static models, the system will incorporate self-learning and adaptive mechanisms to continuously update its knowledge base with new disease patterns, environmental variations, and unseen data.
The core of the invention will integrate advanced deep learning methods like Convolutional Neural Networks (CNNs) and Transformer-based architectures to extract fine-grained features from Paddy plant images. A meta-learning framework will be incorporated to enable the model to adapt quickly to new disease types with minimal additional training.
Furthermore, the system will utilize active learning strategies, where it can interactively query a small amount of expert-verified data to improve its performance on uncertain predictions. To enhance real-time usability, the model will be enhanced for deployment on smart phones and control strategies, allowing farmers to capture leaf images in the field and instantly receive disease diagnosis and treatment suggestions.
Key features of the proposed invention include:
• Self-adaptive learning to handle new and evolving Paddy diseases.
• High accuracy and robustness across different Paddy varieties and growth stages.
• Low computational cost for practical use on smartphones and edge devices.
• Interpretability module to provide users with visual explanations of the diagnosis results.
• Cloud integration for model updates and data aggregation from multiple geographical locations.
This invention aims to empower farmers with an intelligent, easy-to-use, and adaptive tool, ensuring timely intervention to protect Paddy crops, decrease losses, and contribute to workable farmed performs.
NOVELTY:
The proposed system is a Self-Adaptive AI-Based Paddy Disease Detection and Diagnosis Framework, which uniquely integrates meta-learning and active learning techniques to enable real-time, autonomous adaptation to new disease patterns with minimal human intervention, ensuring higher accuracy and resilience across diverse field conditions.
ADVANTAGES OF THE INVENTION
• The proposed Self-Adaptive AI Model for Paddy Disease Detection dynamically learns and adapts from new, unseen data in real-time, unlike existing static models that require frequent manual retraining.
• It leverages meta-learning and active learning techniques to autonomously fine-tune its performance, ensuring higher diagnostic accuracy across evolving disease strains and varying environmental conditions.
• The system significantly reduces the need for large labeled datasets by actively identifying and learning from critical, uncertain cases, minimizing expert intervention.
• Unlike conventional AI models that degrade over time with environmental shifts, the proposed model maintains robust performance by continuously updating itself with minimal computational resources.
• It empowers agricultural stakeholders with an intelligent, resilient, and scalable disease management tool, enhancing the sustainability and productivity of Paddy farming ecosystems—an advancement over rigid, non-adaptive traditional systems.

 
, Claims:1. An artificial intelligence-based system for computerized recognition and identification of paddy diseases, comprising:
a deep learning module employing Convolutional Neural Networks (CNNs) and Transformer-based architectures configured to extract fine-grained visual features from paddy leaf images;
a self-adaptive learning module configured to automatically update the disease recognition model by incorporating new disease patterns, environmental data, and previously unseen inputs;
wherein the system is operable to identify and diagnose multiple paddy diseases with high accuracy across different paddy varieties and growth stages.
2. The system as claimed in claim 1, wherein a meta-learning framework is integrated to enable rapid adaptation to newly emerging or previously unencountered paddy diseases using a limited set of additional training data.
3. The system as claimed in claim 1, wherein an active learning module is configured to selectively query expert-labeled data in response to uncertain predictions, thereby improving model performance with minimal manual intervention.
4. The system as claimed in claim 1, wherein the AI model is optimized for deployment on mobile devices, allowing real-time disease diagnosis through image capture by farmers using smartphones, along with automatic treatment suggestions.