A WEB-BASED AI-POWERED FRAMEWORK FOR ACCURATE AND RAPID IDENTIFICATION OF FRUIT DISEASES

Abstract

This invention provides a web-based AI-powered framework designed to detect fruit plant diseases using Convolutional Neural Networks (CNN) for image-based analysis. Integrated with a Django-based web platform, the system allows users to upload images, receive disease classifications, and access treatment recommendations. The framework supports sustainable agriculture by enabling early disease detection and informed crop management practices, promoting food security and economic stability.

Specification

Description:FIELD OF THE INVENTION
This invention relates to agricultural technology and artificial intelligence, specifically focusing on a web-based AI-powered system designed for accurate and efficient detection of fruit plant diseases. This technology provides an accessible and rapid solution for farmers and agricultural professionals, leveraging machine learning and image recognition to improve crop health management and reduce crop losses.
BACKGROUND OF THE INVENTION
Fruit farming is highly susceptible to various diseases, which, if not addressed promptly, can result in significant crop losses, affecting both yield and quality. Traditional methods of disease detection, which rely on manual inspection and experience-based diagnosis, are time-consuming, error-prone, and often inaccessible to small-scale farmers. Incorrect diagnosis can lead to ineffective treatment, exacerbating crop damage. As a solution to these challenges, this invention offers an AI-powered framework that can detect and classify fruit diseases efficiently and accurately. The framework applies machine learning techniques, particularly Convolutional Neural Networks (CNNs), for disease recognition. Designed to integrate with a user-friendly web platform, the system allows users to upload images of diseased fruit plants and receive a rapid diagnosis with treatment recommendations. By supporting early detection and intervention, this system promotes sustainable agriculture, food security, and economic stability for farmers, addressing critical gaps in traditional crop disease 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 invention provides a web-based AI-powered framework that facilitates the identification of diseases in fruits such as pomegranate, papaya, dragon fruit, tomato, and orange. The system leverages CNN for image-based disease detection, supported by data pre-processing and classification algorithms for accurate diagnosis. Integrated with the Django framework, the web application allows farmers and agricultural professionals to upload images and receive real-time results. The invention not only aids in disease identification but also provides information on disease management and treatment, empowering users to make informed decisions. This framework enhances crop management efficiency, reduces crop losses, and aligns with sustainable agricultural practices.
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 ARCHITECTURE OF THE AI-POWERED FRAMEWORK, SHOWING MODULES FOR DATA PREPROCESSING, CNN-BASED CLASSIFICATION, AND DISEASE IDENTIFICATION.
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 web-based AI-powered framework for fruit disease detection comprises several key components: a Convolutional Neural Network (CNN) for image-based analysis, a Django-powered web interface, and a recommendation system for treatment guidance. The CNN model is designed with layers of convolution, pooling, and dense connections to capture image features associated with various fruit diseases. It is trained on a large dataset of fruit images, labeled according to disease type, enabling the model to distinguish between healthy and diseased plants and classify specific ailments such as fungal infections, viral diseases, or bacterial contamination.
Upon accessing the web platform, users are prompted to log in or register, providing a secure interface for data submission. Once logged in, users can upload images of their crops, which are then pre-processed to enhance clarity and optimize the image for model analysis. The pre-processing includes resizing, normalization, and augmentation techniques to ensure consistency and accuracy in disease classification.
The CNN model processes the uploaded image, identifying visual patterns that correspond to specific fruit diseases. Upon completing the analysis, the system presents a classification result, including the name and type of disease detected. If no disease is detected, the system informs the user, minimizing unnecessary treatment interventions. If a disease is identified, the system offers tailored recommendations for treatment, detailing chemical, biological, or cultural practices that can help manage or eradicate the disease.
This framework also integrates a recommendation system, which provides users with actionable insights based on the detected disease. This feature supports farmers in implementing appropriate treatment measures, promoting sustainable crop management, and reducing dependency on chemical pesticides. Additionally, the system's database is continuously updated to incorporate new diseases and treatment methods, ensuring it remains relevant for diverse agricultural needs.
, Claims:1. A web-based AI-powered framework for identifying diseases in fruit plants, comprising an image analysis system using Convolutional Neural Networks (CNN) for accurate disease detection and classification.
2. The framework as claimed in Claim 1, wherein the CNN model is integrated with a web application developed using the Django framework, enabling users to upload images and receive real-time disease diagnosis and treatment recommendations.
3. The framework as claimed in Claim 1, wherein data preprocessing techniques are applied to uploaded images, including resizing, normalization, and augmentation, ensuring accurate and consistent disease classification.
4. The framework as claimed in Claim 1, wherein a recommendation system provides actionable guidance on treatment based on the identified disease, supporting sustainable crop management.
5. A method for fruit plant disease detection as claimed in Claim 1, involving the application of image recognition and machine learning to identify diseases and provide treatment recommendations through a web interface.
6. The framework as claimed in Claim 1, wherein it supports multiple fruit types, including pomegranate, papaya, dragon fruit, tomato, and orange, offering broad applicability across agricultural needs.

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